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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Lavanya, M. Chandan, Radha Raman Rajasekar, P. Rham, P. Rethik Deivakani, M. Mahesh Kumar, A.S. Master of Computer Applications Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India Shambhunath Institute of Engineering and Technology Department of Computer Science & Engineering Uttar PradeshJhalwa Prayagraj211015 India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India Saveetha Medical College Tamil Nadu Chennai602105 India Psna College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Pes College of Engineering Department of Electronics and Communication Engineering Karnataka Mandya571401 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Alzheimer's disease (AD) is a neurological illness which occurs over time. Early detection reduces the likelihood of disease worsening condition. Unfortunately, the present research focuses on defining disease processes in their existing stages rather than forecasting disease progression. The Generative Personalized embedding model (GPE) is a multi-layer perceptron that is proficient of relating prior knowledge to the prediction process by realizing the patient's behavioural data and helps in forecasting the disease progression. This work suggest a prediction process based on GPE. As a result, a GPA network is built with the connectivity layers and activated layers to observe the possible correlation among information and next generation of Alzheimer's disease. Experiments reveal that the outcomes significantly outperformed the majority of previous designs. © 2022 IEEE.

关键词： Machine learning

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Optimized Data Storage Algorithm of IoT Based on Cloud Computing in Distributed System

Optimized Data Storage Algorithm of IoT Based on Cloud Compu...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： K.Jaya Deepthi T.Suresh Balakrishnan Prabhakar Krishnan U.Samson Ebenezar Nageshwari Department of AIML Mohan Babu University Tirupati-2 AP India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India IEEE Research Scientist Center for Cyber security Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri-campus India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering CARE College of Engineering Trichy Tamil Nadu

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

IoT and cloud technology are vital for large systems. An improved data storage technique for IoT applications and cloud distribution is described in this research. Scalability, reliability, latency, and resource usage are internal and external to the algorithm. The approach above scales well with work portion or dataset size. The first experiment shows the algorithm scaling perfectly even as IoT devices generate additional data. Experiment 2 proves linear scalability works for 100,000 IoT devices, while Experiment 3 efficiently organizes 1 million data points. It proved the algorithm by tolerating manufactured faults. To ensure data service availability, Experiment 1 tests the system’s operational dependability on node failures and network disturbances. With 0.1% downtime even under challenging conditions, experiment 2 showed the algorithm’s incredible robustness in real-world applications. To improve data organization, this experiment exhibits data consistency when nodes fail. Lower and constant time improves operation safety and reliability. Algorithm 1 shows how the algorithm can quickly answer data queries and keep up with the massive amount of data transfers, supporting low-latency IoT applications. Average data retrieval latency of experiment 2 is 10 ms, which is real-time for vital information. Trial 3 processes data online with a latency <20 ms, providing fast insights for speedy response and decision-making. The adaptive distributed workload uses little resources, making the approach efficient. Exp1 uses distributed nodes to share data and processing, improving task efficiency. In experiment 2, 60% of the CPU, 70% of the Memory tool, and 80% of the Storage system are used, demonstrating platform resource efficiency. Trial 3 provides optimal resource allocation with less than 5% waste, making it an effective system that passes all performance benchmarks.

关键词： Cloud computing Scalability Decision making Memory Distributed databases Robustness Real-time systems

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Glaucoma Detection Using Convolutional Neural Network (CNN)

Glaucoma Detection Using Convolutional Neural Network (CNN)

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Tewodros Gizaw Tohye Zhiguang Qin Habte Lejebo Leka Negalign Wake Hundera School of Information and Software Engineering University of Electronic Science and Technology of China China School of Computer Science and Technology Zhejiang Normal University Jinhua China Zhejiang Institute of Optoelectronics Jinhua China

Today, glaucoma detection is a severe problem. Because it was predicted that there would be 111.8 million cases of glaucoma worldwide by 2040, up from the expected 76.0 million cases in 2020. Blindness results if early treatment is not given. The CNN model has been used in a variety of studies to identify glaucoma. The results obtained thus far are insufficient to reliably identify glaucoma. In order to detect glaucoma, which is a binary classification, we are utilizing the CNN model. We divided the REFUGUE data set into tanning and testing, with 80% of the photos being utilized for tanning and 20% for testing. Our model's accuracy score was 99.8% for the training set and 95.4% for the testing set. We get result of precision=98% and recall= 97%. We compare the model with other state of arts (SOTA) our experimental results reveal that, the proposed model for glaucoma detection using convolutional neural network is more proficient in glaucoma classification.

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Almost Exact Recovery in Gossip Opinion Dynamics over Stochastic Block Models

arXiv

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arXiv 2023年

作者： Xing, Yu Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm Sweden

We study community detection based on state observations from gossip opinion dynamics over stochastic block models (SBM). It is assumed that a network is generated from a two-community SBM where each agent has a community label and each edge exists with probability depending on its endpoints’ labels. A gossip process then evolves over the sampled network. We propose two algorithms to detect the communities out of a single trajectory of the process. It is shown that, when the influence of stubborn agents is small and the link probability within communities is large, an algorithm based on clustering transient agent states can achieve almost exact recovery of the communities. That is, the algorithm can recover all but a vanishing part of community labels with high probability. In contrast, when the influence of stubborn agents is large, another algorithm based on clustering time average of agent states can achieve almost exact recovery. Numerical experiments are given for illustration of the two algorithms and the theoretical results of the paper. © 2023, CC BY.

关键词： Recovery

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Machine Learning-Enhanced Analysis of Genomic Data for Precision Medicine

Machine Learning-Enhanced Analysis of Genomic Data for Preci...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： R. Sasikala K. Jaya Deepthi T. Suresh Balakrishnan Prabhakar Krishnan U. Samson Ebenezar Department Of Computer Science And Engineering Care College Of Engineering Trichy Tamil Nadu Department Of Aiml Mohan Babu University Tirupati-2 Ap India Department Of Computer Science And Engineering Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Chennai India Center For Cyber Security Systems And Networks Amrita Vishwa Vidyapeetham Amritapuri-Campus India Department Of Computer Science And Engineering Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

Precision medicine employs genetic data, which is enhanced with machine learning algorithms, to determine the most relevant treatment method per patient’s genotype. We have zoned in on the area of a machine learning-guided genetic information analysis in this study to assist in clinical decisionmaking of precision medicine while improvising. Genetic information for 500 cancer patients cases taken from the public database and cleaned to lean out the desired features were furnished. Different machine learning algorithms were trained, including SVM, random forest, gradient boost, deep learning (CNN) and ensemble (ensemble method). Their performance was investigated for predicting cancer subtypes. The ensemble model reached the top accuracy level ($88 \%$), the best sensitivity ($85 \%$), ever specificity ($91 \%$) and AUC score (0.95). By comparison a group had better result than an individual algorithm could across all metrics. In further, deep learning methods were reported to attain more accuracy and F1 scores which is more than traditional machine learning methods. These findings hence prequalify the usefulness of ML in investigating genotype data in precision medicine, and enable physicians with useful information to select treatment options, make prognosis, and indicate the medical care.

关键词： Deep learning Support vector machines Machine learning algorithms Accuracy Sensitivity Precision medicine Genomics

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Innate Cyber-Immunity and Acquired Cyber-Immunity across Multi-Area Power systems

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IEEE Transactions on Power systems 2025年

作者： Hou, Jiazuo Song, Yue Hou, Yunhe Peng, Jimmy Chih-Hsien National University of Singapore Department of Electrical and Computer Engineering Singapore Tongji University Department of Control Science and Engineering National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology Shanghai201804 China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong University of Hong Kong Shenzhen Institute of Research and Innovation Shenzhen518057 China

Organisms have evolved innate and acquired immune systems to defend against pathogens like coronaviruses. Similarly, power networks, threatened by cyber-attacks, desire cyber-immunity. Inspired by the immunology research, this study develops a cyber-immune strategy-characterizing innate cyber-immunity and establishing acquired cyber-immunity-for any selected portion of a power network (termed as power subnetwork) against stealthy false data injection (FDI) cyberattacks. Therein, without extrinsic protection and information asymmetry, this paper for the first time confirms the existence and provides closed-form conditions for power subnetworks that are innately immune to FDI cyber-attacks despite all measurements being compromised. Single-leader-multi-follower bi-level optimization models are then developed to strategically allocate measurement protection, leading to the acquired cyber-immunity, whose conditions for existence and the least cost are explicitly formulated. More importantly, a bi-directional cyber-immunity propagation mechanism is presented to provide the optimal spatial allocation of defense resources across adjacent subnetworks. It also explicitly determines the temporal allocation sequence, revealing its sequential and cumulative impacts on establishing cyber-immunity. This is achieved by designing recursive traversal algorithms, one of which is modelled as a post-order depth-first-search problem of a multi-fork tree. The proposed subnetwork-specific cyber-immune strategy is verified in a modified Australian National Electricity Market. © 1969-2012 IEEE.

关键词： Phishing

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Optimizing Healthcare Delivery through CloudBased Clinical Decision Support systems

Optimizing Healthcare Delivery through CloudBased Clinical D...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： U. Samson Ebenezar Jenila G. Vennila T. Suresh Balakrishnan Prabhakar Krishnan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering CARE College of Engineering Trichy Tamil Nadu Department of AI & ML School of Computing Mohan Babu University Tirupati Andhra Pradesh Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Center for Cyber security Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri-campus India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

The experiment aims at evaluating how four machine learning algorithms work: Decision Tree, Random Forest, SVM, and Neural Network, in a task, of binary classification. Models were evaluated using comprehensive metrics such as accuracy, precision, recall, F-score and AUC that revealed a good diagnostic performance. Models selected for the experiment and parameters tested on real datasets show steady changes in performance. The Neural Network model remained on top, achieving accuracy, precision, recall, F-score and AUC-ROC of 91.8%, 92.1%, 91.3%, 91.7% and 0.93 respectively. Random Forest displayed a competitive accordance along with the accuracy of 88.6%, precision of 89.2%, recall of 88.7%, and the F-score of 89.0%, and the AUC of 0.91. From the results, Support Vector Machine and Decision Tree models posted lower accuracy, recall, and fscores compared to the Neural Network and Random Forest models in most cases. These reveal the need for the improvement of the suitable machine learning model that comes from specific task needs as well as the consideration of each one of factors including accuracy, interpretability, and computational efficiency. Extensive and development of the model may result in more comprehensive understanding of how the model leads classifying of real-world applications such as medical diagnoses, fraud detection and target marketing.

关键词： Support vector machines Measurement Accuracy Machine learning algorithms Computational modeling Neural networks Medical services

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Convex Ternary Quartics Are SOS-Convex

arXiv

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arXiv 2024年

作者： Ahmadi, Amir Ali Blekherman, Grigoriy Parrilo, Pablo A. The Department of Operations Research and Financial Engineering Princeton University United States The School of Mathematics The Georgia Institute of Technology United States The Laboratory for Information and Decision Systems The Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

We show that if a ternary quartic form is convex, then it must be sos-convex;i.e, if the Hessian H(x) of a ternary quartic form is positive semidefinite for all x, then the biquadratic form yT H(x)y in the variables x:= (x1, x2, x3)T and y:= (y1, y2, y3)T must be a sum of squares. This result is in a meaningful sense the "convex analogue" of Hilbert’s celebrated theorem on ternary quartics. We show that exploiting the structure of the Hessian matrix is crucial in any possible proof of this result by presenting an explicit example of a biquadratic form b(x, y) that is symmetric in x and y, nonnegative, but not a sum of squares. © 2024, CC BY.

关键词： Matrix algebra

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Type-Representation Enhanced Event Detection via Structure Encoder

Type-Representation Enhanced Event Detection via Structure E...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Yuming Feng Linmeng Huang Haozhe Liang Xiaolong Jin Saiping Guan Jiafeng Guo School of Computer Science and Technology University of Chinese Academy of Sciences Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Institute of Systems Engineering Academy of Military Science Beijing

Event detection, aiming to detect whether a sentence contains the type of events of interest, is one of the important tasks in information Extraction. There exist many methods for event detection, which usually treat event detection as a classification problem. These existing methods suffer from the imbalanced training data of different event types. To relieve this problem, this paper proposes a new Type-Representation-Enhanced Event Detection (TREED) method, which introduce hierarchical event type representations to enhance classification accuracy. Specifically, TREED first models the hierarchical relationship among event types in the dataset; Next, upon interactions between the representations of trigger words and event types, it obtains the new representations of trigger words integrating event type information. Finally, TREED classifies the events based on the obtained more accurate trigger word representations. Experimental results on a commonly used benchmark dataset, ACE-2005, demonstrate that the proposed model can improve both accuracy and recall of the event detection task.

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INFLUENCE FUNCTIONS FOR SCALABLE DATA ATTRIBUTION IN DIFFUSION MODELS

arXiv

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arXiv 2024年

作者： Mlodozeniec, Bruno Eschenhagen, Runa Bae, Juhan Immer, Alexander Krueger, David Turner, Richard Department of Engineering University of Cambridge United Kingdom Max Planck Institute for Intelligent Systems Tübingen Germany Department of Computer Science University of Toronto Canada Vector Institute Toronto Canada Department of Computer Science ETH Zurich Switzerland Mila – Quebec AI Institute Montreal Canada The Alan Turing Institute London United Kingdom

Diffusion models have led to significant advancements in generative modelling. Yet their widespread adoption poses challenges regarding data attribution and interpretability. In this paper, we aim to help address such challenges in diffusion models by developing an influence function framework. Influence function-based data attribution methods approximate how a model’s output would have changed if some training data were removed. In supervised learning, this is usually used for predicting how the loss on a particular example would change. For diffusion models, we focus on predicting the change in the probability of generating a particular example via several proxy measurements. We show how to formulate influence functions for such quantities and how previously proposed methods can be interpreted as particular design choices in our framework. To ensure scalability of the Hessian computations in influence functions, we systematically develop K-FAC approximations based on generalised Gauss-Newton matrices specifically tailored to diffusion models. We recast previously proposed methods as specific design choices in our framework, and show that our recommended method outperforms previous data attribution approaches on common evaluations, such as the Linear Data-modelling Score (LDS) or retraining without top influences, without the need for method-specific hyperparameter tuning. Copyright © 2024, The Authors. All rights reserved.

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