A Generative Adversarial Network Model Based on Intelligent Data Analytics for Music Emotion Recognition under IoT

Summary Globally, the water‐level fluctuations in lakes are a dynamic and complex process. The fluctuation is characterized by higher non‐linearity and stochasticity, making it quite hard to forecast for future planning. However, the advent of machine learning algorithms in recent decades provides significant improvement in forecasting such fluctuations. This work provides a systematic review of the machine learning algorithms (ML) used for characterizing lake‐water level dynamics. Among those ML algorithms, the paper reviews seven distinct as well as popular algorithms namely, neural network model, support vector machines, extreme learning machine, artificial neuro‐fuzzy inference systems, evolutionary and hybrid algorithms, and deep learning models. The sample inputs, splitting of data, performance metrics, and its comparison are discussed with future scope. Moreover, the strength and limitations of those algorithms are also examined. Finally, the review in this paper gives a newer vision for water resource planners and hydrologists for sustainable lake management.

Section: Conclusion and Future Directionmentioning

confidence: 99%

Section: Machine Learning Algorithms Used To Forecast Lake Water‐leve...mentioning

confidence: 99%

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Future Directions In Monitoring Fluctuations Of Lake Water-l...mentioning

confidence: 99%

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A systematic review on machine learning algorithms used for forecasting lake‐water level fluctuations

Chakravarthy¹,

Bharanidharan

Rajaguru³

2022

“…15 The significant differences among the wrapper and filter technique regarding feature selection are wrapper technique computes the benefits of feature subset by training the mode; whereas the filter technique estimates the significance of features and the filtration process is evaluated before the classification process due to the independent utilization of classification techniques. [16][17][18][19][20] Numerous techniques [21][22][23][24][25][26] are employed to classify the data gathered from SIoT that provide several advantages of addressing nonlinear data and recognizing the patterns of high dimensional data. Numerous machine learning techniques are employed to classify the data gathered from SIoT in a more powerful way, but due to misclassification results, those techniques were ineffective.…”

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confidence: 99%

A novel deep neural network based marine predator model for effective classification of big data from social Internet of Things

Shaji

Singh

Nisha

2022

Social Internet of Things (SIoT) is considered as one of the most recently evolved topics that connects people and object, object and object as well as people and people. The SIoT and big data provide an exact representation of IoT and social system for human progression characterization. Numerous machine learning techniques are employed to classify the data gathered from SIoT in a more powerful way. In this article, a deep neural network based marine predator (DRNN-MP) algorithm is proposed in classifying big data. Here, an adaptive Savitzky-Golay filter is employed for selecting the subset and to eliminate undesirable data, as well as different noises. The big data databases are reduced using a Hadoop map reducing framework thereby enhancing the performances of the proposed approach. In addition to this, a modified relief technique is employed to select optimal features thereby performing better classification. The testing and training process based on the proposed approach for optimal classification of features from the big data considers four different databases namely coronary illness, GPS trajectories, localization data, and water treatment plant obtained from UCI machine learning repository. In addition to this, the proposed approach is evaluated for diverse performance measures namely accuracy, precision, specificity, sensitivity, throughput, and energy consumption. Finally, the proposed approach is compared for various metrics to illustrate the effectiveness of the system and the results demonstrated that the accuracy of our work is 98.25%. K E Y W O R D Sbig data, database, deep recurrent neural network, marine predator, modified relief, social internet of things INTRODUCTIONInternet of things (IoT) illustrates a separate universe of heterogeneous objects namely actuators, sensors as well as smartphones in which each object and everything possesses a sovereign identity. 1 Nowadays, developing an IoT enabled technology and providing solutions are considered as tedious tasks. On the other hand, IoT deals with sharing and pervasive collection of data regarding a common intention. In IoT, various attribute factors namely integer value and variable define the data thereby permitting specific functions to be performed using a predetermined interface. 2Numerous research scholars are fascinated peculiarly in determining the risk problems emerging while integrating and discovering the data withinIoT. In recent years, the social Internet of Things (SIoT) has been established which is another utilization of IoT. 3 They constitute various distinctive characteristics including diverse communication protocols, operating systems, related standards, and various operational platforms; however, all such differences are neglected. 4

A novel robust convolutional neural network for uniform resource locator classification from the view of cyber security

Kabakuş

2022

Summary Uniform resource locator (URL)‐based cyber‐attacks form a major part of security threats in cyberspace. Even though the experience and awareness of the end‐users help them protect themselves from these attacks, a software‐based solution is necessary for comprehensive protection. To this end, a novel robust URL classification model based on convolutional neural network is proposed in this study. The proposed model classifies given URLs into five classes, namely, (normali$$ \mathrm{i} $$) benign$$ \mathrm{benign} $$, (normalii$$ \mathrm{ii} $$) defacement$$ \mathrm{defacement} $$, (normaliii$$ \mathrm{iii} $$) phishing$$ \mathrm{phishing} $$, (normaliv$$ \mathrm{iv} $$) spam$$ \mathrm{spam} $$, and (normalv$$ \mathrm{v} $$) malware$$ \mathrm{malware} $$. The proposed model was trained and evaluated on a gold standard URL dataset comprising of 36,707$$ \mathrm{36,707} $$ samples. According to the experimental result, the proposed model obtained an accuracy as high as 98.1%$$ 98.1\% $$ which outperformed the state‐of‐the‐art. Based on the same architecture, we proposed another classifier, a binary classifier that detects malicious URLs without dealing with their types. This binary classifier obtained an accuracy as high as 99.3%$$ 99.3\% $$ which outperformed the state‐of‐the‐art as well. The experimental result demonstrates the feasibility of the proposed solution.