Breast cancer is categorized as an aggressive disease, and it is one of the leading causes of death. Accurate survival predictions for both long-term and short-term survivors, when delivered on time, can help physicians make effective treatment decisions for their patients. Therefore, there is a dire need to design an efficient and rapid computational model for breast cancer prognosis. In this study, we propose an ensemble model for breast cancer survivability prediction (EBCSP) that utilizes multi-modal data and stacks the output of multiple neural networks. Specifically, we design a convolutional neural network (CNN) for clinical modalities, a deep neural network (DNN) for copy number variations (CNV), and a long short-term memory (LSTM) architecture for gene expression modalities to effectively handle multi-dimensional data. The independent models’ results are then used for binary classification (long term > 5 years and short term < 5 years) based on survivability using the random forest method. The EBCSP model’s successful application outperforms models that utilize a single data modality for prediction and existing benchmarks.

Computation offloading transfers resource-intensive tasks from local Internet of Things (IoT) devices to powerful edge servers, which minimizes latency and reduces the computational load on IoT devices. Deep Reinforcement Learning (DRL) is widely utilized to optimize computation offloading decisions. However, previous studies fall short in two main ways: firstly, they do not collectively optimize the comprehensive state space, and secondly, their reliance on Q-learning and Deep Q Networks (DQN) makes it challenging for agents to discern the optimal action in large action spaces, as many actions may possess similar values. In this paper, we introduce a multi-branch Dueling Deep Q Network (MBDDQN) that tackles the challenges of high-dimensional state-action spaces and long-term cost optimizations in dynamic environments. The Dueling DQN alleviates the complexity of simultaneous offloading and resource allocation decisions, with each branch independently controlling a subset of the decision variables to scale efficiently with an increasing number of IoT devices, thereby avoiding the combinatorial explosion of potential actions. Furthermore, we implement a long short-term memory (LSTM) network with distinct advantage-value layers to enhance both short-term action selection and long-term system cost estimation, as well as improve the temporal learning capacity of the model. Finally, we propose an innovative adaptive cost-weighting mechanism within the reward function to dynamically balance competing objectives, including energy consumption, latency, and bandwidth utilization. Unlike prior works that use fixed reward structures, we leverage weighted state-action advantage values to dynamically adjust the optimization variables. This approach also enables the agent to self-tune, allowing it to prioritize delay minimization in delay-sensitive scenarios and energy conservation in resource-constrained environments. Simulation results demonstrate the superiority of the proposed scheme compared to benchmarks. For instance, MBDDQN reduces delay by 17.88% over DQN and 12.28% over DDPG. Additionally, regarding energy consumption, MBDDQN achieves a 10.1% improvement over DQN and a 7.64% enhancement over DDPG.

The oil port logistics involves multiple parties including oil tanker owners, port authorities, customs, oil suppliers, and shipping companies. These parties need to exchange a significant amount of data and documentation related to cargo, such as bills of lading, customs declarations, and cargo manifests. This huge amount of data and documentation provides ample opportunities for data manipulation and corruption. Moreover, physical documentation is slow and prone to errors and manipulation. This data can be securely stored and shared between different parties in a tamper-proof and transparent manner using blockchain. Blockchain is a decentralized technology that employs secure hashing and consensus algorithms that can detect any data modification. Hence, this work proposes a blockchain-enabled immutable, and efficient framework for trade documentation in oil port logistics. The proposed framework provides timely processing of oil trade documents and ensures immutability while increasing trust among the trade entities. In addition, this work implements a private blockchain for the execution of smart contracts, which can ensure that all parties involved in the logistics process comply with pre-agreed rules and regulations. Simulation results validate the effectiveness of the proposed framework in terms of transparency, immutability, network latency, throughput, and resource utilization.