Social commerce is a digital and efficient solution to transform existing address contemporary issues. TikTok Shop, popular trending social platform, competes with established competitors like Facebook Marketplace Instagram Shop. Shop offers benefits incentives attract users for both sales product purchases. In this study, various algorithmic approaches such as Naïve Bayes, K-Nearest Neighbor, ...

Stroke and heart disease are among the most com-mon outcomes of hypertension. Each year, disease, stroke, other cardiovascular disorders claim lives more than 877,500 people in United States, making them first fifth leading causes death, so being able to pre- dict early helps save lives. A lot research has been done reach this goal. Machine learning models mostly used for purpose. For time stud...

In the Internet of Things (IoT) era, mobile crowd sensing system (MCS) has become increasingly important. The Auto (IOTA) evolved rapidly in practically every technology field over last decade. IOTA-based is being developed this study using machine learning to detect and prevent users from engaging fake activities. It been determined through testing evaluation that our method effective for both...

The Diabetes-Mellitus (DM) disease is considered a persistent ailment that triggered by excessive sugar levels in the blood of person. It gives rise to severe health complications when left untreated and can also give related diseases such as cardiac attack, nervous damage, foot problems, liver kidney eye problems. These problems are caused series factors interrelated one another age, gender, f...

As the use of devices in mobile environments increases, network attacks such as DDoS have a malicious attempt to flood network's regular traffic overload target and surrounding infrastructure. This research proposed machine learning deep approaches dealing with attacks, results are described follows. First, this successfully detected through an LGBM 100% accuracy score. Second, model (GRU-LM), ...

In this study, a machine learning-based prediction model was developed using the attribute information of underground pipelines and history ground subsidence in order to predict risk level urban areas. The target area divided into grid with sizes 100m×100m, 300m×300m, 500m×500m, data were utilized build dataset. For input data, pipeline’s diameter, number years used, density selected based on l...

The existing forward collision warning (FCW) systems that adopt kinematic or perceptual parameters have some drawbacks in the performance because of poor adaptability to users ineffectiveness warnings. To solve problems adaptability, several FCW models been proposed based on algorithms (machine learning, deep learning). However, there is a lack consideration for multi-staged avoid an abrupt may...

Recently, the Ethereum smart contracts have seen a surge in interest from scientific community and new commercial uses. However, as online trade expands, other fraudulent practices—including phishing, bribery, money laundering—emerge significant challenges to security. This study is useful for reliably detecting transactions; this work developed deep learning model using unique metaheuristic op...