An Adaptive Neuro-Fuzzy Inference System and Black Widow Optimization Approach for Optimal Resource Utilization and Task Scheduling in a Cloud Environment

Nanjappan, Manikandan; Natesan, Gobalakrishnan; Pradeep, K.

doi:10.1007/s11277-021-08744-1

Cited by 41 publications

(21 citation statements)

References 37 publications

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“…Healthcare organizations are developing a myriad of applications to handle all of these capabilities while also catering to the needs of their users' environments. Due to more advancement in technologies, there is generally a focus towards the process of expert medical systems to be adopted that supports diagnosis and treatment (Srinivasan andMadheswari 2017, 2018;Baulkani 2017, 2019a, b;Nanjappan and Albert 2019;Nanjappan et al 2021).…”

Section: Introductionmentioning

confidence: 99%

An ensemble approach for healthcare application and diagnosis using natural language processing

Alekhya¹,

Sasikumar²

2022

Cogn Neurodyn

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Integration of healthcare records into a single application is still a challenging process There are additional issues when data becomes heterogeneous, and its application based on users does not appear to be the same. Hence, we propose an application called MEDSHARE which is a web-based application that integrates the data from various sources and helps the patient to access all their health records in a single point of source. Apart just from the collection of data, this portal enables the process of diagnosis using Natural language processing. The process is carried out by fuzzy logic ruleset which is generated by using NLP packages. The resulted information is given to the SVM classifier which helps in the prediction of diseases resulting in 89% of accuracy and standing the best compared to other classifiers. Finally, the observations resulted are sent to the front end application and the concerned user mobile through text message in their own native language for which translation package is been used.

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Section: Introductionmentioning

confidence: 99%

An ensemble approach for healthcare application and diagnosis using natural language processing

Alekhya¹,

Sasikumar²

2022

Cogn Neurodyn

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“…Digital forensics is a collection of procedures developed by scientific specialists to acquire traces from a crime scene, analyze them, and present the evidence discovered to aid in identifying the malicious actors, their motivation, and the method employed 7 . Digital forensics has been divided into specialties in recent years as a result of technical developments, such as network forensics, cloud forensics, mobile forensics, IoT forensics, data forensics, and memory forensics 8–15 …”

Section: Introductionmentioning

confidence: 99%

“…7 Digital forensics has been divided into specialties in recent years as a result of technical developments, such as network forensics, cloud forensics, mobile forensics, IoT forensics, data forensics, and memory forensics. [8][9][10][11][12][13][14][15] There has been countless research into DL methods; however, just a few studies that have played a significant impact are mentioned here.…”

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confidence: 99%

IoT based cyber forensics in big data optimization and privacy using deep neural anomaly detection with Hadoop clustering and convolution based Adam optimizer

Venugopal¹,

Rengaswamy

Sathianesan

2022

Concurrency and Computation

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The reliability of the digital forensic system mainly lies in its ability to detect the attack on time and minimize the security risk associated with the Internet of Things (IoT) device. The existing machine learning architectures mainly suffered from different complexities while processing the attack features such as increased processing time, high manual intervention, complexity to handle large datasets, and low prediction accuracy. To overcome these issues, this article proposes a novel deep learning architecture for data forensics. The low generalization capability of the neural network is handled via the Hadoop clustering and convolution‐based ADAM (CADAM) optimizer approach for improving the optimization capability of the model. In this way, the privacy of the model is improved. The efficiency of the proposed CADAM optimized deep neural network architecture is evaluated by using the UNSW‐NB15 and Bot‐IoT datasets. The proposed model offers improved performance in terms of runtime, precision, sensitivity, specificity, latency, cost, ROCAUC curve, and anomalous traffic when compared with the existing techniques. From the experiments carried out, we can conclude that the proposed framework revealed higher performance to discover and prevent cyber‐attacks against other conventional approaches.

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“…Artificial neural network, support vector machines, and deep learning are the widely used techniques for intrusion detection. [15][16][17] An IDS 18,19 mainly secures the system and alerts you in case of any malicious entry. There are two types of IDS: host-based IDS (HIDS) and network-based IDS (NIDS).…”

Section: Introductionmentioning

confidence: 99%

“…Anomaly detection mainly identifies malicious behavior by comparing the anomalous behavior with the normal data. Artificial neural network, support vector machines, and deep learning are the widely used techniques for intrusion detection 15–17 …”

Section: Introductionmentioning

confidence: 99%

Malicious behavior detection in cloud using self‐optimized dynamic kernel convolutional neural network

Alajlan

Almasri

2022

Trans Emerging Tel Tech

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Cloud computing is one example of a technological revolution that has drastically altered the way traditional commercial operations are conducted. Because cloud platforms are fundamentally revolutionizing the area of computing, they must provide safe transmission and storage of user data by assuring the confidentiality, integrity, and availability of the data. As a result, establishing a secure cloud computing framework is crucial, and security cannot only rely on the user's credentials being verified. To overcome this problem, this article presents a multi‐population genetic algorithm optimized dynamic kernel convolutional neural network to analyze the user's behavior to identify their malicious intent. This article describes a two‐stage malicious detection and prediction scheme. The proposed system's efficiency is measured by its ability to discriminate between nine different types of attacks in the UNSW‐NB15 dataset (abnormal behavior exhibited by users). The average true positive rate, false positive rate, precision, and F‐measure for the nine attack classes were 99.22%, 99.11%, 99.11%, and 99.22%, respectively, when evaluated using the UNSW‐NB15 dataset. The experimental results demonstrate that this technique is effective in detecting malicious behaviors in the cloud environment with higher accuracy.

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An Adaptive Neuro-Fuzzy Inference System and Black Widow Optimization Approach for Optimal Resource Utilization and Task Scheduling in a Cloud Environment

Cited by 41 publications

References 37 publications

An ensemble approach for healthcare application and diagnosis using natural language processing

An ensemble approach for healthcare application and diagnosis using natural language processing

IoT based cyber forensics in big data optimization and privacy using deep neural anomaly detection with Hadoop clustering and convolution based Adam optimizer

Malicious behavior detection in cloud using self‐optimized dynamic kernel convolutional neural network

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