Industrial products typically lack defects in smart manufacturing systems, which leads to an extremely imbalanced task of recognizing surface defects. With this imbalanced sample distribution, machine learning and deep learning algorithms preferentially learn features from the majority classes, potentially leading to inaccurate results. Addressing the issue of sample imbalance has thus emerged as a critical area of research within the field of industrial intelligent manufacturing. This paper discusses the imbalanced sample problem of industrial product surface defect detection algorithms, and proposes the existence of "four imbalances and two uncertainties". It also summarizes the industrial product surface dataset and innovatively adds the imbalance rate comparison to the dataset. In this study, data re-sampling, data expansion, feature extraction and identification, and re-weighting of category weights are elaborated at the level of data and algorithm respectively. Additionally, the paper explores prospective directions for future research, including supervised and unsupervised learning, transfer learning, anomaly detection, quality prediction, and future challenges. It is hoped to lay a solid foundation for the more far-reaching development of smart manufacturing and surface defect detection methods. And provide some directions for the research of sample imbalance and long-tail problems. 

Surface defect detection methods for industrial products with imbalanced samples: A review of progress in the 2020s

Nowadays, e-commerce is becoming an essential part of business for many reasons, including the simplicity, availability, richness and diversity of products and services, flexibility of payment methods and the convenience of shopping remotely without losing time. These benefits have greatly optimized the lives of users, especially with the technological development of mobile devices and the availability of the Internet anytime and anywhere. Because of their direct impact on the revenue of e-commerce companies, recommender systems are considered a must in this field. Recommender systems detect items that match the customer's needs based on the customer's previous actions and make them appear in an interesting way. Such a customized experience helps to increase customer engagement and purchase rates as the suggested items are tailored to the customer's interests. Therefore, perfecting recommendation systems that allow for more personalized and accurate item recommendations is a major challenge in the e-marketing world. In our study, we succeeded in developing an algorithm to suggest personal recommendations to customers using association rules via the Frequent-Pattern-Growth algorithm. Our technique generated good results with a high average probability of purchasing the next product suggested by the recommendation system.

Machine learning based recommender system for e-commerce

Through the IoT, millions of smart devices may be networked together and set to automatically exchange information with one another. By 2022, the number of connected devices is projected to have reached 80 billion, making IoT one of the fastest-growing digital sectors. Furthermore, additional security concerns have arisen due to the interconnectedness of IoT devices and the many different fields that must work together to install such devices. The underlying weaknesses of IoT devices render useless the implementation of security mechanisms such as protection from cyber threats, authorization, access control, networking, and information protection. To protect the IoT ecosystem, it is necessary to improve upon current security checks. As a result of recent developments in Machine Learning and Deep Learning (ML-DL), artificial intelligence has moved from the realm of science fiction to that of everyday life in several crucial contexts. Therefore, ML-DL techniques play a crucial role in advancing the protection of IoT systems beyond just ensuring encrypted communications between IoT components. To better secure IoT devices, this analysis helps to give a complete overview of ML strategies and current breakthroughs in DL strategies. Vulnerabilities to the protection of the IoT are addressed, including those that have always been present as well as those that are relatively new. The merits, demerits, and overall viability of the ML/DL approach to IoT protection are then described and analyzed. The use of ML/DL to enhance IoT security is explored, along with its advantages and disadvantages. These prospects and difficulties suggest avenues for future study. 

A Comprehensive Analysis of Machine Learning and Deep Learning Approaches towards IoT Security

Attention-based Bayesian inferential imagery captioning maker

Deep models for Multivariate Time Series (MTS) forecasting have recently demonstrated significant success. Channel-dependent models capture complex dependencies that channel-independent models cannot capture. However, the number of channels in real-world applications outpaces the capabilities of existing channel-dependent models, and contrary to common expectations, some models underperform the channel-independent models in handling high-dimensional data, which raises questions about the performance of channel-dependent models. To address this, our study first investigates the reasons behind the suboptimal performance of these channel-dependent models on high-dimensional MTS data. Our analysis reveals that two primary issues lie in the introduced noise from unrelated series that increases the difficulty of capturing the crucial inter-channel dependencies, and challenges in training strategies due to high-dimensional data. To address these issues, we propose STHD, the Scalable Transformer for High-Dimensional Multivariate Time Series Forecasting. STHD has three components: a) Relation Matrix Sparsity that limits the noise introduced and alleviates the memory issue; b) ReIndex applied as a training strategy to enable a more flexible batch size setting and increase the diversity of training data; and c) Transformer that handles 2-D inputs and captures channel dependencies. These components jointly enable STHD to manage the high-dimensional MTS while maintaining computational feasibility. Furthermore, experimental results show STHD's considerable improvement on three high-dimensional datasets: Crime-Chicago, Wiki-People, and Traffic. The source code and dataset are publicly available https://github.com/xinzzzhou/ScalableTransformer4HighDimensionMTSF.git.

https://export.arxiv.org/pdf/2408.04245v1.pdf

Scalable Transformer for High Dimensional Multivariate Time Series Forecasting

India lies in the tropical wet and dry area and gets a huge amount of rainwater in the monsoon season accounting for most of the yearly rainfall. Predicting the rainfall in these types of tropical areas is a difficult task and moreover, the influence of environmental issues has resulted in a substantial rise in the average rainfall distribution as well as the severity and frequency. In this work, for predicting the rainfall various Time series forecasting models like Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt's Winter Seasonal Method (HWSM), Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), and Holt's Linear Trend Method (HLTM) have been used. Finally, a comparative analysis was made to inspect the best time-series forecasting model. The evaluation parameters used to perform a comparative analysis of different Time Series forecasting models are RMSE (Root Mean Square Error), Mean Absolute Error (MAE), and Mean Square Error (MSE). It was observed that the HWSM model had recorded the least error rate of 5.767 and 5.343 as RMSE, 2.143 and 2.432 as MAE, 31.65 and 26.75 as MSE for dataset-1 and dataset-2 respectively when compared to the remaining models.

Comparative Analysis of Time Series Forecasting Models to Predict Amount of Rainfall in Telangana

As communication technology and e-commerce have improved, credit cards have become the most common mode of payment for both online and offline purchases. As a result, security in this system is expected to be very good to avoid fraudulent transactions. Each year, the number of fraudulent credit card data transfers rises. Researchers are also experimenting with unique approaches to detect and prevent such frauds in this direction. However, some strategies that can accurately and efficiently detect these scams are always needed. This study proposes a method for detecting credit card fraud using supervised and unsupervised learning approaches. The suggested neural network method outperforms existing methods such as Logistic Regression, Local Outlier Factor, Isolation Forest, and K-means clustering when compared. As the accuracy values of each model are nearly negligible because the dataset is imbalanced, we will compare the false-negative rate, recall, precision, and recall parameters of each model.

A Comparative Study on Different Fraud Detection Methods in Credit Card-Based Transactions

Role of IoT in Smart Cities: A Review, Applications, Open Challenges and Solutions

Advancements in Forest Fire Prediction: Techniques and Technologies

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