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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： MacHlev, R. Malka, A. Perl, M. Levron, Y. Belikov, J. Technion-Israel Institute of Technology The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Haifa3200003 Israel Tallinn University of Technology Department of Software Science Akadeemia tee 15a Tallinn12618 Estonia

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

Non-Intrusive Load Monitoring (NILM) techniques estimate the consumption of individual appliances in a household or facility, based on readings of a centralized meter. Lately, deep learning techniques have demonstrated outstanding performance for NILM predictions. Nevertheless, a possible problem is that users and consumers may find it hard to trust the results of such algorithms if they do not fully understand the reasons for their outputs. In this light, this work presents a method that explains and justifies the outputs of NILM convolutional neural network (CNN) classifiers, using Explainable Artificial Intelligence (XAI) techniques. The method operates as follows: a CNN model for NILM is used to estimate which appliances are activated in the system based on the total power consumption. Then, a XAI technique uses this model and its outputs to explain and justify the prediction of this model. Thereby, the NILM CNN classifier outputs are both accurate and are more interpretable, allowing users to make informed and trustworthy decisions. These ideas are demonstrated on the REDD dataset using a convolutional neural network classifier and two state-of-the-art XAI techniques. © 2022 IEEE.

关键词： Convolutional neural networks

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Monge-Ampere Regularization for Learning Arbitrary Shapes from Point Clouds

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年

作者： Yang, Chuanxiang Zhou, Yuanfeng Wei, Guangshun Ma, Long Hou, Junhui Liu, Yuan Wang, Wenping The School of Software Shandong University Jinan250100 China The Department of Computer Science City University of Hong Kong Hong Kong The College of Computing and Data Science Nanyang Technological University Singapore The Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States

As commonly used implicit geometry representations, the signed distance function (SDF) is limited to modeling watertight shapes, while the unsigned distance function (UDF) is capable of representing various surfaces. However, its inherent theoretical shortcoming, i.e., the non-differentiability at the zero-level set, would result in sub-optimal reconstruction quality. In this paper, we propose the scaled-squared distance function (S2DF), a novel implicit surface representation for modeling arbitrary surface types. S2DF does not distinguish between inside and outside regions while effectively addressing the non-differentiability issue of UDF at the zero-level set. We demonstrate that S2DF satisfies a second-order partial differential equation of Monge-Ampere-type, allowing us to develop a learning pipeline that leverages a novel Monge-Ampere regularization to directly learn S2DF from raw unoriented point clouds without supervision from ground-truth S2DF values. Extensive experiments across multiple datasets show that our method significantly outperforms state-of-the-art supervised approaches that require ground-truth surface information as supervision for training. The code will be publicly available at https://***/chuanxiang-yang/S2DF. © 2025 IEEE. All rights reserved.

关键词： Supervised learning

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A Patient Specific Algorithm for Plasmodium Malaria Detection on Cell Images

A Patient Specific Algorithm for Plasmodium Malaria Detectio...

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IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Barath Narayanan Narayanan Manawaduge Supun de Silva Russell C. Hardie Sensor and Software Systems Division University of Dayton Research Institute Dayton USA Department of Electrical and Computer Engineering University of Dayton Dayton USA

computer-aided detection of plasmodium malaria on cell images from digital microscopy provides an invaluable second opinion to medical experts. Traditionally, well-established deep learning algorithms are widely used to detect plasmodium, but these techniques have not been deployed due to the uninterpretable nature of the decisions made by the network. To address this, we present an ensemble method that fuses the predictions based on the class activation mapping (CAM) results from multiple networks via a novel selector network. We study the performance on a publicly available dataset of cell images by using three convolutional neural network architectures, Xception, ResNet50, and InceptionV3,to produce CAM results for the selector network and performance comparison. Our proposed approach helps enhance doctors' trust and outperforms the traditional methods by 1%.

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Correction: Harnessing Federated Generative Learning for Green and Sustainable Internet of Things

arXiv

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arXiv 2024年

作者： Qi, Yuanhang Shamim Hossain, M. School of Computer Science University of Electronic Science and Technology of China Zhongshan Institute Zhongshan528402 China Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh13272 Saudi Arabia

The rapid proliferation of devices in the Internet of Things (IoT) has ushered in a transformative era of data-driven connectivity across various domains. However, this exponential growth has raised pressing concerns about environmental sustainability and data privacy. In response to these challenges, this paper introduces One-shot Federated Learning (OSFL), an innovative paradigm that harmonizes sustainability and machine learning within IoT ecosystems. OSFL revolutionizes the traditional Federated Learning (FL) workflow by condensing multiple iterative communication rounds into a single operation, thus significantly reducing energy consumption, communication overhead, and latency. This breakthrough is coupled with the strategic integration of generative learning techniques, ensuring robust data privacy while promoting efficient knowledge sharing among IoT devices. By curtailing resource utilization, OSFL aligns seamlessly with the vision of green and sustainable IoT, effectively extending device lifespans and mitigating their environmental footprint. Our research underscores the transformative potential of OSFL, poised to reshape the landscape of IoT applications across domains such as energy-efficient smart cities and groundbreaking healthcare solutions. This contribution marks a pivotal step towards a more responsible, sustainable, and technologically advanced future. © 2024, CC0.

关键词： Internet of things

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Advancements in 5G and 6G Technologies Shaping the Future of Global Connectivity and Communication

Advancements in 5G and 6G Technologies Shaping the Future of...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Sunita Pachar Satti Sudha Mohan Reddy Gouthami Kompally Lakshmi Narasimhan Srinivasagopalan Rohidas Balu Sangore Sivakoteswararao Katta IBM Department GLA University Department of ECE SRKR Engineering College India Department of CSE-IOT Malla Reddy Egineering College(A) India Software Development Enterprise Architecture Plano TX USA Computer Science and Engineering MIT Art Design Technology University Department of Mechanical KIETW Kakinada

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

The evolution of wireless communication is poised for a major shift with the transition from 5G to 6G, a leap characterized by breakthroughs in speed, ultra-reliable low-latency communication, and AI-driven network optimization. This paper examines these advancements, focusing on sub-millisecond latency, terahertz spectrum utilization, and enhanced network capabilities tailored to industries such as smart infrastructure, healthcare, and autonomous vehicles. While 6G promises revolutionary improvements, it also presents new challenges, including increased computational complexity and energy demands, data privacy concerns, and efficient spectrum allocation. The proposed model aims to address these issues by integrating AI and machine learning for optimized resource management, resulting in a high-performance, cost-effective solution for real-time, data-intensive applications. To assess the model's effectiveness, comprehensive simulations and real-world trials will be conducted, measuring critical metrics such as network throughput, latency, reliability, and energy efficiency. This study offers an all-encompassing guide to understanding and harnessing the transformative potential of 6G technology, laying the groundwork for future wireless communication systems.

关键词： 6G mobile communication Ethics Technological innovation Computational modeling Medical services Energy efficiency Real-time systems Security Resource management Standards

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A Nudity Detection Algorithm for Web-based Online Networking Platform

A Nudity Detection Algorithm for Web-based Online Networking...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Ritu Dewan Rohith Polishetty Naveen Jagadam Kiran Kumar Ravulakollu Mayank Kumar Goyal Bhagwati Sharan Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India Sr. Software Engineer Migo India Limited Hyderabad India CEO N2force Technologies Inc. Naperville IL USA School of Technology Woxsen University Hyderabad India Department of Computer Science & Engineering School of Engineering & Technology Sharda University Greater Noida India Department of Computer Science and Engineering School of Engineering and Sciences SRM University Amaravati AP India

Social media and online platforms have become an integral part of the day-to-day life of the people and the organizations, which shows a lot of diversity. Many online platforms allow users to share media extensively, like images, videos, and audio but not all the media content shared on the internet is safe for work or should be restricted from particular platforms. Images with nudity are of big concern when it comes to online media sharing and social platforms must take serious measures to restrict users to share images with nudity. The rate of growth of nudity content over the internet or social media is very high. It affects the mental and physical health of individuals. In this paper, a novel method for nudity detection in images is presented. We aim to develop a Machine Learning algorithm for nudity detection in a community networking platform and how it should be integrated with such a platform so that posted images can be thoroughly checked for nudity and if detected how it should be dealt with.

关键词： Runtime Machine learning algorithms Social networking (online) Organizations Media Mobile applications Reliability

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm

Web Application to predict Skin Disease: An Approach Using D...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Eelanila Thayalan Ayingaran Arumugavel Sharujan Ganeshan Makenthiran Dalaxshan Harinda Fernando Kaushalya Rajapakse Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system’s proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife.

关键词： Transfer learning Medical services Skin Pattern recognition Medical diagnostic imaging Standards Robots

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Attention-Based Multiscale Localization for Bone Age Assessment

Attention-Based Multiscale Localization for Bone Age Assessm...

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IEEE International Symposium on Biomedical Imaging

作者： Kyunghee Jung Toan Duc Nguyen Duc-Tai Le Junghyun Bum Chang-Hwan Son Hyunseung Choo Department of Superintelligence Engineering Sungkyunkwan University Korea College of Computing and Informatics Sungkyunkwan University Korea Sungkyun AI Research Institute Sungkyunkwan University Korea Department of Software Science & Engineering Kunsan National University Korea Department of Electrical and Computer Engineering Sungkyunkwan University Korea

Bone Age Assessment (BAA) is crucial for the biological maturity evaluation of children. Developing automated techniques of BAA has gained a lot of attention from both academia and medicine. This paper presents a novel deep-learning-based BAA method including refining and multi-scale processing of hand X-ray images. The refining step removes unnecessary background and noises in the images, resulting in a high-quality dataset. Such image refinement is beneficial for Region of Interest (ROI) localization step with self-attention mechanism. The localization model is trained with multiscale hand X-ray images separately to obtain multiple ROIs for each image. Eventually, the multiscale ROIs are used as complementary features of an image in training a regression model for BAA. We evaluate the performance of the proposed method using 2017 RSNA pediatric bone age challenge dataset. The results show mean absolute error of 3.52, which is 24.9% lower than state-of-the-art results.

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Fault-Tolerant Data Aggregation with Error-Checking for Smart Grids

Fault-Tolerant Data Aggregation with Error-Checking for Smar...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Congcong Wang Chen Wang Jian Shen Pandi Vijayakumar Zakirul Alam Bhuiyan School of Software Nanjing University of Information Science & Technology Nanjing China School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou China Department of Computer Science and Engineering University College of Engineering Tindivanam Tamilnadu India Department of Computer and Information Sciences Fordham University New York USA

With the rapid development of smart grid technology, a new challenge has been brought about by the massive data generated from distributed devices. Data aggregation technology plays a crucial role in smart grid schemes, in terms of energy data statistics and resource allocation. However, achieving fast error-checking and improving device fault-tolerance are still open issues in the smart grid data aggregation. To address these issues, this paper proposes an error-tolerant data aggregation scheme integrated with error-checking mechanisms. The scheme proposes a tree-based aggregation structure, facilitating swift error-checking of data. We combine masking values with user anonymity in the ElGamal cryptographic scheme to ensure the security of sensitive data and resist collusion attacks. Both theoretical and experimental analyses demonstrate that this scheme enables a lightweight, secure data aggregation, ensuring enhanced reliability and error-checking.

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Exploring Deep Learning and Grad-CAM for Speech-Based Detection of Mild Traumatic Brain Injury

Exploring Deep Learning and Grad-CAM for Speech-Based Detect...

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IEEE International Conference on Big Data

作者： Fredy Rojas Samaneh Madanian John Michael Templeton Christian Poellabauer Sandra L. Schneider Department of Computer Science and Software Engineering Auckland University of Technology (AUT) Auckland New Zealand Department of Computer Science and Engineering University of South Florida Tampa FL USA School of Computing and Information Sciences Florida International University Miami FL USA Department of Communicative Sciences & Disorders Saint Mary’s College Notre Dame IN USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Mild traumatic brain injury (mTBI) is challenging to diagnose due to its subtle and transient symptoms, making noninvasive diagnostic tools crucial for early detection. This study explores the use of a custom ResNet deep learning model combined with the Grad-CAM interpretability technique for mTBI detection via speech analysis. Speech data were transformed into Mel-spectrograms and fed into the model for binary classification between concussed and control individuals. The Grad-CAM method provided insights into which frequency regions of the Mel-spectrogram were most important for the model's predictions, with higher-frequency regions identified as significant for the model in detecting mTBI. Using Monte Carlo Cross-Validation (MCCV), we evaluated 50 different subject train-test split configurations to gain insights into the model's performance stability and variability. This analysis can assess the model's ability to learn consistent patterns within the dataset and suggest potential generalization tendencies. The variability observed in the performance metrics distribution underscores the importance of robust evaluation methods, particularly when working with small datasets. The combination of deep learning, robust evaluation technique and interpretability in this study contributes to the development of clinically viable, speech-based tools for mTBI detection, with potential applications in sports and healthcare settings.

关键词： Deep learning Analytical models Medical services Predictive models Brain modeling Data models Stability analysis Transient analysis Brain injuries Sports

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