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A study on the deviations in performance of FNNs and CNNs in the realm of grayscale adversarial images

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arXiv 2022年

作者： Nagabushanam, Durga Shree Mathew, Steve D.A. Chowdhary, Chiranji Lal Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Vellore632014 India Data Science and Applications Indian Institute of Technology Madras Chennai600036 India

Neural Networks are prone to having lesser accuracy in the classification of images with noise perturbation. Convolutional Neural Networks, CNNs are known for their unparalleled accuracy in the classification of benign images. But our study shows that they are extremely vulnerable to noise addition while Feed-forward Neural Networks, FNNs show very less correspondence with noise perturbation, maintaining their accuracy almost undisturbed. FNNs are observed to be better at classifying noise-intensive, single-channeled images that are just sheer noise to human vision. In our study, we have used the hand-written digits dataset, MNIST with the following architectures: FNNs with 1 and 2 hidden layers and CNNs with 3, 4, 6 and 8 convolutions and analyzed their accuracies. FNNs stand out to show that irrespective of the intensity of noise, they have a classification accuracy of more than 85%. In our analysis of CNNs with this data, the deceleration of classification accuracy of CNN with 8 convolutions was half of that of the rest of the CNNs. Correlation analysis and mathematical modelling of the accuracy trends act as roadmaps to these conclusions. © 2022, CC BY.

关键词： Convolutional neural networks

An Artificial Neural Network Model to Forecast Grain Crops in North-Kazakstan Agricultural Experimental Station

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An Artificial Neural Network Model to Forecast Grain Crops i...

2023 IEEE International Conference on Communications, information, Electronic and Energy systems, CIEES 2023

作者： Mimenbayeva, Aigul Omirtay, Maylen Niyazova, Rozamgul Bekmagambetova, Gulmira Suleimenova, Raya Tursumbayeva, Ainur Astana It University Dept. of Computational and Data Sciences Astana Kazakhstan Astana It University Specialty Smart Technologies Astana Kazakhstan L. Gumilyov Eurasian National University Dept. of Artificial Intelligence Technologies Astana Kazakhstan S. Seifullin Kazakh Agrotechnical Research University Dept. of Computer Science Astana Kazakhstan Eurasian Technological University School of Engineering and Information Technology Almaty Kazakhstan S. Seifullin Kazakh Agrotechnical Research University Dept. of Information Systems Astana Kazakhstan

ISBN: (纸本)9798350336917

The paper analyzes and predicts the yield of grain crops of LLP 'North-Kazakhstan Agricultural Experimental Station' for the last 30 years. The class of artificial neural networks-multilayer perceptron (MLP) was used as a research tool. The process of training of a model of a neural network consisting of 30 networks was performed on the basis of 70% training and 30% control samples. Based on the analysis of residuals histogram and scatter plot of the target and output function, the best neural network models MLP-1-7-1 (p=0.92), MLP-1-8-1 (p=0.97), MLP-1-5-1 (p=0.82) were selected. Next, using the best MLP 1-8-1 network, the model was tested the given data. Using time series projection, a graph was illustrated and a table of predicted grain yield values for the coming years was tabulated. The absolute error showed the high accuracy of the obtained best MLP 1-8-1 network for forecasting the amount of grain crop yield. The obtained model of artificial network can be applied in research and monitoring of agricultural production development. © 2023 IEEE.

关键词： Multilayer neural networks

Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse

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arXiv 2024年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Mao, Shiwen Kim, Dong In The College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Auburn University Auburn United States The School of Electrical and Computer Engineering University of Sydney Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of Experts (MoE) models with Generative Artificial Intelligence (GAI) for mobile edge computing to revolutionize content creation and interaction in the Metaverse. Specifically, we harness an MoE model's ability to efficiently manage complex data and complex tasks by dynamically selecting the most relevant experts running various sub-models to enhance the capabilities of GAI. We then present a novel framework that improves video content generation quality and consistency, and demonstrate its application through case studies. Our findings underscore the efficacy of MoE and GAI integration to redefine virtual experiences by offering a scalable, efficient pathway to harvest the Metaverse's full potential. © 2024, CC BY.

关键词： Augmented reality

A General Framework for Clustering and Distribution Matching with Bandit Feedback

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arXiv 2024年

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y.F. Scarlett, Jonathan Descartes Program CNRS@CREATE 138602 Singapore Department of Information Systems and Analytics School of Computing National University of Singapore 119077 Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Department of Computer Science Department of Mathematics Institute of Data Science National University of Singapore 119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank–Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ *** Codes 68T05 Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

Prediction of Sprint Delivery Capability in Agile Software Development Using Machine Learning

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Prediction of Sprint Delivery Capability in Agile Software D...

International Research Conference on Smart Computing and systems engineering (SCSE)

作者： Narayanasamy Lorshan P. M. A. K. Wijerathna B. T. G. S. Kumara Department of Computing and Information Systems Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Software Engineering Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Data Science Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

ISBN: (数字)9798331523268

ISBN: (纸本)9798331523275

Scrum is one of the most prominent agile development frameworks as it delivers the project in many sprints in an incremental manner. Sprint delivery capability is a crucial concept in the scrum projects as it assesses whether sprint can capable of delivering its issues on its length. It is difficult to consider forecasting sprint delivery capability in large scrum projects as multiple sprints are processed simultaneously and agile dynamics. Machine Learning (ML) models were applied in the existing studies to forecast sprint delivery but, had limitations in finding non-linear and hidden patterns. To address these problems, this research exploited the untapped ensemble learning approach to bridge the gap to improve the prediction of sprint delivery capability in different project progression levels. The study gathered the data from one of the large open-source scrum projects, JIRA which included 1873 iterations and 10853 issues in table format spanning at 30%, 50%, and 80% project progression levels. data preprocessing enabled to refinement of the data for analysis. The author enhanced the feature aggregation by using both statistical and automatic feature learning to derive the new features from the issue table and combine them with the iteration table. The key features affecting sprint delivery capability were identified using hybrid feature selection approach. The study developed the untapped ensemble models by choosing the different combinations of ML, Deep Learning (DL), and ensemble algorithms using appropriate ensemble methods and combination rules. The author examined the models and concluded that Categorical Boost (CatBoost) gained the highest accuracy of 96.8% at the 80% project progression level. This prediction assists in improving sprint planning, delivering the software on time, and getting more insights about sprint performance.

关键词： Representation learning Software algorithms Predictive models Feature extraction Prediction algorithms Software Planning Ensemble learning Modeling Forecasting

Multi-Resolution, Multi-Horizon Distributed Solar PV Power Forecasting with Forecast Combinations

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arXiv 2022年

作者： Perera, Maneesha De Hoog, Julian Bandara, Kasun Halgamuge, Saman Department of Mechanical Engineering School of Electrical Mechanical and Infrastructure Engineering The University of Melbourne Melbourne Australia School of Computing and Information Systems Melbourne Centre for Data Science The University of Melbourne Melbourne Australia

Distributed, small-scale solar photovoltaic (PV) systems are being installed at a rapidly increasing rate. This can cause major impacts on distribution networks and energy markets. As a result, there is a significant need for improved forecasting of the power generation of these systems at different time resolutions and horizons. However, the performance of forecasting models depends on the resolution and horizon. Forecast combinations (ensembles), that combine the forecasts of multiple models into a single forecast may be robust in such cases. Therefore, in this paper, we provide comparisons and insights into the performance of five state-of-the-art forecast models and existing forecast combinations at multiple resolutions and horizons. We propose a forecast combination approach based on particle swarm optimization (PSO) that will enable a forecaster to produce accurate forecasts for the task at hand by weighting the forecasts produced by individual models. Furthermore, we compare the performance of the proposed combination approach with existing forecast combination approaches. A comprehensive evaluation is conducted using a real-world residential PV power data set measured at 25 houses located in three locations in the United States. The results across four different resolutions and four different horizons show that the PSO-based forecast combination approach outperforms the use of any individual forecast model and other forecast combination counterparts, with an average Mean Absolute Scaled Error reduction by 3.81% compared to the best performing individual model. Our approach enables a solar forecaster to produce accurate forecasts for their application regardless of the forecast resolution or horizon. © 2022, CC BY-NC-ND.

关键词： Forecasting

Empowering secure and sustainable healthcare through federated learning and blockchain synergies in a Medical Internet of Things

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International Journal of Machine Learning and Cybernetics 2025年 1-33页

作者： Wang, Jing Manna, Sarbajit Aksoy, Muammer Sarkar, Arindam Rahman, Md Arafatur Noorwali, Abdulfattah Othman, Kamal M. Alenazi, Mohammed J. F. School of Computer and Information Qiannan Normal University for Nationalities Duyun China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Malaysia Department of Computer Science and Electronics Ramakrishna Mission Vidyamandira Howrah India Cyber Security Department College of Sciences Al-Mustaqbal University Babylon Iraq Computer Information Systems Department Ahmed Bin Mohammed Military College Doha Qatar School of Mathematics and Computer Science University of Wolverhampton Wolverhampton UK Department of Electrical Engineering Umm Al-Qura University Makkah Saudi Arabia Department of Electrical Engineering College of Engineering and Architecture Umm Al-Qura University Makkah Saudi Arabia Department of Computer Engineering College of Computer Science King Saud University Riyadh Saudi Arabia

To promote a patient-centered, sustainable healthcare ecosystem, this paper explores how blockchain and Federated Learning (FL) might be integrated into the Medical Internet of Things (MIoT). A decentralized architecture for improving data security, privacy, and interoperability between healthcare systems is proposed in this article. The article handles the drawbacks of centralized Machine Learning (ML) techniques, which frequently jeopardize the privacy of sensitive medical data, by utilizing the collaborative character of FL and the security aspects of blockchain. ML models are jointly trained while protecting patient privacy by leveraging distributed MIoT data. To effectively anticipate diseases, the study uses a variety of approaches, including FL coupled with AdaBoost, Extra Tree Classifier, Decision Tree (DT), or Linear Discriminant Analysis (LDA). Extensive testing encompassing feature selection, data splitting, cross-validation, and hyperparameter tuning guarantees the effectiveness and confidentiality of the suggested methodology. To assess the efficacy of the technique, performance measures such as Accuracy, Balanced Accuracy (BA), Fowlkes-Mallows Index (FM), Matthews Correlation Coefficient (MCC), and Bookmaker Informedness (BI) are calculated. The findings demonstrate an improved level of accuracy in contrast with conventional techniques. The research represents a novel approach to medical data analysis by choosing the best algorithm using several evaluation metrics and then incorporating it into FL.

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Enabling Protection for Critical Infrastructure through Security and Privacy in the Industrial Internet of Things

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Enabling Protection for Critical Infrastructure through Secu...

International Conference on Communication and Electronics systems (ICCES)

作者： B. Yasotha T. Vijayanandh P Thirumoorthy M.Sangeetha Priya Sasidaran S S.B. Prakalya Department of Data Science and Business Systems SRMIST Kattankulathur Department of Computer Science & Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Dhanalakshmi College of Engineering Chennai India Department of Computer Science and Engineering (Cyber Security) Saveetha Engineering College Chennai Department of Information Technology Panimalar Engineering College Chennai Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. Reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable.

关键词： Privacy Collaboration Standardization Critical infrastructure Encryption Security Stakeholders Protection Computer crime Industrial Internet of Things

Deep Graph Anomaly Detection: A Survey and New Perspectives

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arXiv 2024年

作者： Qiao, Hezhe Tong, Hanghang An, Bo King, Irwin Aggarwal, Charu Pang, Guansong School of Computing and Information Systems Singapore Management University Singapore Department of Computer Science University of Illinois Urbana-Champaign United States College of Computing and Data Science Nanyang Technological University Singapore Department of Computer Science & Engineering Chinese University of Hong Kong Hong Kong IBM T. J. Watson Research Center United States

Graph anomaly detection (GAD), which aims to identify unusual graph instances (e.g., nodes, edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its significance in a wide range of applications. Deep learning approaches, graph neural networks (GNNs) in particular, have been emerging as a promising paradigm for GAD, owing to its strong capability in capturing complex structure and/or node attributes in graph data. Considering the large number of methods proposed for GNN-based GAD, it is of paramount importance to summarize the methodologies and findings in the existing GAD studies, so that we can pinpoint effective model designs for tackling open GAD problems. To this end, in this work we aim to present a comprehensive review of deep learning approaches for GAD. Existing GAD surveys are focused on task-specific discussions, making it difficult to understand the technical insights of existing methods and their limitations in addressing some unique challenges in GAD. To fill this gap, we first discuss the problem complexities and their resulting challenges in GAD, and then provide a systematic review of current deep GAD methods from three novel perspectives of methodology, including GNN backbone design, proxy task design for GAD, and graph anomaly measures. To deepen the discussions, we further propose a taxonomy of 13 fine-grained method categories under these three perspectives to provide more in-depth insights into the model designs and their capabilities. To facilitate the experiments and validation of the GAD methods, we also summarize a collection of widely-used datasets for GAD and empirical performance comparison on these datasets. We further discuss multiple important open research problems in GAD to inspire more future high-quality research in this area. A continuously updated repository for GAD datasets, links to the codes of GAD algorithms, and empirical comparison is available at https://***/mala-lab/Awesome-Deep-G

关键词： Contrastive Learning