SPY-BOT: Machine learning-enabled post filtering for Social Network-Integrated Industrial Internet of Things

Rahman, Arafatur; Zaman, Nafees; Asyhari, A. Taufiq; Sadat, S. M. Nazmus; Pillai, Prashant; Arshah, Ruzaini Abdullah

doi:10.1016/j.adhoc.2021.102588

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…We use support vector machine (SVM) [27] algorithm to develop the predictive model. According to the No Free Lunch theorem, there is no incentive to choose one machine learning algorithm over another until assumptions are made about the dataset [28]. This is related to inductive bias.…”

Section: A Pre-flood Stepsmentioning

confidence: 99%

A Data-Intelligent Scheme Toward Smart Rescue and Micro-Services

et al. 2023

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A considerable portion of the world frequently experiences flooding during the monsoon season. As a result of this catastrophic event, hundreds of individuals have become homeless. In addition, rescuers are not usually effective enough to rescue the majority of victims. This is due to inadequate rescue operations infrastructure, a severe flaw in today's technologically advanced society. This manuscript proposes a microservice-dependent secure rescue framework that uses geographic information system mapping with a K-Means clustering algorithm to identify flood-prone regions. Numerous microservices, such as fleet management, cloud computing, and data security, integrate and execute the framework in preand post-flood situations. Labeling data from the proposed framework generates a support vector machinebased classifier for predicting flood risk. Furthermore, a hybrid A* algorithm is developed to find an optimal route for the rescue operation. Based on the K-means clustering results, which reduced the variance by 89.2 percent overall, dividing the data into six clusters was the best option for this study. The smoothness of the suggested hybrid algorithm is also used to verify its superiority.

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Section: A Pre-flood Stepsmentioning

confidence: 99%

A Data-Intelligent Scheme Toward Smart Rescue and Micro-Services

et al. 2023

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“…Cyberbullying activities encompass a range of actions, including flame, harassment, cyberstalking, defamation, exclusion, trolling, impersonation, and dishonesty [11]. However, detecting verbal or written expressions that incite bullying or hatred can be challenging due to variations in accents and languages, necessitating onelanguage methodologies and manual data collection [12].…”

Section: Introductionmentioning

confidence: 99%

Utilizing K-Means Clustering for the Detection of Cyberbullying Within Instagram Comments

Muhariya,

Riadi,

Prayudi

et al. 2023

ISI

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With the proliferation of social media platforms like Instagram, Twitter, and Facebook, the dissemination of information has undergone a significant transformation. Instagram, distinguished by its emphasis on visual media, has emerged as a platform of choice for photo and video sharing. Despite its popularity, the platform's vast reach renders it vulnerable to malevolent activities, including cyberbullying. While prior research has employed SVM, NBC, C45, and K-Nearest Neighbors for cyberbullying analysis, these studies predominantly focused on Twitter. This paper presents a novel approach, harnessing the power of K-means Clustering to identify instances of cyberbullying on Instagram. In this study, a labelled dataset is gathered and subjected to pre-processing steps, including case folding, tokenization, removal of stopwords, normalization, and stemming. Subsequently, the K-means Clustering algorithm is implemented and evaluated using 10fold cross-validation. The results indicate a threshold value of 1.0, an accuracy rate of 64.25%, a precision of 79.29%, and a recall of 59.88% in categorizing bullying words on Instagram. This research underscores the potential of the K-means algorithm in effectively distinguishing between bullying and non-bullying comments. A notable advancement of this paper is the integration of the two tf-idf weighting methods with the K-means clustering algorithm, thereby enhancing the accuracy in grouping comment data into cyberbullying and non-cyberbullying categories.

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