The urgent penetration of the Industrial Internet of Things (IIoT) in industrial sectors has fostered unmatched benefits in terms of efficiency, productivity, and overall performance. Although current security frameworks already offer a sound level of protection, they do not adequately scale to the growing diversity and number of devices integrated into the Industrial Internet of Things. They rely on static, centralized network organization and management, which cannot ensure data integrity across a huge number of highly distributed devices. The proposed research provides insight into an innovative end-to-end security solution model, which caters to the needs of IIoT via the introduction of the blockchain. The current security solutions seem to fail in terms of security, scalability, data efficiency, integrity, and the actual needs of IIoT. As a result, the top chance of a cyberattack or similar threat remains high. The proposed work demonstrates that IIoTs may benefit from blockchain frameworks because blockchains reuse existing security mechanisms, enable transaction validation without central trust, and implement appropriate authorization. Additionally, the IIoT environment can benefit from implementing security frameworks that reflect their characteristics. In this paper, an overall solution that employed the decentralized setup of blockchain was proposed for authentication, authorization, and data integrity for all devices. Our approach is free from the need for any central authority. That mechanism is based on smart contracts that enforce security policies. Our framework allows for the switching of any access rule at any time, as well as it can immediately react to some suspicious behavior. In our paper, we proposed some lightweight cryptographic schemes, and Data management overhead which could fit any device. Moreover, our algorithm for the blockchain implementation is hybrid because this approach incorporates all the best features of the private and public implementation due to the individual blocks containing feasible information rather than the entire history.

With the advancement of information technology and economic globalization, the problem of supplier selection is gaining in popularity. The impact of supplier selection decisions made were quick and noteworthy on the healthcare profitability and total cost of medical equipment. Thus, there is an urgent need for decision support systems that address the optimal healthcare supplier selection problem, as this problem is addressed by a limited number of studies. Those studies addressed this problem mathematically or by using meta-heuristics methods. The focus of this work is to advance the meta-heuristics methods by considering more objectives rather than the utilized objectives. In this context, the optimal supplier selection problem for healthcare equipment was formulated as a mathematical model to expose the required objectives and constraints for the sake of searching for the optimal suppliers. Subsequently, the problem is realized as a multi-objective problem, with the help of this proposed model. The model has three minimization objectives: (1) transportation cost; (2) delivery time; and (3) the number of damaged items. The proposed system includes realistic constraints such as device quality, usability, and service quality. The model also takes into account capacity limits for each supplier. Next, it is proposed to adapt the well-known non-dominated sorting genetic algorithm (NSGA)-III algorithm to choose the optimal suppliers. The results of the adapted NSGA-III have been compared with several heuristic algorithms and two meta-heuristic algorithms (i.e., particle swarm optimization and NSGA-II). The obtained results show that the adapted NSGA-III outperformed the methods of comparison.

The bin packing problem (BPP) is a classic combinatorial optimization problem with several variations. The BPP with conflicts (BPPCs) is not a well-investigated variation. In the BPPC, there are conditions that prevent packing some items together in the same bin. There are very limited efforts utilizing metaheuristic methods to address the BPPC. The current methods only pack the conflict items only and then start a new normal BPP for the non-conflict items; thus, there are two stages to address the BPPC. In this work, an adaption of the jellyfish metaheuristic has been proposed to solve the BPPC in one stage (i.e., packing the conflict and non-conflict items together) by defining the jellyfish operations in the context of the BPPC by proposing two solution representations. These representations frame the BPPC problem on two different levels: item-wise and bin-wise. In the item-wise solution representation, the adapted jellyfish metaheuristic updates the solutions through a set of item swaps without any preference for the bins. In the bin-wise solution representation, the metaheuristic method selects a set of bins, and then it performs the item swaps from these selected bins only. The proposed method was thoroughly benchmarked on a standard dataset and compared against the well-known PSO, Jaya, and heuristics. The obtained results revealed that the proposed methods outperformed the other comparison methods in terms of the number of bins and the average bin utilization. In addition, the proposed method achieved the lowest deviation rate from the lowest bound of the standard dataset relative to the other methods of comparison.

The procedure of processing the vermicompost production includes several stages, where the vermicompost material has different temperatures during these different stages. Thermal sensors play a key role in numerous fields, such as medical and agricultural applications. Thermal cameras can produce a thermal image or an array of values representing the array of sensory data. i.e., an array of temperatures. In this study, we proposed the first thermal imagery dataset of the vermicompost production process. The contributions of this work are two-fold using the proposed dataset. First, we framed the process of predicting the vermicompost production process as a classification problem. Second, we compared classifying the different stages of the process of vermicompost production based on two different input types, namely, thermal images and an array of temperatures. In other words, the classifier will be fed with an input (an image or an array of temperatures), and then the classifier will predict the vermicompost production stage. In this context, we utilized several machine and deep learning models as classifiers. For the utilized dataset, the study has been conducted on a set of images collected during the vermicompost production procedure which was collected every 14 days over 42 consecutive days, i.e., four classes.We proposed running a series of experiments to determine which input type yields better classification accuracy. The obtained results show that using thermal images for the sake of classifying the vermicompost production stages achieved higher accuracy, about 92%, in comparison to using the sensor array data, about 60%.

Image forgery detection and localization are critical for verifying the authenticity of digital images. This paper presents REFORGE, an ensemble dual-branch deep network that combines a classification branch for detecting manipulated images and a segmentation branch for localizing tampered regions, enhanced with a reinforcement learning (RL) based refinement module. The classification branch identifies forged images with high precision, while the segmentation branch produces pixel-level tampering masks. A lightweight RL agent then iteratively improves the predicted mask, treating each pixel as an agent to maximize a reward for accurate localization. We integrate outputs from multiple state-of-the-art models in an ensemble fashion to boost generalization and robustness. Extensive experiments on standard benchmarks (CASIA v2 and augmented social-network degraded images) demonstrate that our method achieves near-perfect forgery detection (accuracy ∼98.9%, AUROC ∼0.997) and significantly improved localization on tampered images (mean IoU = 0.570, median = 0.644, Q1/Q3 = 0.323/0.852, IoU≥ 0.5: 63.35%, IoU≥ 0.75: 38.36%). We present ablation studies showing the contribution of each component (ensemble learning, dual-branch architecture, and RL refinement) to overall performance. The proposed approach outperforms baseline models and conventional post-processing (e.g. DenseCRF) in both detection and localization. Our work represents a novel integration of ensemble deep learning and reinforcement learning for image forensics, pushing the state-of-the-art in tampering localization accuracy and reliability.