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Communication & Computing (WCONF), World Conference on

作者： Vaishali Singh Pratik Pandey Santosh Chowhan S. Hemelatha Divya N Yogesh D. Deshpande Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Data Analytics and Mathematical Sciences Jain (Deemed to be University) Bangalore Karnataka Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai-127 Department of Information Technology Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

This analytical examine investigates the safety of net Key change (IKE) Protocol for wireless networks. It specializes in how IKE works and the unique additives that it involves so one can provide relaxed key trade. The paper examines the IKE protocol primarily based at the maximum current requirements and gives a complete review of ways the protocol works, its key components, the safety mechanisms it is predicated on, its blessings, and its weaknesses. thru our examine, it's miles discovered that even though IKE is considered to be one of the most relaxed protocols for Wi-Fi networks, there are nevertheless certain regions within the protocol that have ability for improvement. The paper concludes with numerous tips on a way to maximize the security furnished by using IKE while minimizing its potential for weakness.

关键词： Protocols Additives Reviews Wireless networks Authentication Organizations Internet Security Protection Wireless fidelity

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BCFTL: Blockchain-Enabled Multimodal Federated Transfer Learning for Decentralized Alzheimer's Diagnosis

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IEEE Internet of Things Journal 2025年

作者： Myrzashova, Raushan Alsamhi, Saeed Hamood Shvetsov, Alexey V. Hawbani, Ammar Guizani, Mohsen Wei, Xi School of Computer Science and Technology University of Science and Technology of China Hefei230027 China Insight Center for Data Analytics University of Galway Ireland Faculty of Engineering IBB University Ibb Yemen Moscow Polytechnic University Moscow Russia North-Eastern Federal University Yakutsk Russia School of Computer Science Shenyang Aerospace University Shenyang110136 China Abu Dhabi United Arab Emirates Department of Chemistry University of Science and Technology of China Anhui Hefei China

Efficient monitoring of carbon capture and storage (CCS) systems heavily relies on sensor data. However, sensors are susceptible to potential multiple faults, leading to performance degradation and posing a risk of catastrophic failures. Timely detection of sensor faults in CCS systems is crucial for safe and efficient carbon dioxide (CO2) pipeline operation. This paper addresses the challenge of diagnosing multiple sensor faults in CO2 pipelines by introducing a novel approach based on partial-distributed particle filter (PDPF). The novel distributed-filtering framework aims to reduce the computational complexity while identifying multiple faults in highly nonlinear systems. The proposed PDPF architecture comprises a collection of linear local filters and a nonlinear main filter. More specifically, the algorithm segregates nonlinear computations from local filters and assigns them to the main filter. The main filter handles the time updates involving all nonlinear computations associated with the nonlinear system, while the parallel linear local filters, each equipped with a distinct subset of sensor measurements, perform the measurement updates and combine their estimates via information fusion. As for fault detection and isolation, each local filter utilizes a novel kernel density estimation (KDE)-based approach that analyzes the consistency between model predictions and observed behavior, enabling the identification of sensor faults. Compared to existing methods, this approach reduces the computational requirements and is well-suited for highly nonlinear systems experiencing multiple sensor faults. Additionally, performance assessment via numerical simulations confirms its effectiveness and superiority in comparison to stateof-the-art alternative methods. © 2014 IEEE.

关键词： Transfer learning

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A Novel Hybrid AI Framework for Real-Time Prediction and Risk Assessment of Vector-Borne Epidemics using Spatiotemporal data

A Novel Hybrid AI Framework for Real-Time Prediction and Ris...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Rajesh Munirathnam Meenakshi Maindola Bhargav H K Prachi Ramesh Patil Pooja Bhatt Natrayan L Data & Analytics Division TechnoGen Inc. New Jersey USA Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Shridevi Institute of Engineering and Technology Industry Institute Interaction Cell(IIIC) Tumakuru India Department of Electronics and Telecommunication Engineering Jagdambha College of Engineering and Technology Yavatmal India Department of Computer science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Vadodara India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Vector-borne epidemics constitute major threats to global health that call for improved modeling systems for managing diseases. In order to incorporate such factors into an actual model, a traditional forecasting process may fail to grasp the nature of disease transmission adequately. We present a hybrid AI system based on LSTM and GCN for forest bug outbreak detection in real time. The framework also integrated a spatial-temporal analysis of climate variability, vector population fluctuations, and human mobility with climate variables across 15 regions over a five-year period (2019–2023). The hybrid framework also produced superior results, achieving a 94.2% prediction rate for health risk disease outbreaks, compared to a 17.3% improvement in traditional machine learning. When used to distinguish between high-risk and low-risk zones, the model demonstrated the following metrics: sensitivity of 92.8% and specificity of 95.1%. Real-time risk assessment demonstrated 89.6% effectiveness in indicating early warning and the best prediction lead time average of 21 days. This framework significantly enhances the prediction of vector-borne diseases by offering a dependable, real-time platform for disease surveillance and risk assessment. Its high accuracy and early warning capabilities make the system a useful tool in epidemiological and public health campaigns and control measures.

关键词： Epidemics Accuracy Surveillance Real-time systems Vectors Spatiotemporal phenomena Risk management Public healthcare Long short term memory Diseases

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Classification and Contrast of Supervised Machine Learning Algorithms

Classification and Contrast of Supervised Machine Learning A...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Ramakrishnan Raman Rejuwan Shamim Shaik Vaseem Akram Lalit Thakur Biju G. Pillai R. Ponnusamy Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering with Data Science Maharishi University of Information Technology Noida Uttaranchal Institute of Technology Uttaranchal University Department of CIVIL Parul University Department of IT & Business Analytics BIIB & Dean - Faculty of Management Sri Balaji University Pune (SBUP) Department of Computer Science & Engineering Chennai Institute of Technology

Supervised Classification (SML) is the pursuit of systems that reasoning from externally given instances to generate broad hypotheses, which subsequently generate predictions for future instances. One of the jobs performed by intelligent systems most commonly is supervised classification. Based on the data set, number of occurrences and variables in this study, the most efficient classification algorithm is selected. It is comprehensively described and contrasted with various supervised learning methods. Seven distinct machine learning algorithms were taken into consideration utilizing the Waikato Environment for data Analysis (WEDA) machine learning tool. For the identification process, the data set was used with 780 instances, eight independent variables (quality), and one dependent variable (variable). According to the data, SVM was the technique with the highest degree of precision and accuracy. Accordingly, the next accurate classification algorithms after SVM were determined to be Naive Bayes and Random Forest. The study demonstrates that precision (accuracy) and model construction time are two factors, whereas kappa statistics and mean error percentage (MAE) are two other factors. Therefore, controlled predictive machine learning requires precision, accuracy, and minimal error in ML algorithms.

关键词： Support vector machines Machine learning algorithms Supervised learning Training data Prediction algorithms Time measurement Classification algorithms

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Towards Sustainable Industry 4.0: A Survey on Greening the Ioe in 6g Networks

SSRN

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SSRN 2024年

作者： Alsamhi, Saeed Hamood Hawbani, Ammar Sahal, Radhya Srivastava, Sumit Kumar, Santosh Zhao, Liang Al-Qaness, Mohammed A.A. Hassan, Jahan Guizani, Mohsen Curry, Edward Insight Centre for Data Analytics University of Galaway Ireland Faculty of Engineering IBB University Ibb70270 Yemen School of Computer Science Shenyang Aerospace University Shenyang110136 China Computer Science Hudida University Hudida Ibb Yemen Department of Electronics and Communication Engineering FET MJP Rohilkhand University Uttar Pradesh Bareilly243006 India Department of CSE IIIT Naya Raipur Chhattisgarh India College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Central Queensland University Australia Abu Dhabi United Arab Emirates Insight Centre for Data Analytics University of Galway Ireland

The dramatic recent increase of the smart Internet of Everything (IoE) in Industry 4.0 has significantly increased energy consumption, carbon emissions, and global warming. IoE applications in Industry 4.0 face many challenges, including energy efficiency, heterogeneity, security, interoperability, and centralization. Therefore, Industry 4.0 in Beyond the Sixth-Generation (6G) networks demands moving to sustainable, green IoE and identifying efficient and emerging technologies to overcome sustainability challenges. Many advanced technologies and strategies efficiently solve issues by enhancing connectivity, interoperability, security, decentralization, and reliability. Greening IoE is a promising approach that focuses on improving energy efficiency, providing a high Quality of Service (QoS), and reducing carbon emissions to enhance the quality of life at a low cost. This survey provides a comprehensive overview of how advanced technologies can contribute to green IoE in the 6G network of Industry 4.0 applications. This survey provides a comprehensive overview of advanced technologies, including Blockchain, Digital Twins (DTs), Unmanned Aerial Vehicles (UAVs, a.k.a. drones), and Machine Learning (ML), to improve connectivity, QoS, and energy efficiency for green IoE in 6G networks. We evaluate the capability of each technology in greening IoE in Industry 4.0 applications and analyze the challenges and opportunities to make IoE greener using the discussed technologies. © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

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Cauchy activation function and XNet

arXiv

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arXiv 2024年

作者： Li, Xin Xia, Zhihong Zhang, Hongkun Department of Computer Science Northwestern University EvanstonIL United States Mathematical Modelling and Data Analytics Center Oxford Suzhou Centre for Advanced Research Suzhou China School of Natural Science Great Bay University Guangdong China Department of Mathematics Northwestern University EvanstonIL United States Department of Mathematics and Statistics University of Massachusetts Amherst AmherstMA United States

We have developed a novel activation function, named the Cauchy Activation Function. This function is derived from the Cauchy Integral Theorem in complex analysis and is specifically tailored for problems requiring high precision. This innovation has led to the creation of a new class of neural networks, which we call (Comple)XNet, or simply XNet. We will demonstrate that XNet is particularly effective for high-dimensional challenges such as image classification and solving Partial Differential Equations (PDEs). Our evaluations show that XNet significantly outperforms established benchmarks like MNIST and CIFAR-10 in computer vision, and offers substantial advantages over Physics-Informed Neural Networks (PINNs) in both low-dimensional and high-dimensional PDE scenarios. © 2024, CC BY.

关键词： Image classification

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A Quality-Aware Rendezvous Framework for Cognitive Radio Networks

A Quality-Aware Rendezvous Framework for Cognitive Radio Net...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Hai Liu Lu Yu Chung Keung Poon Zhiyong Lin Yiu- Wing Leung Xiaowen Chu Department of Computing The Hang Seng University of Hong Kong Hong Kong Department of Computer Science GuangDong Polytechnic Normal University Guangzhou China Department of Computer Science Hong Kong Baptist University Hong Kong Data Science and Analytics Thrust Hong Kong University of Science and Technology (Guangzhou)

In cognitive radio networks, rendezvous is a fundamental operation by which cognitive users establish communication links. Most of existing works were devoted to shortening the time-to-rendezvous (TTR) but paid little attention to qualities of the channels on which rendezvous is achieved. In fact, qualities of channels, such as resistance to primary users' activities, have a great effect on the rendezvous operation. If users achieve a rendezvous on a low-quality channel, the communication link is unstable and the communication performance is poor. In this case, re- rendezvous is required which results in considerable communication overhead and a large latency. In this paper, we first show that actual TTRs of existing rendezvous solutions increase by 65.40-104.38% if qualities of channels are not perfect. Then we propose a Quality-Aware Rendezvous Framework (QARF) that can be applied to any existing ren-dezvous algorithms to achieve rendezvous on high-quality channels. The basic idea of QARF is to expand the set of available channels by selectively duplicating high-quality channels. We prove that QARF can reduce the expected TTR of any rendezvous algorithm when the expanded ratio $\lambda$ is smaller than the threshold $(-3+\sqrt{1+4(\frac{\sigma}{\mu})^{2}}) / 2$ , where $\mu$ and $\sigma$ , respectively, are the mean and the standard deviation of qualities of channels. We further prove that QARF can always reduce the expected TTR of Random algorithm by a factor of $1+(\frac{\sigma}{\mu})^{2}$ . Extensive experiments are conducted and the results show that QARF can significantly reduce the TTRs of the existing rendezvous algorithms by 10.50-51.05 % when qualities of channels are taken into account.

关键词： Resistance Sensors Cognitive radio Standards

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Multirate Time-Integration based on Dynamic ODE Partitioning through Adaptively Refined Meshes for Compressible Fluid Dynamics

arXiv

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arXiv 2024年

作者： Doehring, Daniel Schlottke-Lakemper, Michael Gassner, Gregor J. Torrilhon, Manuel Applied and Computational Mathematics RWTH Aachen University Schinkelstrasse 2 North Rhine-Westphalia Aachen52062 Germany University of Stuttgart Germany High-Performance Scientific Computing Center for Advanced Analytics and Predictive Sciences University of Augsburg Germany Department of Mathematics and Computer Science Center for Data and Simulation Science University of Cologne Germany

In this paper, we apply the Paired-Explicit Runge-Kutta (P-ERK) schemes by Vermeire et. al. [1, 2] to dynamically partitioned systems arising from adaptive mesh refinement. The PERK schemes enable multirate time-integration with no changes in the spatial discretization methodology, making them readily implementable in existing codes that employ a method-of-lines approach. We show that speedup compared to a range of state of the art Runge-Kutta methods can be realized, despite additional overhead due to the dynamic re-assignment of flagging variables and restricting nonlinear stability properties. The effectiveness of the approach is demonstrated for a range of simulation setups for viscous and inviscid convection-dominated compressible flows for which we provide a reproducibility repository. In addition, we perform a thorough investigation of the nonlinear stability properties of the Paired-Explicit Runge-Kutta schemes regarding limitations due to the violation of monotonicity properties of the underlying spatial discretization. Furthermore, we present a novel approach for estimating the relevant eigenvalues of large Jacobians required for the optimization of stability *** Codes 65L06, 65M20, 76Mxx, 76Nxx © 2024, CC BY.

关键词： Runge Kutta methods

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An Improved Unbalanced data Classification Method Based on Hybrid Sampling Approach 4

An Improved Unbalanced Data Classification Method Based on H...

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2021 IEEE 4th International Conference on Big data and Artificial Intelligence, BDAI 2021

作者： Xu, Biru Wang, Wenjia Yang, Rui Han, Qi University of Liverpool Department of Computer Science Liverpool United Kingdom School of Advanced Technology Research Institute of Big Data Analytics Xi an Jiaotong-Liverpool University Suzhou China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781665412704

The problem of data imbalance has received far- reaching concerns since they could affect the accuracy of classification problem in the area of machine learning. As the minority class instances can be ignored by traditional classifiers, it is necessary to improve the recognition rate of minority instances. Therefore, the paper proposes a new hybrid sampling method to solve the data imbalance problem by enlarging the proportion of minority instances. For the oversampling part, a variant of SMOTE is provided combining methods of LR-SMOTE and CCR (Combined Cleaning and Resampling Algorithm);for the under-sampling part, the Tomek-link method is utilized to complete the task. After the pre-processing stage, the data set is classified by Random Forest (RF). Experimental results show that the novel algorithm effectively enhances the performance of RF on the data set with a higher accuracy. © 2021 IEEE.

关键词： data mining

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Interpretable bilinear attention network with domain adaptation improves drug-target prediction

arXiv

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arXiv 2022年

作者： Bai, Peizhen Miljković, Filip John, Bino Lu, Haiping Department of Computer Science University of Sheffield Sheffield United Kingdom Imaging and Data Analytics Clinical Pharmacology & Safety Sciences R&D AstraZeneca Gothenburg Sweden Imaging and Data Analytics Clinical Pharmacology & Safety Sciences R&D AstraZeneca Waltham United States

Predicting drug-target interaction is key for drug discovery. Recent deep learning-based methods show promising performance but two challenges remain: (i) how to explicitly model and learn local interactions between drugs and targets for better prediction and interpretation;(ii) how to generalize prediction performance on novel drug-target pairs from different distribution. In this work, we propose DrugBAN, a deep bilinear attention network (BAN) framework with domain adaptation to explicitly learn pair-wise local interactions between drugs and targets, and adapt on out-of-distribution data. DrugBAN works on drug molecular graphs and target protein sequences to perform prediction, with conditional domain adversarial learning to align learned interaction representations across different distributions for better generalization on novel drug-target pairs. Experiments on three benchmark datasets under both in-domain and cross-domain settings show that DrugBAN achieves the best overall performance against five state-of-the-art baselines. Moreover, visualizing the learned bilinear attention map provides interpretable insights from prediction results. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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