Magnetic Force Classifier: A Novel Method for Big Data Classification

Ali, Hasan; Hassanat, Ahmad B. A.; Tarawneh, Ahmad S.; Alrashidi, Malek; Alghamdi, Mansoor

doi:10.1109/access.2022.3142888

Cited by 11 publications

(4 citation statements)

References 131 publications

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“…We run our experiments on Google Colab, which is a Python-based programming environment [41]. Professional packages are used, for example, TensorFlow is used to build LSTM deep model, Librosa is used to read and process the recordings, Sklearn is used to split the dataset into training and validation sets, and Matplotlib is used for graphs and visualizing the results.…”

Section: Experiments Design and Datasetmentioning

confidence: 99%

Recognition of Arabic Accents From English Spoken Speech Using Deep Learning Approach

Habbash,

Mnasri,

Alghamdi

et al. 2024

IEEE Access

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Accents, or changes in how different people speak the same word/sentence in the same language, pose substantial communication issues in most spoken languages. This is a well-known fact, but how does the accent of one language affect learning/speaking another? In this paper, we look at how Arab accents influence the English language. To that end, we built a deep machine-learning system for Arabic accent recognition that was learned from an in-house English speech database of four Arabic accents collected from Jordan, Iraq, Saudi Arabia, and Tunisia. The proposed system employs Mel spectrograms of an English-spoken paragraph to train an LSTM neural network to recognize the accent in each sound signal. Although the collected data was extremely difficult to learn due to the presence of both males and females and fluent speakers in each class, the proposed system could recognize speakers with various accents by up to 79%. This answers the study's main question, demonstrating that speakers with an Arabic accent have their way of speaking English, which varies by country. As a result, if trained on appropriate and adequate data, the proposed system can also be used to recognize accents in any language.

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Section: Experiments Design and Datasetmentioning

confidence: 99%

Recognition of Arabic Accents From English Spoken Speech Using Deep Learning Approach

Habbash,

Mnasri,

Alghamdi

et al. 2024

IEEE Access

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“…Hassanat and co-workers [39] proposed an innovative rapid classification approach based on the well-known magnetic force (MF). This classifier calculates the magnetic force at each discrete point in the feature space based on the number of points belonging to a certain class/magnet.…”

Section: Literature Reviewmentioning

confidence: 99%

CTELC: A Constant-Time Ensemble Learning Classifier Based on KNN for Big Data

Tarawneh,

Alamri,

Al-Saedi

et al. 2023

IEEE Access

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Big data classification is a challenging task because most known classification methods need a long time and a lot of processing resources to execute such a task and use the vast amount of available data. In this paper, we propose a novel big data classification method that leverages the power of the KNN classifier and the efficiency of the ensemble learning technique to create a new method capable of performing classification tasks on big data efficiently. The proposed method picks tiny data chunks at random from a big dataset, with each chunk including random examples of a small number of randomly selected features. A weak KNN classifier is employed on each data chunk to perform classification on new (unseen) data, and the majority voting rule is used to reach the final classification decision based on the outcomes of the weak classifiers. The proposed method has a constant classification time, according to the time complexity analysis. Furthermore, the proposed method was found to be more efficient on a single node than existing methods, some of which run on a large cluster of nodes. Because of its speed and enhanced performance, the proposed method can be considered an ideal classifier for handling complex data types such as Geospatial data, Big trajectory data, and Big Data in general. INDEX TERMSBig data; Geospatial data; Trajectory data; Classification; Ensemble learning; KNN I. INTRODUCTIONFor decades, researchers have been studying and evaluating big data methodologies and tools. This interest stems from the massive volume of data exchanged and saved by social media users, medical organizations, educational institutions, and others.According to statistical reports, the number of users on different social media platforms has reached more than 2 billion [1]. WhatsApp, for example, has over 600 million users, more than half a billion photos, and one hundred million videos transferred and shared between users on a daily basis.[2]. Also, due to the huge advance in smartphone technology, it has become easier for users to share text\images and write posts on such social media platforms. Some reports show that the number of posts on Twitter in 2007 was 5K, this number became around 500 million after about 6 years, in 2013 [3], which indicates the massive amount of available data on social media in general. This amount of data is not restricted to social media, as many other platforms generate and store huge data volumes [4,5,6,7].This amount of data needs to be processed and analyzed in order to use it for building useful knowledge discovery and machine learning big data-based applications, like facial big data applications [8,9,10], signal big data [11] and various industry big data-based applications [12,13,14].Volume (big), Variety, and Velocity are among the most distinguishing characteristics of Big data, and as a result, it is attractive to have an efficient classification/prediction system to learn from such Big data. Such applications include, but are not limited to, medical [15,16], financial [17,18,19]...

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“…The study focused on several off-the-shelf classifiers, although different algorithms or ensemble approaches could produce better results [61][62][63][64]. Exploring and comparing different classifier alternatives could provide useful insights into the most successful ways for coffee classification.…”

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confidence: 99%

Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

et al. 2023

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This study investigates the classification of Arabic coffee into three major variations (light, medium, and dark) using simulated data gathered from the actual measurements of color information, antioxidant laboratory testing, and chemical composition tests. The goal is to overcome the restrictions of limited real-world data availability and the high costs involved with laboratory testing. The Monte Carlo approach is used to generate new samples for each type of Arabic coffee using the mean values and standard deviations of publicly available data. Using these simulated data, multiple machine-learning algorithms are used to classify Arabic coffee, while also investigating the importance of features in identifying the key chemical components. The findings emphasize the importance of color information in accurately recognizing Arabic coffee types. However, depending purely on antioxidant information results in poor classification accuracy due to increased data complexity and classifier variability. The chemical composition information, on the other hand, has exceptional discriminatory power, allowing faultless classification on its own. Notably, particular characteristics like crude protein and crude fiber show high relationships and play an important role in coffee type classification. Based on these findings, it is suggested that a mobile application be developed that uses image recognition to examine coffee color while also providing chemical composition information. End users, especially consumers, would be able to make informed judgments regarding their coffee preferences.

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Magnetic Force Classifier: A Novel Method for Big Data Classification

Cited by 11 publications

References 131 publications

Recognition of Arabic Accents From English Spoken Speech Using Deep Learning Approach

Recognition of Arabic Accents From English Spoken Speech Using Deep Learning Approach

CTELC: A Constant-Time Ensemble Learning Classifier Based on KNN for Big Data

Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

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