Participants Ranking Algorithm for Crowdsensing in Mobile Communication

Ali, Tariq; Noureen, Javeria; Draz, Umar; Shaf, Ahmad; Yasin, Sana; Ayaz, Muhammad

doi:10.4108/eai.13-4-2018.154476

Cited by 13 publications

(8 citation statements)

References 7 publications

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“…STING is a Density-based clustering algorithm and it supports the only spatial type of datasets. In addition, most of our previous work in which we adopt similar approaches to machine learning has been successfully simulated for different aspects like artificial intelligence [18], participant selection and ranking algorithms [19,20] and neural networks [21] provide support our words.…”

Section: F Cactusmentioning

confidence: 77%

Systematic Review: A State of Art ML Based Clustering Algorithms for Data Mining

Ali

Faheem

Waseem

et al. 2020

2020 IEEE 23rd International Multitopic Conference (INMIC)

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Data mining is an unsupervised learning technique to extract the insights and hidden relationships among data. Data mining has more importance in data science and machine learning because through data mining all hidden information is shown to determine various aspects of the data set. Clustering is a data mining technique to group the data, on the basis of similarity measures. The objects or data points in a cluster are similar. Similarly, objects or data points in another cluster will also be similar. But when these clusters are compared, they are dissimilar to each other. Clustering is considered the most important unsupervised learning technique because it deals with finding a structure in a collection of unlabeled data. Clustering can be done by the different approaches like partitioning clustering, hierarchical clustering, density-based clustering, and grid-based clustering. These clustering approaches can be done by the numbers of algorithms, such as K-means clustering, Fuzzy C-means clustering, Hierarchical clustering, DBSCAN, OPTICS, STING, ROCK and CACTUS. This proposed paper contained reviews of the above techniques by using a more powerful programming language (Python) as a tool. Evidence attainable from this study is helpful for researchers to select an appropriate clustering approach based on their domain.

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Section: F Cactusmentioning

confidence: 77%

Systematic Review: A State of Art ML Based Clustering Algorithms for Data Mining

Ali

Faheem

Waseem

et al. 2020

2020 IEEE 23rd International Multitopic Conference (INMIC)

Self Cite

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“…The observations that exist in node "t" is to class "s" as the mainstream class exists in node "t". The maximum deviance reduction was estimated using Equation (10). The deviance reduction splitting criterion was differentiable and easy to use for the optimization of numerical data, meaning it gives better accuracy as compared to other tested classifiers.…”

Section: Resultsmentioning

confidence: 99%

“…O ts logO ts (10) class results of the proposed methods with different classifier variants are shown in The overall accuracy of each classifier on the Corel-1000, Corel-1500, and Corel-5k datasets is shown in Figure 4. Further, feature selection is very important in terms of predicting the accuracy of models with outliers.…”

Section: Resultsmentioning

confidence: 99%

“…The merger of more than one feature is also used to improve the CBIR and facial expression system performance [4]. The extraction of the texture feature in the spatial domain can be performed through the Local Binary Pattern (LBP) [5], Completed Volume Local Binary Pattern (CVLBP) [6], Volume Local Binary Pattern (VLBP) [7], Noise Invariant Structure Pattern (NISP) [8], Local Ternary Pattern (LTP) [9], Scale Invariant Feature Transform (SIFT), and Speeded-up Robust Features (SURF), among others algorithm which are utilizing it in different case study [10].…”

Section: Introductionmentioning

confidence: 99%

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Integration of Discrete Wavelet Transform, DBSCAN, and Classifiers for Efficient Content Based Image Retrieval

et al. 2020

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In the domain of computer vision, the efficient representation of an image feature vector for the retrieval of images remains a significant problem. Extensive research has been undertaken on Content-Based Image Retrieval (CBIR) using various descriptors, and machine learning algorithms with certain descriptors have significantly improved the performance of these systems. In this proposed research, a new scheme for CBIR was implemented to address the semantic gap issue and to form an efficient feature vector. This technique was based on the histogram formation of query and dataset images. The auto-correlogram of the images was computed w.r.t RGB format, followed by a moment’s extraction. To form efficient feature vectors, Discrete Wavelet Transform (DWT) in a multi-resolution framework was applied. A codebook was formed using a density-based clustering approach known as Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The similarity index was computed using the Euclidean distance between the feature vector of the query image and the dataset images. Different classifiers, like Support Vector (SVM), K-Nearest Neighbor (KNN), and Decision Tree, were used for the classification of images. The set experiment was performed on three publicly available datasets, and the performance of the proposed framework was compared with another state of the proposed frameworks which have had a positive performance in terms of accuracy.

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“…The gas levels obtained in a particular time slot are time-critical and time-sensitive, so the manual extraction of the features increases the complexity of the task. Time-series data analysis is a complicated task that also affects the target selection of the features, resulting in the degraded performance of a model [43][44][45].…”

Section: Deep Learning Modelmentioning

confidence: 99%

Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

et al. 2020

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Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

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Participants Ranking Algorithm for Crowdsensing in Mobile Communication

Cited by 13 publications

References 7 publications

Systematic Review: A State of Art ML Based Clustering Algorithms for Data Mining

Systematic Review: A State of Art ML Based Clustering Algorithms for Data Mining

Integration of Discrete Wavelet Transform, DBSCAN, and Classifiers for Efficient Content Based Image Retrieval

Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

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