This study introduces an innovative approach to predict late delivery risks, aiming to strengthen supply chain resilience through smart, data-driven strategies. The approach combines clustering using the Elbow method and multiclassification, incorporating advanced deep learning models like Neural Network(NN), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM). The findings highlight the effectiveness of the (CNN-LSTM) model in producing more accurate results, ultimately improving supply chain resilience, boosting customer satisfaction, and enabling proactive risk management.

This paper examines the integration of Blockchain and the Industrial Internet of Things (IIoT) in manufacturing process monitoring. The proposed model emphasizes Blockchain's decentralization, cryptographic security, and smart contracts for enhanced security and efficiency. It addresses challenges like data security and scalability, showcasing the transformative potential of Blockchain-IIoT integration. In the end this work highlights future development opportunities in smart manufacturing.

Edge caching has emerged as a promising approach to deal with the redundant traffic, to improve the Quality of Service (QoS) and to optimize the energy use in Internet of Vehicles (IoV). However, the intrinsic storage limitations of edge servers pose a critical challenge for IoV edge caching scheme. To solve these issues, several caching policy management techniques have been proposed in literature. In this paper, we perform a systematic comparison among the recent Artificial Intelligence (AI) based caching approaches and the classical caching techniques for IoV. Our objective is to provide a roadmap for choosing the best caching strategy for a given constrained environment. Through a practical scenario, the simulation results show that AI-based edge caching methods achieve high performance in terms of total content access cost and edge hit rate while maintaining a relatively low average delay. On the other hand, hash routing strategies tend to maximize the edge hit rate to the detriment of delivery latency.

Connected Autonomous Vehicles (CAV) are expected to revolutionize the transportation sector. However, given that CAV are connected to internet, they face a principal challenge to ensure security, safety and confidentiality. It is highly valuable to provide a real-time and proactive anomaly detection approach for Vehicular Ad hoc Network (VANET) exchanged data since such an approach helps to trigger prompt countermeasures to be undertaken allowing the damage avoidance. Recent machine learning methods show great efficiency, especially due to their capacity to handle nonlinear problems. However, an accurate anomaly detection in a space–time series is a challenging problem because of the heterogeneity of space–time data and the spatio-temporal correlations. An anomalous behavior can be seen as normal in different context. Thus, using one deep learning model to classify the observations into normal and abnormal or to identify the type of the anomaly is usually not efficient for large high-dimensional multi-variate time-series datasets. In this paper, we propose a stepwise method in which the time-series data are clustered on spatio-temporal clusters using Long Short Term Memory (LSTM) auto-encoder for dimension reduction and Grey Wolf Optimizer based clustering. Then, the anomaly detection is performed on each cluster apart using a hybrid method consisting of Auto-Encoder for feature extraction and Convolution Neural Network for classification. The results shows an increase in the accuracy by 2% in average and in the precision by approximately 1.5%.

Connected and autonomous vehicles (CAV) are expected to change the landscape of the automotive market. They are autonomous decision-making systems that process streams of observations coming from different external and on-board sensors. CAV like any other cyber-physical objects are prone to signal interference, hardware deterioration, software errors, power instability, and cyberattacks. To avoid these anomalies which can be fatal, it is mandatory to design a robust real-time technique to detect them and identify their sources. In this paper, we propose a deep learning approach which consists of hierarchic models to firstly extract the signal features using an LSTM auto-encoder, then perform an accurate classification of each signal sequence in real-time. In addition, we investigated the impact of the model parameter tuning on the anomaly detection and the advantage of channel boosting through three scenarios. The model achieves an accuracy of 95.5% and precision of 94.2%.

Loading and unloading rolling cargo in roll-on/roll-off are important and very recurrent operations in maritime logistics. In this paper, we apply state-of-the-art deep reinforcement learning algorithms to automate these operations in a complex and real environment. The objective is to teach an autonomous tug master to manage rolling cargo and perform loading and unloading operations while avoiding collisions with static and dynamic obstacles along the way. The artificial intelligence agent, representing the tug master, is trained and evaluated in a challenging environment based on the Unity3D learning framework, called the ML-Agents, and using proximal policy optimization. The agent is equipped with sensors for obstacle detection and is provided with real-time feedback from the environment thanks to its own reward function, allowing it to dynamically adapt its policies and navigation strategy. The performance evaluation shows that by choosing appropriate hyperparameters, the agents can successfully learn all required operations including lane-following, obstacle avoidance, and rolling cargo placement. This study also demonstrates the potential of intelligent autonomous systems to improve the performance and service quality of maritime transport.

The retail sector is undergoing an apparent digital transformation that completely revolutionises shopping operations. To stay competitive, retailer stakeholders are forced to rethink and improve their business models to provide an attractive personalised experience to consumers. The self-service checkout process is at the heart of this transformation and should be designed to identify the products accurately and detect any possible anomalous behaviour. In this paper, we introduce a product verification system based on OCR classification and Mondrian conformal prediction. The proposed system includes three components: OCR reading, text classification and product verification. By using image data from existing grocery stores, the system can detect anomalies with high performance, even when there is partial text information on the products. This makes the system applicable for reducing shrinkage loss (caused, for example, by employee theft or shoplifting) in grocery stores by identifying fraudulent behaviours such as barcode switching and miss-scan. Additionally, OCR reading with NLP classification shows that it is in itself a powerful classifier of products.