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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Alsalami, Zaid Satish, E.G. Ponnibala, M. Rajalakshmi, J. Sushama, C. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Dept. of Computer Science and Engineering Bengaluru India Velalar College of Engineering and Technology Erode India School of Computing Mohan Babu University Dept. of Cse Tirupati India

ISBN: (纸本)9798350373110

The IoT based scheme e-health uses some measurable device for to monitor the health condition of our body. The long-term measurable system like cardiovascular, electrocardiogram (ECG) are significant methods to diagnose the patient's diseases. The drawbacks of this measurable system such as high computational cost, consume more time for training and ECG signals contained some noise and corrupted signals. To addresses this kind of problem, the Denoising Convolution Auto Encoder (DCAE) with Bidirectional Long Short-Term Memory (Bi-LSTM) method is implemented. The DCAE contains encoder and decoder part to convert the ECG corrupted signals into compressed form. Bi-LSTM attached to encoder part that carries the input signals and trains that. Bi-LSTM carries double layers, which makes the training process easily. This proposed method tested on MIT-BIH databases. The advantages of this method, auto encoder easily convert the corrupted signals without using any parameters and reduce computation cost. Bi-LSTM reducing the training process, time consumption and it consisted of double layers, which increase the compression process of signals fastly. The developed DCAE-Bi-LSTM gives better output rate of 97.85% which is better than other existing methods like CNN-LSTM and SVM. © 2024 IEEE.

关键词： Signal denoising

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Internet of Things routing protocol with mobility awareness and energy efficiency 3

Internet of Things routing protocol with mobility awareness ...

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3rd International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2023

作者： Chauhan, Birendra Kumar Banupriya, M. Venkatagurunatham, K. Sungheetha, Akey Kumari, Ch D V P Karthick, L. Iimt University School of Basic Sciences and Technology Uttar Pradesh Meerut250001 India Hindustan Institute of Technology Department of Computer Science and Engineering Coimbatore India Andhrapradesh Guntur522019 India Adama Science and Technology University School of Electrical Engineering and Computing Department of Computer Science and Engineering Nazareth1888 Ethiopia Aditya College of Engineering Department of Computer Science and Engineering Andhra Pradesh Surampalem533437 India Hindusthan College of Engineering and Technology Department of Mechanical Engineering Coimbatore641032 India

ISBN: (纸本)9798350399264

Internet of things enabled devices are considered one of the technologies for the future and at the same time, the mass use of IoT devices has revolutionised the way of using modern technology. Hence, with the mass implication creating energy-efficient systems without compromising on the data ratio has become mandatory. Thus, the study collected secondary data and conducted a thematic analysis. Such methods of study have emperors the researchers to collect reliable data and with the discussion create a reliable result. For a better understanding, this study has discussed bout different kinds of routing and helps o create an energy-efficient system with better mobility. The discussion is based on different bandwidths of data along with their use at the same time problem of the system is mentioned at the end. Such a critical discussion allows a reader to prepare their own opinion about a system creation which have an energy-efficient topology and that has improved data packet ratio. © 2023 IEEE.

关键词： Energy efficiency

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Real-Time Enhanced GAN-Powered Intrusion Prevention System: Safeguarding Networks with Advanced AI 2

Real-Time Enhanced GAN-Powered Intrusion Prevention System: ...

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Sutherlin Subitha, G. Seldev Christopher, C. Wilfred Blessing, N.R. Sheeja Kumari, V. Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Kanyakumari India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Tamil Nadu Nagercoil India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Saveetha School of Engineering Simats Univeristy Department of Computational Intellegence Chennai600025 India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542573

In an era of escalating cyber threats and evolving networks, the demand for robust intrusion prevention is paramount. Introducing the Real-Time Enhanced GAN-Powered Intrusion Prevention System (RT-EGAN-IPS), a state-of-the-art solution fortified with advanced AI. RT-EGAN-IPS harnesses the power of Generative Adversarial Networks (GANs) to revolutionize real-time intrusion detection. GANs, renowned for their data synthesis capabilities, excel at uncovering subtle anomalies and emerging threats within network data. Its real-time adaptability enables it to swiftly identify novel attack patterns by continuously learning from incoming network data. Deep reinforcement learning and predictive analytics empower RT-EGAN-IPS to not only detect intrusions but autonomously respond, bolstering network protection. Versatile in handling multi-modal data streams, from text logs to multimedia content, RT-EGAN-IPS thrives on a collaborative approach. Security experts collaborate with the system for real-time validation and feedback. Dynamic thresholding ensures responsiveness to evolving threats, supported by low-latency responses and an ensemble approach. This abstract unveils RT-EGAN-IPS, a holistic AI-driven network security paradigm, promising a new level of protection in the ever-changing realm of cyber threats. © 2024 IEEE.

关键词： Network intrusion

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Securing the Metaverse: A Deep Reinforcement Learning and Generative Adversarial Network Approach to Intrusion Detection

Securing the Metaverse: A Deep Reinforcement Learning and Ge...

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IEEE International Conference on Communications Workshops, ICC

作者： Tarek Ali Mohammed Al-Khalidi Rabab Al-Zaidi Amna Eleyan Muhammad Atif Ur Rehman Department of Computing and Mathematics Manchester Metropolitan University Manchester UK School of Computing Science and Engineering University of Salford Manchester UK

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

This paper explores the pivotal domain of security within the Metaverse and proposes an innovative method to tackle the distinctive challenges it presents. The proposed solution leverages Generative Adversarial Networks (GANs) to generate synthetic data, and Deep Reinforcement Learning (DRL) as a targeted model. The proposed Intrusion Detection System (IDS) effectively navigates the intricate Metaverse environment. Incorporating GANs guarantees the production of diverse synthetic data, thereby mitigating concerns linked to class imbalance. Moreover, DRL empowers the IDS to differentiate between customary and unusual user actions. According to our research findings, our approach surpasses competitors, particularly when faced with synthetic or augmented data, considering virtual space, user interactions, and network activities. This serves as evidence that our model possesses the capability to enhance intrusion detection in the dynamic Metaverse environment.

关键词： Metaverse Conferences Intrusion detection Production Generative adversarial networks Deep reinforcement learning Robustness

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Spatio-Temporal Road Traffic Prediction using Real-time Regional Knowledge

arXiv

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arXiv 2024年

作者： Han, Sumin An, Jisun Lee, Dongman School of Computing Korea Advanced Institute of Science and Technology Korea Republic of Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

For traffic prediction in transportation services such as carsharing and ride-hailing, mid-term road traffic prediction (within a few hours) is considered essential. However, the existing road-level traffic prediction has mainly studied how significantly micro traffic events propagate to the adjacent roads in terms of short-term prediction. On the other hand, recent attempts have been made to incorporate regional knowledge such as POIs, road characteristics, and real-time social events to help traffic prediction. However, these studies lack in understandings of different modalities of road-level and region-level spatio-temporal correlations and how to combine such knowledge. This paper proposes a novel method that embeds real-time region-level knowledge using POIs, satellite images, and real-time LTE access traces via a regional spatio-temporal module that consists of dynamic convolution and temporal attention, and conducts bipartite spatial transform attention to convert into road-level knowledge. Then the model ingests this embedded knowledge into a roadlevel attention-based prediction model. Experimental results on real-world road traffic prediction show that our model outperforms the baselines. © 2024, CC BY.

关键词： Prediction models

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Chaotic Grey Wolf Optimization for Energy-Efficient Clustering and Routing in Wireless Sensor Networks 2

Chaotic Grey Wolf Optimization for Energy-Efficient Clusteri...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Almusawi, Muntather Ravindran, Gobinath Parameshachari, B.D. Bhasker, B. Lavanya, N.L. The Islamic University Najaf Iraq SR University Department of Civil Engineering Telangana Warangal India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India REVA University School of Computing and Information Technology Bengaluru India Sapthagiri College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350317558

Wireless Sensor Networks (WSNs), the imperative challenge of energy efficiency takes center stage. This paper delves into inventive methodologies to combat energy depletion in sensor nodes, introducing the Chaotic Grey Wolf Optimization Algorithm (CGWOA) for optimal Cluster Head (CH) selection in WSNs. An evolution strategy and a mutation operation operator, CGWOA aims to amplify energy efficiency, scalability, and minimize network overhead through streamlined clustering. The outcomes underscore CGWOA's potential as a solution for advancing energy-efficient clustering and routing in WSNs, particularly in optimizing CH selection. The ensuing numerical validation underscores CGWOA's revealing an energy consumption of 1100 J, a throughput ratio of 69,785 kbps, 4900 active nodes, and a network lifetime extension to 8021. This comparative analysis shows the emphasis on problems to overcome existing methods like Particle Swarm Optimization for Energy-Efficient Clustering (PSO-EEC), Adaptive Hybrid Clustering Scheme with Grey Wolf Optimization (AHCS-GWO), Hybrid Optimization for Cluster Head Selection (HOCK). © 2024 IEEE.

关键词： Cluster head Clustering Energy consumption Network Lifetime Wireless senor network

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Detection of Paddy Crops Diseases and Early Diagnosis Using Faster Regional Convolutional Neural Networks

Detection of Paddy Crops Diseases and Early Diagnosis Using ...

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2021 International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2021

作者： Anandhan, K.A. Singh, Ajay Shanker School of Computing Science Engineering Galgotias University India

ISBN: (纸本)9781728177410

Nowadays the grain food demand is increasing due to population increase. Rice is one of the essential foods for half of the world's population. In India most of the farmer struggled to identify paddy leaf disease at a premature stage. The deep learning models were used to detect a rice blast disease such as Brown spot, Sheath blight, Blast and Leaf streak disease at early stage, so we can control disease spread over all plant to increase the rice production. The proposed system is applied to identify a various rice plant leaf images disease detection using mask R-CNN, Faster R-CNN algorithms. We have studied five different paddy diseases and applied in southern region (India) to improve the paddy quantity. We collected a paddy leaf images through a Sony Camera Mobile device (Vivo V9) and conducted experiments to train the model using 1500 images. Our experimental results analysis mask R-CNN is best suitable for to detect and identify the various rice blast disease such as Blast-96%, Brown spot-95%, and Sheath blight-94.5%. The results implies capable of disease identification from real-time capture leaf images and improve the accuracy applying different areas to control the spread rice blast disease. © 2021 IEEE.

关键词： Convolutional neural networks

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Fuel Type Forecasting Based on CO2 Emissions: Aharris Hawk's Optimization Based Machine Learningapproach

SSRN

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SSRN 2023年

作者： Thakur, Divya Balabantaray, Sandeep Mishra, Rupal Rastogi, Akshat Gupta, Shubh Bikhani, Meet School of Computing Science and Engineering VIT Bhopal University India

The goal of this research is to determine a vehicle’s fuel type depending on the kind of vehicle, its attributes, and its CO2 emissions. The Canadian government gathered data for this experiment over a 7-year period, including information on the maker, model, gearbox type, fuel type, and fuel usage of numerous automobiles across various criteria. This research seeks to determine the most efficient fuel by comparing the fuels that are included in the dataset. Several machine learning model namely Adaboost Classifier, Multinomial NB, Gaussian NB, Logistic Regression were used to assess the accuracy of the dataset. The study shows that the Gaussian NB model can estimate the fuel type of a vehicle with an accuracy of up to 66% based on CO2 emissions. In this research, we have used HHO optimisation to all the aforementioned machine learning models to further improve the model’s accuracy. Utilising a cutting-edge method known as HHO optimisation, machine learning performance was enhanced. Each machine learning model stated in the abstract also includes ROC curves and confusion matrices. The findings show that the accuracy of the Logistic Regression model has increased considerably by 22% due to HHO optimisation. As a result, the model’s total accuracy is currently 87% allowing it to achieve the highest accuracy. These results demonstrate the intriguing potential of HHO optimisation as a method of improving the results and execution of machine learning models. © 2023, The Authors. All rights reserved.

关键词： Carbon

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Covid-19 Detection from Chest X-Ray Images Using Advanced Deep Learning Techniques

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Computers, Materials & Continua 2022年第1期70卷 1541-1556页

作者： Shubham Mahajan Akshay Raina Mohamed Abouhawwash Xiao-Zhi Gao Amit Kant Pandit School of Electronics&Communication Engineering Shri Mata Vaishno Devi UniversityKatra182320India School of Electrical Engineering Shri Mata Vaishno Devi UniversityKatra182320India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA School of Computing University of Eastern FinlandKuopio70210Finland

Like the Covid-19 pandemic,smallpox virus infection broke out in the last century,wherein 500 million deaths were reported along with enormous economic *** unlike smallpox,the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement inmedical aid and *** analytics,machine learning,and automation techniques can help in early diagnostics and supporting treatments of many reported *** paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning *** study suggests that using the Prediction and Deconvolutional Modules in combination with the SSD architecture can improve the performance of the model trained at this *** used a publicly open CXR image dataset and implemented the detectionmodelwith task-specific pre-processing and near 80:20 *** achieved a competitive specificity of 0.9474 and a sensibility/accuracy of 0.9597,which shall help better decision-making for various aspects of identification and treat the infection.

关键词： Machine learning deep learning object detection chest X-ray medical images Covid-19

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Enhancing Android Security: Dynamic Analysis for Resilient Defences Using Machine Learning 7

Enhancing Android Security: Dynamic Analysis for Resilient D...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Abhishek, S. Rajendran, Adithya Sha, Akhbar Anjali, T. Nair, Arun Karunakaran Amrita School of Computing Department of Computer Science and Engineering Amritapuri India University of Greenwich Old Navel College Department of Computer Science LondonSE10 9LS United Kingdom

ISBN: (纸本)9798350340600

In recent years, the widespread popularity of Android as a mobile operating system has been accompanied by an abundance of mobile applications, enriching the user experience. However, this surge in Android app availability has also ushered in a heightened risk of encountering malicious Android applications, commonly known as malware, particularly when downloading from third-party sources. The formidable challenge lies in the efficient and precise detection of such malware. In this research, a unique virus detection approach optimized for the Android platform is studied, harnessing the potential of dynamic analysis. Through rigorous experimentation, the performances are evaluated of the proposed scheme, showcases its effectiveness and efficiency in identifying malicious Android applications. Notably, a diverse set of machine learning models are employed and their respective accuracies are calculated, these models include K-Nearest Neighbors (KNN) with 0.8825%, Naive Bayes with 0.4409%, Gradient Boosting with 0.8524%, Random Forest with 0.9122%, XGBoost with 0.8907%, LightGBM with 89.07% and Bagging 0.9040%, with Random Forest giving the best accuracy and Naive Bayes the least. the study not only delves into the intricacies of dynamic analysis but also highlights the accuracy scores achieved by these prominent machine learning models, providing valuable insights for the field of Android malware detection. © 2023 IEEE.

关键词： Android malware

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