作者：
Gote, Pradnyawant M.Kumar, PraveenVerma, PrateekYesankar, PrajyotPawar, AdeshSaratkar, Saniya
Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India
Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India
Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India
Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India
The swift progression of wireless communication technologies-specifically from 5G to 6G is an approach that could be the most significant revolutionary leap towards changing connectivity and data transmission forever....
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Vehicular networks focus on improving traffic and safety concerns in an Intelligent Transportation System (ITS). However, the presence of misbehaving nodes can drastically impact the safety, traffic and other applicat...
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The ubiquitous availability of heterogeneous sensor data created by Internet-of-Things (IoT) technologies and Industry 4.0 trends drastically accelerated the development of machinelearning applications. AutoML servic...
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The ubiquitous availability of heterogeneous sensor data created by Internet-of-Things (IoT) technologies and Industry 4.0 trends drastically accelerated the development of machinelearning applications. AutoML services enable users with sparse machinelearning knowledge to develop AI-based applications and rapidly evaluate the feasibility of data-driven ideas. Therefore, there exists a demand for holistic, low-code, end-to-end AutoML systems, which cover all stages of the machinelearning lifecycle (i.e., feature engineering, model training, evaluation, versioning, provisioning, etc.). Although there are proprietary, cost-intensive platforms addressing these issues, no open-source solutions covering these aspects are known to us. In this paper we present AutoTiM, an open-source service capable of creating and operating highly performant machinelearning models without requiring domain expertise or machinelearning knowledge.
The emergence of Deep learning (DL) was a response to the widespread availability of mobile data and the growing complexity of wireless networks. It is a subfield of machinelearning that has been proposed in academic...
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Early disease prediction is vital for improving healthcare quality and preventing patients from developing critical health issues. This research introduces a Hypertension Prediction Model (HPM) that uses individual cl...
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With the growing reliance on the Internet and today's interconnected world, cyber threats have been tremendously increased. Thus, Internet is vulnerable to a wide range of attacks that risk its data. Ensuring the ...
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ISBN:
(纸本)9798350343427;9798350343434
With the growing reliance on the Internet and today's interconnected world, cyber threats have been tremendously increased. Thus, Internet is vulnerable to a wide range of attacks that risk its data. Ensuring the security and integrity of information transmitted and stored across networks has become crucial objective. Intrusion Detection systems (IDS) play a critical role in detecting security breaches within computer networks and mitigating the potential loss of highly valuable data. Recently, Software-Defined Networks (SDN) have revolutionized the traditional networking paradigm by decoupling the control plane from the data plane, thereby enhanced network management and agility. This paper provides a literature survey on SDN-based IDSs, their classifications, and their different implementations using recent machine and deep learning techniques. Exploiting machine and deep learning in IDS contributed significantly in IDSs detection accuracy as well as focused on a new area of data security, including the adopted learning models, attributes, and learning parameters. Not only the detection accuracy impact, but also optimizing and accelerating inference time is a common goal in the deployment of IDSs while implementing machine and deep learning techniques. This paper aims to create awareness and understanding of the importance of implementing robust security measures to protect sensitive information and ensure the integrity and availability of network resources.
The role of technology is vital and can be observed through the 5th industrial revolution. As a matter of fact, the impact is so severe, it can be felt almost everywhere. As technology advances, one of the most promis...
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Technology in the field of healthcare has been in constant enhancement, incorporating machinelearning and various other technologies for clinical decision support systems or computer-aided healthcare systems that ass...
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Designing an Intrusion Detection System (IDS) for attack detection in IoT networks is done by applying different machinelearning and deep learning approaches. Standard CIE-CICIIDS2018 is a dataset with enormous benig...
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The increasing frequency and severity of plant diseases pose significant threats to global food security and agricultural sustainability. This research paper addresses the urgent need for an integrated framework that ...
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