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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Gewaly, Mohamed Mahmoud Saeed, Mahmoud Samy Ammar, Ammar Mohammed FGSSR Cairo University Dept. of Data Science Cairo Egypt Telecom Egypt Cairo Egypt FGSSR Cairo University Dep. of Computer Science Cairo Egypt

ISBN: (纸本)9798350362633

The quality of service of the individual user of telecommunication services is one of the most critical criteria in the telecom industry that affects customer satisfaction. This paper aims to predict customer complaints before escalation and validate current escalated complaints in fixed broadband from telecommunication companies via historical and real-time customer data, including service logs and previous complaints, to optimize resource allocation and maximize customer satisfaction as the challenges are related to the volume of physical complains and churn. The dataset is real-world data provided by a single Egyptian mobile network operator and consists of 12 features pertaining to 100,000 customers. We represent 12 predictive models developed using machine learning algorithms via advanced techniques such as classification algorithms like Random Forest Classifier (RFC), AdaBoost Classifier with a Decision Tree (DT), Logistic Regression (LR), -Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Decision Tree Classifier (DTC), Support Vector Machine (SVM) to analyze various features and patterns within the data. The results show that the AdaBoost with a Decision Tree model did well using a variety of assessment indicators, including Accuracy, Precision, Recall, and F1-score;the model aims to classify customers, indicating their likelihood of raising complaints in the future. The results and insights obtained from this study can provide telecom service providers with valuable guidance for implementing data-driven strategies to improve customer experience and take proactive maintenance that will reduce cost and churn. © 2024 IEEE.

关键词： Customer satisfaction

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Diffusion Models for Automatic Music Mixing

Diffusion Models for Automatic Music Mixing

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Wu, Xinyang Horner, Andrew Hong Kong University of Science and Technology Dept. Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350362480

Music mixing is a process that involves fine-tuning the levels, dynamics, and frequency content of musical elements to ensure clarity and harmony in the final music production. In this paper, we present an automatic mixing system based on diffusion models to correct imbalances in music mixes. We manipulate the well mixed stems' short-time Fourier transform randomly to simulate the frequency and level imbalances commonly encountered in real-world scenarios. The difference between the imbalanced mix and the original mix is treated as noise for the diffusion model to predict, enabling the reverse denoising process to generate an automated mix. We evaluate our model's performance by calculating the signal-to-distortion ratio between the original and predicted mixes. These results are compared with baseline automatic mixing models, demonstrating significant improvements. Test set results in audio: https://***/diffmusicmix/ © 2024 IEEE.

关键词： Signal denoising

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Sparse Oblique Decision Trees: A Tool to Understand and Manipulate Neural Net Features

Sparse Oblique Decision Trees: A Tool to Understand and Mani...

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KDD Workshop on Human-Interpretable AI 2024, HI-AI 2024

作者： Hada, Suryabhan Singh Carreira-Perpiñán, Miguel Á. Zharmagambetov, Arman LinkedIn Dept. of Computer Science & Engineering University of California Merced United States Dept. of Computer Science & Engineering University of California Merced United States

The widespread deployment of deep nets in practical applications has lead to a growing desire to understand how and why such black-box methods perform prediction. Much work has focused on understanding what part of the input pattern (an image, say) is responsible for a particular class being predicted, and how the input may be manipulated to predict a different class. We focus instead on understanding what internal features computed by the neural net are responsible for a particular class. We achieve this by mimicking part of the net with a decision tree having sparse weight vectors at the nodes. We are able to learn trees that are both highly accurate and interpretable, so they can provide insights into the deep net black box. Further, we show we can easily manipulate the neural net features in order to make the net predict, or not predict, a given class, thus showing that it is possible to carry out adversarial attacks at the level of the features. We demonstrate this robustly in MNIST and ImageNet with LeNet5 and VGG networks. © 2024 Copyright for this paper by its authors.

关键词： Deep neural networks

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Earthquake Prediction Through Machine Learning Approach 4

Earthquake Prediction Through Machine Learning Approach

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Shrote, Pratik Dasarwar, Priya Dongre, Snehlata Khobragade, Prashant Intern Center of Excellence Data Science Gh Raisoni College of Engineering Nagpur India Nagpur Campus Pune India Gh Raisoni College of Engineering Dept. of Computer Science and Engineering Nagpur India H Raisoni College of Engineering Dept. of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350387490

The population of earthquake-prone areas is growing, as is the number of people killed and injured by earthquakes. The problem of the increasing number of populated regions that are vulnerable to earthquakes is being addressed by a shortage of experts in earthquake engineering. This implies the requirement for automated intelligence-monitored mitigation and warning systems. Predicting earthquakes is still a major problem for disaster management around the globe. The objective of this work is to build a machine learning (ML) approach that uses geographic coordinates (latitude, longitude) and elevation (height) as inputs to forecast earthquake magnitude. The estimated magnitude is used to classify the seismic risk level for a given site into high, low, or no risk categories using Richter's scale. The study offers a thorough analysis of the methodology, covering feature engineering, data collecting, preprocessing, and model construction. A variety of machine learning methods are investigated, and relevant measures are used to assess their effectiveness. © 2024 IEEE.

关键词： Adversarial machine learning

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Development of a Deep Learning-Based Model for Canine Skin Disease Diagnosis Using RandAugment and Patterned-GridMask

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Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 142-148页

作者： Kim, Min-Jun Park, Jae-Beom Cho, Hyun-Chong Dept. of Electronics Engineering Interdisciplinary Graduate Program for BIT Medical Convergence Dept. of Data Science Kangwon National University Korea Republic of Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

In contemporary society, pets are increasingly regarded as integral family members, contributing significantly to human quality of life. The growing prevalence of dog ownership has concurrently escalated the economic burden associated with veterinary care, particularly in managing common conditions like skin diseases. This study introduces an advanced deep learning-based diagnostic system for canine skin diseases, designed for practical application in home environments. We employed RandAugment to enhance data augmentation, thereby increasing the diversity of the training dataset. Furthermore, the implementation of Patterned-GridMask significantly improved the model's generalization capabilities. The use of the AdamW optimization algorithm was instrumental in mitigating overfitting, thus enhancing the model's overall learning efficiency. The proposed Transformer-based model, ViT/B-16, achieved an accuracy of 78.65% with the original dataset. With the integration of RandAugment and Patterned-GridMask techniques, the model's accuracy improved to 84.33%, underscoring its potential effectiveness for practical veterinary applications. Copyright © The Korean Institute of Electrical Engineers.

关键词： Medical imaging

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Design and Development of Cognitive Training Systems Based on Extended Reality and BCI Technology 7

Design and Development of Cognitive Training Systems Based o...

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7th IEEE Eurasian Conference on Educational Innovation, ECEI 2024

作者： Lin, Pei-Jung Ku, Hsiu-Chien Lin, Li-Lun Feng Chia University Dept. of Information Engineering and Computer Science Taichung Taiwan Hungkuang University Dept. of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9798350307207

Dementia is considered a Mild Cognitive Impairment (MCI), representing a blurred transition from normal cognitive function to dementia. MCI might be a precursor to dementia being caused by various neurodegenerative diseases, including Alzheimer's disease, stroke, metabolic disorders, and mental health conditions. According to the World Health Organization (WHO), over 55 million people worldwide are suffering from dementia, with nearly 10 million new cases each year. This number is expected to rise to 78 million by 2030. Numerous studies have proposed various training methods targeting specific cognitive domains in dementia. For instance, reminiscence therapy encourages individuals to recall buried memories, while reality orientation therapy focuses on strengthening orientation cues to help patients with dementia establish a stronger sense of control. With aging, changes occur in cognitive and physical functions. Research suggests that memory functions can be improved through training. Therefore, the primary goal of this study was to develop an Extended Reality (XR) cognitive system to assist MCI patients in enhancing logical thinking, increasing independence in daily life, and improving response accuracy. The application of extended reality technology in the medical field is expanding, covering areas such as surgical assistance, medical consultation, disease treatment, and medical training. We integrated mixed reality devices with brain-computer interface (BCI) equipment. By detecting brainwave signals in the frontal region of the brain through sensors, neural activity was quantified, and digital brainwave data was transmitted to the system. This data was then visualized on screen in real-time to analyze changes in brainwave data during training. Through multifaceted task training, the system enhanced cognitive response and sensory perception, stimulating brain activity with the goal of strengthening cognitive functions. © 2024 IEEE.

关键词： Mixed reality

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Statistical Analysis of the Properties of Geometric Network with Node Mobility 29

Statistical Analysis of the Properties of Geometric Network ...

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29th IEEE Asia Pacific Conference on Communications, APCC 2024

作者： Arquam, Md. Tiwari, Utkarsh Kumari, Suchi Iiit Dept. Computer Science and Engineering Haryana Sonepat India Shiv Nadar Institute of Eminence Dept. Computer Science and Engineering Delhi-NCR India

ISBN: (纸本)9798331517816

The movement changes the underlying spatial representation of the participated mobile objects or nodes. In real-world scenarios, such mobile nodes can be part of any biological network, transportation network, social network, human interaction, etc. The change in the geometry leads to the change in various desirable properties of real-world networks, especially in human interaction networks. In real life, human movement is concerned with a better lifestyle where they form their new connections due to the geographical changes. Therefore, in this paper, we design a model for geometric networks with mobile nodes (GNMN) and conduct a comprehensive statistical analysis of their properties. We analyze the effect of node mobility by evaluating key network metrics such as connectivity, node degree distribution, second-hop neighbors, and centrality measures. Through extensive simulations, we observe significant variations in the behavior of geometric networks with mobile nodes. © 2024 IEEE.

关键词： Complex networks

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AI-Driven Dynamic Network Slicing for Resource Optimization in 5G Networks: Implementation and Performance Evaluation

AI-Driven Dynamic Network Slicing for Resource Optimization ...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Jain, Ayushi Jain, Prateek Verma, Aditya Gopal, Lisa Chaudhary, Preeti Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Scholar Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331542344

We introduce in this paper an AI-based dynamic network slicing model for resource allocation targeting 5G networks. It does this by using machine learning algorithms that help the model learn and adapt to live traffic conditions so as to intelligently manage different service types, including eMBB (enhanced Mobile Broadband), URLLC (Ultra-Reliable Low Latency Communication) and mMTC (massive Machine Type Communication). This leads to substantial improvements in three of the four core performance metrics, latency, throughput and energy efficiency (at a low sacrifice of when it comes to QoS adherence). Their work also demonstrates that AI technologies hold the promise for revolutionary network management, offering a scalable solution that can address ever-increasing requirements of next-generation telecommunications, by surpassing the capabilities of well-established static slicing techniques. © 2024 IEEE.

关键词： 5G mobile communication systems

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Generalizing Clinical De-identification Models by Privacy-safe data Augmentation using GPT-4

Generalizing Clinical De-identification Models by Privacy-sa...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Kim, Woojin Hahm, Sungeun Lee, Jaejin Korean National Police Agency Seoul Korea Republic of Dept. of Data Science Seoul National University Seoul Korea Republic of Dept. of Computer Science and Engineering Seoul National University Seoul Korea Republic of

ISBN: (纸本)9798891761643

De-identification (de-ID) refers to removing the association between a set of identifying data and the data subject. In clinical data management, the de-ID of Protected Health Information (PHI) is critical for patient confidentiality. However, state-of-the-art de-ID models show poor generalization on a new dataset. This is due to the difficulty of retaining training corpora. Additionally, labeling standards and the formats of patient records vary across different institutions. Our study addresses these issues by exploiting GPT-4 for data augmentation through one-shot and zero-shot prompts. Our approach effectively circumvents the problem of PHI leakage, ensuring privacy by redacting PHI before processing. To evaluate the effectiveness of our proposal, we conduct cross-dataset testing. The experimental result demonstrates significant improvements across three types of F1 scores. © 2024 Association for Computational Linguistics.

关键词： Phishing

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A Study on Optimization and Augmentation Techniques for Improving the Performance of CADx System for Cat Skin Disease

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Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 135-141页

作者： Won, Hyeong-Sik Park, Jae-Beom Cho, Hyun-Chong Dept. of Electronics Engineering Interdisciplinary Graduate Program for BIT Medical Convergence Dept. of Data Science Kangwon National University Korea Republic of Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

In modern society, the number of households raising pets is increasing. As pet ownership increases, the cost of treating companion cats is also rising, with a significant portion of these costs going toward the treatment of skin diseases. Skin diseases are among the common ailments in pets, and swift action is required if they occur in cats. However, early lesions lack distinctive characteristics, making accurate diagnosis difficult. Therefore, this study proposes a CADx(computer-Aided Diagnosis) system that classifies images of cat skin conditions into inflammatory lesions, non-inflammatory lesions, and normal images using a dataset of cat skin diseases. We selected the ConvNeXt model, based on CNN, which can learn regional information and features. To learn various patterns of skin lesions, we applied Mixup, an image augmentation technique. When Mixup was applied, the model accuracy was 0.8679, showing a 3.35% improvement compared to the original dataset. Additionally, Lookahead Optimizer was applied to ensure stable learning of Mixup. When both Mixup and Lookahead Optimizer were applied, the model accuracy was 0.8741, showing a high improvement of 3.97% compared to the original dataset. Therefore, the Lookahead Optimizer helps with the stable training of mixup, which can improve the performance of the model. Copyright © The Korean Institute of Electrical Engineers.

关键词： Dermatology

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