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Forwarding Factor Routing Protocol for Opportunistic Internet of Things Networks

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Forwarding Factor Routing Protocol for Opportunistic Interne...

International Conference on Contemporary Computing and Informatics (IC3I)

作者： Mohammed E. Seno Areej Adnan Abed Yousif A. Hamad Sumit Pundir Arun Pratap Srivastava Safdar Ali Department of Computer Science Al-Maarif University College Iraq Anbar Province Al-Ramadi Artificial Intelligence Laboratory Siberian Federal University Krasnoyarsk Russia Collage of Information Technology Imam Ja’afar Al-Sadiq University Baghdad Iraq Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of CSE Lloyd Institute of Engineering and Technology Greater Noida Uttar Pradesh India Lloyd Institute of Engineering and Technology Greater Noida Uttar Pradesh India

The delay opportunistic network is one of the subnetworks that make up the more tolerant network. The Internet of Things and the Opportunistic Network are the two components that come together to make up what is known as the Opportunistic IoT Network. Mobile ad hoc networks, on the other hand, need a connection that is always active and goes all the way to the end in order to function properly. But in an opportunistic network, the nodes are free to go anywhere they choose, and messages can use different channels to get where they need to go. Therefore, opportunistic networks make the process of routing more difficult. Opportunistic networks are characterised by the fact that the connections between nodes in the network are dynamically rearranged in response to the mobility of the nodes. This is accomplished by communication with neighbouring nodes using a technique referred to as “store-carry-and-forward." The message is transmitted from one node to the next until it reaches the node that is its final destination. As a result of our investigation, we propose a brand new routing system that we term Forwarding Factor with Acknowledgement. Because it identifies potential forwarders by watching how nodes communicate with one another, the number of copies that are dispersed throughout networks is decreased. An acknowledgement is transmitted in the similar manner back to the sender node to determine whether or not the delivery was successful at the destination node. Forwarding Factor with Acknowledgement is a protocol that was developed with the intention of improving upon the performance of earlier protocols such as Epidemic and Prophet by lowering the amount of communication overhead and raising the delivery ratio.

关键词： Epidemics Routing Routing protocols Delays Internet of Things Informatics Mobile ad hoc networks

Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection

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arXiv 2025年

作者： Xu, Meiyan Chen, Qingqing Chen, Duo Ding, Yi Wang, Jingyuan Gu, Peipei Pan, Yijie Huang, Deshuang Guo, Jiayang Zhang, Xun Department of Computer Science The Institute of Human Factors Engineering Minnan Normal University Zhangzhou363000 China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing210023 China College of Computing and Data Science Nanyang Technological University Singapore School of Digital Media and Design Arts Beijing University of Posts and Telecommunications Beijing100088 China College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450000 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo315200 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315200 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361005 China

EEG-based fatigue monitoring can effectively reduce the incidence of related traffic accidents. In the past decade, with the advancement of deep learning, convolutional neural networks (CNN) have been increasingly used for EEG signal processing. However, due to the data’s non-Euclidean characteristics, existing CNNs may lose important spatial information from EEG, specifically channel correlation. Thus, we propose the node-holistic graph convolutional network (NHGNet), a model that uses graphic convolution to dynamically learn each channel’s features. With exact fit attention optimization, the network captures inter-channel correlations through a trainable adjacency matrix. The interpretability is enhanced by revealing critical areas of brain activity and their interrelations in various mental states. In validations on two public datasets, NHGNet outperforms the SOTAs. Specifically, in the intra-subject, NHGNet improved detection accuracy by at least 2.34% and 3.42%, and in the inter-subjects, it improved by at least 2.09% and 15.06%. Visualization research on the model revealed that the central parietal area plays an important role in detecting fatigue levels, whereas the frontal and temporal lobes are essential for maintaining vigilance. Copyright © 2025, The Authors. All rights reserved.

关键词： Electroencephalography

Brain MRI Synthesis Using Stylegan2-ADA

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Brain MRI Synthesis Using Stylegan2-ADA

IEEE International Symposium on Biomedical Imaging

作者： Matteo Lai Chiara Marzi Mario Mascalchi Stefano Diciotti Department of Electrical Electronic and Information Engineering "Guglielmo Marconi" - DEI University of Bologna Cesena Italy Alma Mater Research Institute for Human-Centered Artificial Intelligence University of Bologna Bologna Italy Department of Statistics Computer Science Applications "G. Parenti" University of Firenze Firenze Italy Experimental and Clinical Biomedical Sciences "Mario Serio" University of Florence Firenze Italy Clinical Epidemiology Unit Institute for Cancer Research Prevention and Clinical Network (ISPRO) Florence Italy

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Deep learning (DL) applications in the medical field often face challenges related to limited data availability, resulting in issues like overfitting and imbalanced datasets. Synthetic data offers a promising solution to these problems by enabling data augmentation and enhancing the performance of DL models. In this study, we trained the state-of-the-art generative model StyleGAN2-ADA on 1412 images from the Alzheimer’s disease neuroimaging initiative (ADNI) dataset to generate synthetic slices of T1-weighted brain MRI of healthy subjects. The quality of the synthetic images has been evaluated through quantitative and qualitative assessments, including a visual Turing test conducted by an expert observer with 2000 images. The observer achieved an accuracy of 52.95%, indicative of a performance level comparable to random guessing. These results demonstrate the capability of StyleGAN2-ADA to generate anatomically relevant synthetic brain MRI data.

关键词： Neuroimaging Visualization Magnetic resonance imaging Observers Brain modeling Data augmentation Data models

Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

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arXiv 2022年

作者： Rahman, Chnoor M. Rashid, Tarik A. Ahmed, Aram Mahmood Mirjalili, Seyedali Applied Computer Department College of Medicals and Applied Sciences Charmo University Sulaimany Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Department of Information Technology Technical College of Informatics Sulaimani Polytechnic University Sulaimany Iraq Department of Information Technology College of Science and Technology University of Human Development Sulaimany Iraq Centre for Artificial Intelligence Research and Optimization Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

In this work, a new multiobjective optimization algorithm called multiobjective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of transferring students from high school to college. The proposed technique produces a set of non-dominated solutions. To judge the ability and efficacy of the proposed multiobjective algorithm, it is evaluated against a group of benchmarks and five real-world engineering optimization problems. Additionally, to evaluate the proposed technique quantitatively, several most widely used metrics are applied. Moreover, the results are confirmed statistically. The proposed work is then compared with three multiobjective algorithms, which are MOWCA, NSGA-II, and MODA. Similar to the proposed technique, the other algorithms in the literature were run against the benchmarks, and the real-world engineering problems utilized in the paper. The algorithms are compared with each other employing descriptive, tabular, and graphical demonstrations. The results proved the ability of the proposed work in providing a set of non-dominated solutions, and that the algorithm outperformed the other participated algorithms in most of the cases. © 2022, CC BY-NC-SA.

关键词： Multiobjective optimization

Position: topological deep learning is the new frontier for relational learning 24

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Position: topological deep learning is the new frontier for ...

Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Tolga Birdal Michael Bronstein Gunnar Carlsson Justin Curry Yue Gao Mustafa Hajij Roland Kwitt Pietro Liò Paolo Di Lorenzo Vasileios Maroulas Nina Miolane Farzana Nasrin Karthikeyan Natesan Ramamurthy Bastian Rieck Simone Scardapane Michael T. Schaub Petar Veličković Bei Wang Yusu Wang Guo-Wei Wei Ghada Zamzmi Department of Mathematics The University of Manchester Manchester UK Department of Computing Imperial College London London UK Department of Computer Science University of Oxford Oxford UK Department of Mathematics Stanford University Stanford and BlueLightAI Inc University at Albany New York School of Software Tsinghua University Beijing China University of San Francisco San Francisco Department of Artificial Intelligence and Human Interfaces University of Salzburg Austria Department of Computer Science and Technology University of Cambridge Cambridge UK Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy Department of Mathematics University of Tennessee Knoxville Department of Electrical and Computer Engineering UC Santa Barbara Santa Barbara Department of Mathematics University of Hawai' at M-ānoa Hawai'i IBM Corporation New York Helmholtz Munich Munich Germany and Technical University of Munich Munich Germany Technical University of Munich Munich Germany Google DeepMind and Department of Computer Science and Technology University of Cambridge Cambridge UK School of Computing University of Utah Utah Computer Science and Engineering Department University of California San Diego San Diego Department of Mathematics Michigan State University East Lansing Michigan University of South Florida Florida

Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field.

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Edge Learning for Large-Scale Internet of Things With Task-Oriented Efficient Communication

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arXiv 2023年

作者： Xie, Haihui Xia, Minghua Wu, Peiran Wang, Shuai Poor, H. Vincent The School of Electronics and Information Technology Sun Yat-Sen University Guangzhou510006 China The Southern Marine Science and Engineering Guangdong Laboratory Zhuhai519082 China The Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

In the Internet of Things (IoT) networks, edge learning for data-driven tasks provides intelligent applications and services. As the network size becomes large, different users may generate distinct datasets. Thus, to suit multiple edge learning tasks for large-scale IoT networks, this paper performs efficient communication under the task-oriented principle by using the collaborative design of wireless resource allocation and edge learning error prediction. In particular, we start with multi-user scheduling to alleviate co-channel interference in dense networks. Then, we perform optimal power allocation in parallel for different learning tasks. Thanks to the high parallelization of the designed algorithm, extensive experimental results corroborate that the multi-user scheduling and task-oriented power allocation improve the performance of distinct edge learning tasks efficiently compared with the state-of-the-art benchmark algorithms. © 2023, CC BY-NC-SA.

关键词： Internet of things

EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review

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Computational intelligence and Neuroscience 2022年第0期2022卷 6486570页

作者： Ahmad, Ijaz Wang, Xin Zhu, Mingxing Wang, Cheng Pi, Yao Khan, Javed Ali Khan, Siyab Samuel, Oluwarotimi Williams Chen, Shixiong Li, Guanglin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China School of Biomedical Engineering Sun Yat-Sen University Guangzhou China Department of Software Engineering University of Science and Technology Khyber Pakhtunkhwa Bannu Pakistan Institute of Computer Science and Information Technology The University of Agriculture Khyber Pakhtunkhwa Peshawar Pakistan

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG's noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG's low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL m

关键词： Electroencephalography

Cardiovascular disease identification using a hybrid CNN-LSTM model with explainable AI

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Informatics in Medicine Unlocked 2023年 42卷

作者： Hossain, Md Maruf Ali, Md Shahin Ahmed, Md Mahfuz Rakib, Md Rakibul Hasan Kona, Moutushi Akter Afrin, Sadia Islam, Md Khairul Ahsan, Md Manjurul Raj, Sheikh Md Razibul Hasan Rahman, Md Habibur Department of Biomedical Engineering Islamic University Kushtia 7003 Bangladesh Department of Food and Nutrition Government College of Applied Human Science Azimpur Dhaka 1205 Bangladesh Department of Radiology Northwestern University Machine and Hybrid Intelligence Lab Chicago 60611 IL United States Industrial & Production Engineering Shahjalal University of Science and Technology Sylhet 3114 Bangladesh Department of Information and Communication Technology Islamic University Kushtia 7003 Bangladesh Department of Computer Science and Engineering Islamic University Kushtia 7003 Bangladesh Center for Advanced Bioinformatics and Artificial Intelligent Research Islamic University Kushtia 7003 Bangladesh

Cardiovascular disease (CVD) is a leading cause of death worldwide, with millions dying each year. The identification and early diagnosis of CVD are critical in preventing adverse health outcomes. Hence, this study proposes a hybrid deep learning (DL) model that combines a convolutional neural network (CNN) and long short-term memory (LSTM) to identify CVD from the clinical data. This study utilizes CNN to extract the relevant features from the input data and the LSTM network to process sequential data and capture dependencies and patterns over time. This study provides insights into the potential of a hybrid DL model combined with feature engineering and explainable AI to improve the accuracy and interpretability of CVD prediction. We evaluated our model on a publicly available dataset where the proposed CNN-LSTM achieved a high accuracy of 73.52% and 74.15% with and without feature engineering, respectively, in identifying individuals with CVD, which is the best result compared to the current state-of-the-art model. The results of this study demonstrate the potential of DL models for the early diagnosis of CVD. Our proposed CNN-LSTM model also incorporates explainable AI to identify the top features responsible for CVD. They could be used to develop more eﬀective screening tools in clinical practice. © 2023 The Authors

关键词： Cardiovascular disease CNN-LSTM Deep learning Explainable AI Feature engineering

Building human Values into Recommender Systems: An Interdisciplinary Synthesis

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arXiv 2022年

作者： Stray, Jonathan Halevy, Alon Assar, Parisa Hadfield-Menell, Dylan Boutilier, Craig Ashar, Amar Beattie, Lex Ekstrand, Michael Leibowicz, Claire Sehat, Connie Moon Johansen, Sara Kerlin, Lianne Vickrey, David Singh, Spandana Vrijenhoek, Sanne Zhang, Amy Andrus, McKane Helberger, Natali Proutskova, Polina Mitra, Tanushree Vasan, Nina Center for Human-Compatible Artificial Intelligence UC Berkeley United States Meta AI United States Department of Electrical Engineering and Computer Science MIT United States Google Research United States Spotify Inc. Computer Science Department Boise State University United States Partnership on AI Hacks/Hackers Brainstorm The Stanford Lab for Mental Health Innovation Department of Psychiatry and Behavioral Sciences Stanford University United States BBC United Kingdom Meta United States New America's Open Technology Institute United States Faculty of Law Information Law University of Amsterdam Netherlands Allen School of Computer Science & Engineering University of Washington United States Institute for Information Law University of Amsterdam Netherlands Information and Computer Science University of Washington United States

Recommender systems are the algorithms which select, filter, and personalize content across many of the world's largest platforms and apps. As such, their positive and negative effects on individuals and on societies have been extensively theorized and studied. Our overarching question is how to ensure that recommender systems enact the values of the individuals and societies that they serve. Addressing this question in a principled fashion requires technical knowledge of recommender design and operation, and also critically depends on insights from diverse fields including social science, ethics, economics, psychology, policy and law. This paper is a multidisciplinary effort to synthesize theory and practice from different perspectives, with the goal of providing a shared language, articulating current design approaches, and identifying open problems. It is not a comprehensive survey of this large space, but a set of highlights identified by our diverse author cohort. We collect a set of values that seem most relevant to recommender systems operating across different domains, then examine them from the perspectives of current industry practice, measurement, product design, and policy approaches. Important open problems include multi-stakeholder processes for defining values and resolving trade-offs, better values-driven measurements, recommender controls that people use, non-behavioral algorithmic feedback, optimization for long-term outcomes, causal inference of recommender effects, academic-industry research collaborations, and interdisciplinary policy-making. Copyright © 2022, The Authors. All rights reserved.

关键词： Recommender systems