Effective project risk management (PRM) necessitates accurate prediction and actionable insights. Machine learning (ML) models improve risk assessment by uncovering complex patterns; however, their black-box nature limits interpretability, making it difficult for stakeholders to trust predictions. Traditional explainable artificial intelligence (XAI) methods highlight influential risk factors yet often fail to provide actionable recommendations, focusing on model behavior rather than practical interventions. Counterfactual explanations (CEs) aim to bridge this gap by suggesting modifications to risk factors or project conditions that could alter outcomes. However, existing CE methods in PRM often lack domain specificity, overlook interdependencies, and ignore temporal constraints, producing recommendations that are unrealistic or infeasible. To address these limitations, we propose Counterfactual Reasoning with Risk Temporal Knowledge Graph (CR-RTKG), a framework that integrates counterfactual reasoning with a Risk Temporal Knowledge Graph (RTKG) to improve interpretability and actionability of risk mitigation. The RTKG encodes domain knowledge, models causal dependencies and cascading effects, and classifies risks by temporal horizon, supporting prioritization based on urgency and systemic influence. By embedding stakeholder-defined constraints into a multi-objective optimization process, CR-RTKG generates context-sensitive and feasible counterfactuals. Unlike conventional methods, it aligns recommendations with real-world project constraints. Experimental results show that CR-RTKG achieves higher plausibility (96%) and feasibility (93%), outperforming baselines including Diverse Counterfactual Explanations (DiCE) and Flow-based Counterfactual Explanation (CeFlow).

Bearing the significance of environmental issues in supply chain systems, this study aims to reduce emissions by imposing government regulations to achieve environmental benefits and improved profitability. The sustainability framework is actively addressed from economic and environmental perspectives, while social issues are considered indirectly in this study. A single setup multiple delivery policy is adopted to reduce the vendor’s supply chain cost and improve the customer’s (buyer’s) satisfaction. This policy is further modified by integrating a realistic transportation function, which accounts for unequal delivery of lot sizes and return transport for defective items. Carbon emissions are considered during transportation, production, rework, and product holding processes. To regulate emissions, well-known carbon regulations (e.g., carbon tax, carbon cap-and-offset, and cap-and-trade) have been adopted in the proposed model. Under the single setup multiple delivery policy, the vendor’s setup cost plays a crucial role in integrated cost calculation; therefore, setup cost reduction is also examined to assess its effect on the integrated cost function. Finally, theoretical observations and sensitivity analyses have been carried out to demonstrate the robustness of the proposed model. The actionable managerial and policy implications based on the study findings have also been developed for industry practitioners and regulatory agencies

In high-stakes project risk assessment, balancing predictive accuracy with interpretability is critical to fostering stakeholder trust and supporting well-informed decision-making. This study presents the Interpretable Risk Assessment Framework with Belief Rule-Based Systems (IRAF-BRB), an Explainable AI (XAI) framework specifically designed to improve transparency, accountability, and accuracy in risk assessment. IRAF-BRB combines Interpretive Structural Modeling (ISM) to map and analyze interdependencies among risk factors with an optimized Belief Rule-Based (BRB) model. A modified Differential Evolution Covariance Matrix Self-Adaptation (DECMSA) algorithm is employed to enhance the predictive power of the BRB model while preserving interpretability, ensuring that stakeholders can both trust and understand the model’s outputs. By transforming complex risk data into intuitive visualizations, the IRAF-BRB framework enables project managers to identify key risk drivers and anticipate cascading effects, leading to proactive risk mitigation. Experimental results demonstrate that IRAF-BRB reduces Mean Squared Error (MSE) to 4.09 e − 4 in predicting risk levels for high-rise construction projects, outperforming traditional BRB models such as Differential Evolution-based BRB (DE-BRB) ( 8.29 e − 4 ) and Particle Swarm Optimization-based BRB (PSO-BRB) ( 2.53 e − 3 ) . The statistical significance of these results was confirmed via a two-sample t-test ( p < 0.05 ) , establishing IRAF-BRB as a reliable and effective tool for accurate and interpretable risk assessment.

Integrating machine learning (ML) into healthcare has rapidly advanced, necessitating precise and reliable explanatory mechanisms, especially in critical areas such as glaucoma detection and analysis. This systematic review examines how Explainable Artificial Intelligence (XAI) enhances the transparency and comprehensibility of machine learning algorithms for glaucoma detection. By emphasizing XAI, the review aims to demonstrate its critical importance in a field where accuracy and trust are essential. To evaluate the effectiveness of XAI in conjunction with ML, this review meticulously assesses various XAI methodologies for their ability to clarify the intricate workings of ML models. The analysis is grounded in examining well-known medical imaging datasets geared towards glaucoma detection, providing a focused overview of XAI’s role in interpreting complex ML decisions in a healthcare context. Findings from the review indicate that applying XAI techniques has significantly improved clinician trust in ML-driven decisions by making the decision-making processes more transparent and comprehensible. This enhancement in trust is attributed to XAI’s ability to provide deeper insights into the logic and reasoning behind ML algorithms, thereby facilitating a better understanding of their outcomes. Although the application of XAI in glaucoma detection and analysis has shown promising improvements in clinician trust and the transparency of ML models, there remains a critical need for more comprehensive research. Such studies would aim to fully ascertain the long-term impacts of XAI-enhanced ML on healthcare outcomes, particularly in glaucoma analysis, where the stakes for accurate and understandable diagnostic tools are incredibly high.

Agriculture and food production constantly struggle with tracking the growth of crops and controlling weeds. Weeds take away important resources like water, nutrients, and sunlight from crops. This can cause a big decrease in how many crops grow if the weeds are not taken care of properly. Modern agriculture is increasingly using artificial intelligence (AI) based systems to effectively monitor and manage weeds. Mapping weeds with remote sensing or image processing helps effectively measure their impact. Extensive research indicates that machine learning or AI is effective in assessing the impact of weeds and quantifying their presence within crops. However, the internal decision-making processes of some AI approaches are complex, making them difficult to understand even for AI experts. In agriculture, it’s essential that these decision-making approaches are easy to understand for people who are not familiar with AI. In this research, we propose an interpretable method for identifying crops and weeds from images captured by unmanned aerial vehicles (UAVs), or drones. First, we used U-net segmentation on UAV datasets to filter out noise in the images. U-net is effective in extracting detailed local information, like textures, and learning the connections between pixels in an image. The filtered images are then processed by Vision Transformers (ViT), which extract both local and global contextual information about weeds and crops from them. This information aids in measuring the quantity of weeds in the fields. Finaly we apply Explainable AI (XAI) approaches layer-wise relevance propagation (LRP) and pixel density analysis (PDA). These techniques demonstrate the step-by-step decision-making process in measuring the amount of weeds from the images.

Abstract Locating a set of influential users within a social network, known as the Influence Maximization (IM) problem, can have significant implications for boosting the spread of positive information/news and curbing the spread of negative elements such as misinformation and disease. However, the traditional simulation‐based spread computations under conventional diffusion models render existing algorithms inefficient in finding optimal solutions. In recent years, hop and path‐based approaches have gained popularity, particularly under the cascade models to address the scalability issue. Nevertheless, these existing functions vary based on the considered hop‐distance and provide no guidance on capturing spread sizes beyond two‐hops. In this paper, we introduce Hop‐based Expected Influence Maximization (HEIM), an approach utilizing generalized functions to compute influence spread across varying hop‐distances in conventional diffusion models. We extend our investigation to the Linear Threshold (LT) model, in addition to the Independent Cascade (IC) and Weighted Cascade (WC) models, filling a gap in current literature. Our theoretical analysis shows that the proposed functions preserve both monotonicity and submodularity, and the proposed HEIM algorithm can achieve an approximation ratio of under a limited hop‐measures, whereas a multiplicative ‐approximation under global measures. Furthermore, we show that expected spread methods can serve as a better benchmark approach than existing simulation‐based methods. The performance of the HEIM algorithm is evaluated through experiments on three real‐world networks, and is compared to six other existing algorithms. Results demonstrate that the three‐hop based HEIM algorithm achieves superior solution quality, ranking first in statistical tests, and is notably faster than existing benchmark approaches. Conversely, the one‐hop‐based HEIM offers faster computation while still delivering competitive solutions, providing decision‐makers with flexibility based on application needs.

The emergence of artificial intelligence (AI) and its related capabilities has led industries to rethink the existing practices of conventional supply chain management and data analysis. Machine learning (ML), Deep Learning (DL) and their unique ability to predict future data and classify data have led to important research in the supply chain (SC) domain, particularly in identifying and prioritising supply chain risks. This paper proposes several DL methodologies to exploit the benefit of DL, particularly to identify whether any product will be delivered late due to any unforeseen reason in a complex SC system. Four different DL architectures (Simple-LSTM, Deep-LSTM, 1D-CNN, and TCN-1DSPCNN models) are proposed to extract features, while six variant classifiers: Softmax, random trees (RT), random forest (RF), K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM), were used to classify delay or non-delay information. By seamlessly capturing intricate temporal dependencies, these DL models enhance accuracy in robustly identifying supply chain late orders. Leveraging their hierarchical feature learning, these proposed DL models excel in recognizing subtle patterns and correlations, making them ideal for classifying late orders within the supply chain. Their parallel processing prowess facilitates real-time decision support, allowing organizations to address potential delays and allocate resources effectively and proactively. Five-fold cross-validation is presented to avoid over-fitting and to prove the efficiency of the proposed DL models. The total accuracies of the six ML classifiers are 74.03, 75.81, 93.35, 87.72, 93.59, and 95.10, respectively, while the maximum accuracies obtained from four proposed DL methodologies obtained an accuracy of 97.6, 98.63, 100, 100% respectively using the SVM classifier for predicting late orders based on five-fold cross-validation.

The profitability of a supply chain (SC) is proportional to the stability of all its stakeholders as well as their consistent information sharing with an effective and efficient communication mechanism. Various inefficiencies, such as the bullwhip effect (BWE) and product unavailability, may be caused by a lack of coordination in an SC. The importance of sharing consumer demand has been quantified by comprehensive studies under the assumption that all SC participants will access the same information. However, only a few studies have studied the effect of minimal coordination or limited visibility of information while considering their effect on the overall efficiency of an SC. This work primarily leverages blockchain technology (BCT) to create a simulation model. To do this, an SC BWE-based model is initially developed. Following that, a blockchain-based robust information sharing system is simulated. Furthermore, information sharing is challenging, and SC stakeholders may not really trust each other and hence be reluctant to share sensitive information. Considering that, this paper propose an improved proof-of-authority (PoA) consensus algorithm that will increase trust in a decentralized SC model. Multiple experiments are carried out to demonstrate the effectiveness of our approach, and the simulation results clearly demonstrate the effectiveness of information sharing in a supply chain via blockchain, as well as that trust between partners tends to increase overall SC efficiency and reduce BWE.

In recent years, supply chains have evolved into huge ecosystems, demanding trust, provenance, and data privacy. Since blockchain technology (BCT) allows for the development of a distributed environment, it is ideal for supply chain management (SCM) applications. However, concerns regarding data privacy have impeded the development of blockchains. Despite the fact that some blockchains can restrict participants from reading and/or writing data, blockchain’s transparency makes protecting sensitive data challenging. To solve the data privacy challenge, this paper proposes a framework, AccessChain, that is an SCM access control framework that is based on an attribute-based access control (ABAC) model that restricts access to competing parties while allowing for network scalability. This proposed AccessChain model has two types of ledgers in its system: local and global. Local ledgers are used to store business contracts between stakeholders and the attribute-based access control model management, whereas the global ledger is used to record transaction data. AccessChain can enable decentralized, fine-grained and dynamic access control management in SCM when combined with the ABAC model and BCT. This paper’s experimental results illustrate that high throughput can be achieved in a large-scale request environment while maintaining data privacy and sustaining a scalable network.

The accuracy and reliability of XAI methods are important to establish their credibility and use in complex decision-making tasks. Existing XAI methods provide little information about the correctness and reliability of their outputs. Furthermore, post-hoc explanation approaches explain the outcomes after producing them, not in a step-by-step glass-box manner to explain how an output is reached. Our proposed approach addresses these drawbacks by designing a Belief-Rule-Based (BRB) framework that interprets in a glass-box manner why a particular decision has been reached. It does that by determining the chance of different output classes occurring for a specific time period by considering the different possible permutations of the inputs along with their influence. This also assists the user to determine if the given input dataset is incomplete, vague, imprecise or inconsistent before trusting the analysis emanating from it. We compare the performance of the proposed BRB approach against the different eXplainable artificial intelligence (XAI) methods, such as SHAP, LIME and LINDA-BN to ensure the users of the trustworthiness of its analysis. This also enables users to determine the extent to which each of the XAI techniques meets the requirements of XAI and the gaps that need to be addressed.

The five papers in this special section focus on the impact to supply chain management in a post-COVID pandemic era, with emphasis on the applications on artificial intelligent technologies for the emerging Fifth Industrial Revolution (Industry 5.0).

This article proposes a novel framework to improve the prediction accuracy of very short-term (5-min) wind power generation. The framework consists of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), monarch butterfly optimization (MBO) and long short-term memory (LSTM), called CEMOLS. The CEEMDAN is employed to extract complex hidden features of time-series data into intrinsic mode functions that are predicted using LSTM models with dropout regularization to retain long-term relationships between input and output data, while the optimization algorithm tunes the hyperparameters of the forecasting model. Data from four real wind farms in New South Wales are collected and preprocessed to train and test the forecasting models. Recently developed rival models are compared to identify the best-performing prediction model. The analysis demonstrates that the proposed CEMOLS with low computation time can improve forecasting accuracy on average by 32.96% in mean absolute error, 47.10% in root mean square error and 32.33% in mean absolute percentage error as compared to the benchmark Persistence model. It also demonstrates that sensitive and statistical analysis needs to be carried out to determine robust prediction models among rival models for practical application.

Uncertainty is ubiquitous in almost every real-life optimization problem, which must be effectively managed to get a robust outcome. This is also true for the Influence Maximization (IM) problem, which entails locating a set of influential users within a social network. However, most of the existing IM approaches have overlooked the uncertain factors in finding the optimal solution, which often leads to subpar performance in reality. A few recent studies have considered only the epistemic uncertainty (i.e., arises from the imprecise data), while ignoring completely the aleatory uncertainty (i.e., arises from natural or physical variability). In this article, we propose a formulation and a novel algorithm for the Robust Influence Maximization (RIM) problem under both types of uncertainties. First, we develop a robust influence spread function under aleatory uncertainty that, in contrast to the existing IM theory, is no longer monotone and submodular. Thereafter, we expand our RIM formulation to incorporate epistemic uncertainty aiming to maximize the robust ratio between the selected worst-case solution and the best-case optimal solution, adopting a conservative approach. Furthermore, using a chance-constraint-based method, we investigated feasibility robustness by accounting for the uncertainties related to constraint functions. Finally, an Evolutionary Algorithm (named EA-RIM) is designed to solve the proposed formulation of the RIM problem. Experimental evaluation results on four empirical datasets show that our proposed formulation and algorithm are more effective in dealing with uncertainties and finding an optimal solution for the RIM problem.

Identification of a set of influential spreaders in a network, called the Influence Maximization (IM) problem, has gained much popularity due to its immense practicality. In real-life applications, not only the influence spread size, but also some other criteria such as the selection cost and the size of the seed set play an important role in selecting the optimal solution. However, majority of the existing works have treated this issue as a single-objective optimization problem, where decision-makers are forced to make their choices regarding other variables in advance despite having a thorough understanding of them. This research formulates a multi-objective version of the IM problem (referred to as MOIMP), which considers three competing objectives while subject to certain practical restrictions. Theoretical analysis reveals that the influence spreading function under the suggested MOIMP framework is no longer monotone, but submodular. We also considered three well-established multi-objective evolutionary algorithms to solve the proposed MOIMP. Since the proposed MOIMP addresses varying-size seeds, all the considered algorithms are significantly modified to fit into it. Experimental results on four real-life datasets, evaluating and comparing the performance of the considered algorithms, demonstrate the effectiveness of the proposed MOIMP.

With continued improvements in wireless sensing technology, the notion of the Internet of Things (IoT) has been widely adopted and has become pervasive owing to its broad applications in scenarios such as ambient assisted living, smart healthcare, and smart homes. In that regard, human activity recognition (HAR) is a vital element of intelligent systems to undertake persistent surveillance of human behavior. Due to the omnipresent impact of smartphones in each person’s life, smartphone inertial sensors are used as a case study for this research. Most of the conventional approaches regard HAR as a time-series classification problem; yet, the accuracy of recognition degrades for heterogeneous sensors. In this article, we investigate encoding sensory heterogeneous HAR (HHAR) data into three-channel image representation (i.e., RGB), hence treat the HHAR task as an image classification problem. Since present convolutional network models are computationally heavy when deployed in the IoT environment, we propose a lightweight model image encoded HHAR, called multiscale image-encoded HHAR (MS-IE-HHAR). The model employs a hierarchical multiscale extraction (HME) module followed by an improved spatialwise and channelwise attention (ISCA) module to form the main architecture of the model. The HME module is formed by a group of residually connected shuffle group convolutions (SG-Conv) to extract and learn image representations from different receptive fields while reducing the number of network parameters. The ISCA module combines a lightweight spatialwise attention (SwA) block and an improved channelwise attention (CwA) module to enable the network to pay instructive attention to spatial correlations as well as channel interdependency information. Finally, two widely available HHAR public data sets (i.e., HHAR UCI, and MHEALTH) were used to evaluate the performance of the proposed models with accuracy over 98% and 99%, respectively, demonstrating the model superiority for modeling HAR from heterogeneous data sources.

Abstract Feature Selection (FS) is an important preprocessing step that is involved in machine learning and data mining tasks for preparing data (especially high-dimensional data) by eliminating irrelevant and redundant features, thus reducing the potential curse of dimensionality of a given large dataset. Consequently, FS is arguably a combinatorial NP-hard problem in which the computational time increases exponentially with an increase in problem complexity. To tackle such a problem type, meta-heuristic techniques have been opted by an increasing number of scholars. Herein, a novel meta-heuristic algorithm, called Sparrow Search Algorithm (SSA), is presented. The SSA still performs poorly on exploratory behavior and exploration-exploitation trade-off because it does not duly stimulate the search within feasible regions, and the exploitation process suffers noticeable stagnation. Therefore, we improve SSA by adopting: i) a strategy for Random Re-positioning of Roaming Agents (3RA); and ii) a novel Local Search Algorithm (LSA), which are algorithmically incorporated into the original SSA structure. To the FS problem, SSA is improved and cloned as a binary variant, namely, the improved Binary SSA (iBSSA), which would strive to select the optimal or near-optimal features from a given dataset while keeping the classification accuracy maximized. For binary conversion, the iBSSA was primarily validated against nine common S-shaped and V-shaped Transfer Functions (TFs), thus producing nine iBSSA variants. To verify the robustness of these variants, three well-known classification techniques, includingk-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest (RF) were adopted as fitness evaluators with the proposed iBSSA approach and many other competing algorithms, on 18 multifaceted, multi-scale benchmark datasets from the University of California Irvine (UCI) data repository. Then, the overall best-performing iBSSA variant for each of the three classifiers was compared with binary variants of 12 different well-known meta-heuristic algorithms, including the original SSA (BSSA), Artificial Bee Colony (BABC), Particle Swarm Optimization (BPSO), Bat Algorithm (BBA), Grey Wolf Optimization (BGWO), Whale Optimization Algorithm (BWOA), Grasshopper Optimization Algorithm (BGOA) SailFish Optimizer (BSFO), Harris Hawks Optimization (BHHO), Bird Swarm Algorithm (BBSA), Atom Search Optimization (BASO), and Henry Gas Solubility Optimization (BHGSO). Based on a Wilcoxon’s non-parametric statistical test ( $$\alpha =0.05$$ α=0.05 ), the superiority of iBSSA with the three classifiers was very evident against counterparts across the vast majority of the selected datasets, achieving a feature size reduction of up to 92% along with up to 100% classification accuracy on some of those datasets.

Processing data from Internet of Things (IoT) applications at the cloud centers has known limitations relating to latency, task scheduling, and load balancing. Hence, there have been a shift towards adopting fog computing as a complementary paradigm to cloud systems. In this article, we first propose a multi-objective task-scheduling optimization problem that minimizes both the makespans and total costs in a fog-cloud environment. Then, we suggest an optimization model based on a Discrete Non-dominated Sorting Genetic Algorithm II (DNSGA-II) to deal with the discrete multi-objective task-scheduling problem and to automatically allocate tasks that should be executed either on fog or cloud nodes. The NSGA-II algorithm is adapted to discretize crossover and mutation evolutionary operators, rather than using continuous operators that require high computational resources and not able to allocate proper computing nodes. In our model, the communications between the fog and cloud tiers are formulated as a multi-objective function to optimize the execution of tasks. The proposed model allocates computing resources that would effectively run on either the fog or cloud nodes. Moreover, it efficiently organizes the distribution of workloads through various computing resources at the fog. Several experiments are conducted to determine the performance of the proposed model compared with a continuous NSGA-II (CNSGA-II) algorithm and four peer mechanisms. The outcomes demonstrate that the model is capable of achieving dynamic task scheduling with minimizing the total execution times (i.e., makespans) and costs in fog-cloud environments.

Autonomous learning algorithms operate in an online fashion in dealing with data stream mining, where minimum computational complexity is a desirable feature. For such applications, parsimonious learning machines (PALMs) are suitable candidates due to their structural simplicity. However, these parsimonious algorithms depend upon predefined thresholds to adjust their structures in terms of adding or deleting rules. Besides, another adjustable parameter of PALM is the fuzziness in membership grades. The best set of such hyper parameters is determined by experts' knowledge or by optimization techniques such as greedy algorithms. To mitigate such experts' dependency or usage of computationally expensive greedy algorithms, in this work, a meta heuristic-based optimization technique, called the multimethod-based optimization technique (MOT), is utilized to develop an advanced PALM. The performance has been compared with some popular optimization techniques, namely, the greedy search, local search, genetic algorithm (GA), and particle swarm optimization (PSO). The proposed parsimonious learning algorithm with MOT outperforms the others in most cases. It validates the multioperator-based optimization technique's advantages over the single operator-based variants in selecting the best feasible hyperparameters for the autonomous learning algorithm by maintaining a compact architecture.

Real-time forecasting of the financial time-series data is challenging for many machine learning (ML) algorithms. First, many ML models operate offline, where they need a batch of data, which may not be available during training. Besides, due to a fixed architecture of the majority of the offline-based ML models, they suffer to deal with the uncertain nature of financial time-series data. In contrast, online learning mode evolving-structured ML models could be promising for financial time-series forecasting. For real-time deployment of such models, low memory demand is a must. Besides, the model’s explainability plays a crucial role in forecasting financial time-series. Considering all the requirements, a rule-based autonomous neuro-fuzzy learning algorithm called the parsimonious learning machine (PALM) is proposed here to forecast time-varying stock indices. To provide efficient automation of the proposed algorithm by maintaining the model explainability in terms of limited number linguistic IF-THEN rules, two popular multiobjective evolutionary algorithms (MEAs), such as a real-coded genetic algorithm (GA) and a self-adaptive differential evolution (DE) algorithm are utilized here. In addition, fuzzy type-2 variants of PALMs’ are considered here due to better uncertainty handling capacity than their type-1 counterparts. To evaluate the proposed algorithm’s performance, the closing stock price of fifteen (15) different stock market indices are predicted here. From the results, it is observed that the MEA-based PALMs are performing better than the state-of-the-art benchmark online ML models and providing a rule-based explainable model to the end-user.

Increases in demand for a greater variety of products help companies gain more shares of growing competitive markets but, in contrast, lead to an increase in production processes and, therefore, higher costs and longer lead times. Although several techniques for platform formations and assembly lines have been introduced to enable more varieties of goods to be produced, this also makes a system more complex and less cost-efficient. This paper proposes a differential evolution (DE) approach that incorporates a new heuristic method, improved solution representation and enhanced crossover and mutation operators for solving the modular-based product family design problem in a reconfigurable manufacturing system. The heuristic is applied to repair some solutions in the initial population by replacing eligible components with packages to provide near-optimal solutions in the initial stage and enable DE to find the optimal solution quickly. The proposed crossover is designed to further use the repaired solutions to produce new individuals with better qualities. Finally, a case study of a kettle family is conducted to validate this heuristic method, with the experimental results showing that it saves 57.5% of the purchasing costs of components and, on average, 41.35% of setup costs compared with those of median-joining phylogenetic network- and non-platform-based heuristics. Moreover, the proposed DE achieves improved performances with average errors of 63.34% and 38.52% from those of the standard versions of DE and a genetic algorithm, respectively, in terms of the total production costs of producing the same variants.

The Resource-Constrained Project Scheduling Problem (RCPSP) is an NP-hard optimisation problem that can be found in many real-world applications. Considerable research effort has been put into overcoming the difficulties in solving the RCPSP by proposing innovative heuristics, meta-heuristics and their hybridisation. However, finding optimal solutions is still not guaranteed. It is known that quantum-inspired metaheuristics can improve population diversity and the quality of solutions but little has been published on adapting them to solving RCPSPs. Here, we examine the performance of a Quantum-Inspired Differential Evolution (QIDE) algorithm in solving such problems. The proposed QIDE uses a quantum population that is initialised using the rotation quantum gate and quantum superposition in the continuous domain, and then evolved using the differential-evolution operators. A local search is also adopted to accelerate convergence. The performance of the QIDE algorithm was tested by solving problems with 30 and 60 activities from the PSPLIB benchmark datasets. The QIDE algorithm outperformed another quantum-based particle swarm algorithm and some other meta-heuristics.

In project planning, traditionally, the project managers first schedule the project activities, and then plan for materials ordering and supplier selection. This disintegrated approach lacks in planning co-ordination and causes a loss in expected profit for the organization. In this study, a concurrent model is proposed for resource constraint project scheduling with materials ordering and suppliers selection problems. The proposed model aims to maximize overall net present value for the organization considering ordering cost, procurement cost, materials holding cost, and the project’s deadline penalty cost. The resource constraint project scheduling with materials ordering and suppliers selection is an NP-hard problem. Thus, a memetic algorithm hybridizing the genetic algorithm with a forward-backward improvement based local search is proposed to solve the proposed model. The proposed algorithm is tested on self-generated 120 instances varied from 30 to 60 activity projects with 4 to 8 suppliers. Experimental results show that the proposed genetic algorithm-based memetic algorithm approach generates better solutions than the standalone genetic algorithm. The concurrent project scheduling with materials ordering and supplier selection approach and solution methods have a significant implication for the managers to complete the project in an economic and timely manner.

Modern information technology, such as the internet of things (IoT) provides a real-time experience into how a system is performing and has been used in diversified areas spanning from machines, supply chain, and logistics to smart cities. IoT captures the changes in surrounding environments based on collections of distributed sensors and then sends the data to a fog computing (FC) layer for analysis and subsequent response. The speed of decision in such a process relies on there being minimal delay, which requires efficient distribution of tasks among the fog nodes. Since the utility of FC relies on the efficiency of this task scheduling task, improvements are always being sought in the speed of response. Here, we suggest an improved elitism genetic algorithm (IEGA) for overcoming the task scheduling problem for FC to enhance the quality of services to users of IoT devices. The improvements offered by IEGA stem from two main phases: first, the mutation rate and crossover rate are manipulated to help the algorithms in exploring most of the combinations that may form the near-optimal permutation; and a second phase mutates a number of solutions based on a certain probability to avoid becoming trapped in local minima and to find a better solution. IEGA is compared with five recent robust optimization algorithms in addition to EGA in terms of makespan, flow time, fitness function, carbon dioxide emission rate, and energy consumption. IEGA is shown to be superior to all other algorithms in all respects.

Human activity recognition (HAR) has been regarded as an indispensable part of many smart home systems and smart healthcare applications. Specifically, HAR is of great importance in the Internet of Healthcare Things (IoHT), owing to the rapid proliferation of Internet of Things (IoT) technologies embedded in various smart appliances and wearable devices (such as smartphones and smartwatches) that have a pervasive impact on an individual's life. The inertial sensors of smartphones generate massive amounts of multidimensional time-series data, which can be exploited effectively for HAR purposes. Unlike traditional approaches, deep learning techniques are the most suitable choice for such multivariate streams. In this study, we introduce a supervised dual-channel model that comprises long short-term memory (LSTM), followed by an attention mechanism for the temporal fusion of inertial sensor data concurrent with a convolutional residual network for the spatial fusion of sensor data. We also introduce an adaptive channel-squeezing operation to fine-tune convolutional a neural network feature extraction capability by exploiting multichannel dependency. Finally, two widely available and public HAR data sets are used in experiments to evaluate the performance of our model. The results demonstrate that our proposed approach can overcome state-of-the-art methods.

The rapid growth of the Internet of Things (IoT) technologies has generated a huge amount of traffic that can be exploited for detecting intrusions through IoT networks. Despite the great effort made in annotating IoT traffic records, the number of labeled records is still very small, increasing the difficulty in recognizing attacks and intrusions. This study introduces a semi-supervised deep learning approach for intrusion detection (SS-Deep-ID), in which we propose a multiscale residual temporal convolutional (MS-Res) module to finetune the network capability in learning spatiotemporal representations. An improved traffic attention (TA) mechanism is introduced to estimate the importance score that helps the model to concentrate on important information during learning. Furthermore, a hierarchical semi-supervised training method is introduced which takes into account the sequential characteristics of the IoT traffic data during training. The proposed SS-Deep-ID is easily integrated into a fog-enabled IoT network to offer efficient real-time intrusion detection. Finally, empirical evaluations on two recent data sets (CIC-IDS2017 and CIC-IDS2018) demonstrate that SS-Deep-ID improves the efficiency of intrusion detection and increases the robustness of performance while maintaining computational efficiency.

Although intelligent load forecasting is essential for optimal energy management (EM) in smart cities, there is a lack of current research exploring EM in well-regulated Internet-of-Things (IoT) networks. This article develops a new deep learning (DL) model for efficient forecasting of short-term energy consumption while maintaining effective communication between energy providers and users. The proposed Energy-Net stack comprises multiple stacked spatiotemporal modules, where each module consists of a temporal transformer (TT) submodule and a spatial transformer (ST) submodule. The TT models the temporal relationships in load data; and the ST submodule extracts hidden spatial information by integrating convolutional layers and includes an improved self-attention mechanism. The experimental evaluation on IHPEC and independent system operator New England (ISO-NE) data set demonstrates the superiority of Energy-Net over recent cutting-edge DL models with root mean-square error (RMSE) of 0.354 and 0.535, respectively. The computational complexity of Energy-Net is appropriate for dependable resource-constrained IoT devices (i.e., fog nodes or edge nodes) linked to a joint IoT-cloud server that interacts with connected smart grids to handle EM tasks.

The extensive propagation of industrial Internet of Things (IIoT) technologies has encouraged intruders to initiate a variety of attacks that need to be identified to maintain the security of end-user data and the safety of services offered by service providers. Deep learning (DL), especially recurrent approaches, has been applied successfully to the analysis of IIoT forensics but their key challenge of recurrent DL models is that they struggle with long traffic sequences and cannot be parallelized. Multihead attention (MHA) tried to address this shortfall but failed to capture the local representation of IIoT traffic sequences. In this article, we propose a forensics-based DL model (called Deep-IFS) to identify intrusions in IIoT traffic. The model learns local representations using local gated recurrent unit (LocalGRU), and introduces an MHA layer to capture and learn global representation (i.e., long-range dependencies). A residual connection between layers is designed to prevent information loss. Another challenge facing the current IIoT forensics frameworks is their limited scalability, limiting performance in handling Big IIoT traffic data produced by IIoT devices. This challenge is addressed by deploying and training the proposed Deep-IFS in a fog computing environment. The intrusion identification becomes scalable by distributing the computation and the IIoT traffic data across worker fog nodes for training the model. The master fog node is responsible for sharing training parameters and aggregating worker node output. The aggregated classification output is subsequently passed to the cloud platform for mitigating attacks. Empirical results on the Bot-IIoT dataset demonstrate that the developed distributed Deep-IFS can effectively handle Big IIoT traffic data compared with the present centralized DL-based forensics techniques. Further, the results validate the robustness of the proposed Deep-IFS across various evaluation measures.

In recent years, several multi-method and multi-operator-based algorithms have been proposed for solving optimization problems. Generally, their performance is better than other algorithms that based on a single operator and/or algorithm. However, they do not perform consistently well over all the problems tested in the literature. In this paper, we propose an improved optimization algorithm that uses the benefits of multiple differential evolution operators, with more emphasis placed on the best-performing operator. The performance of the proposed algorithm is tested by solving 10 problems with 5, 10, 15 and 20 dimensions taken from CEC2020 competition on single objective bound constrained optimization, with its results outperforming both single operator-based and different state-of-the-art algorithms.

Recently, many deferential evolution-based algorithms have been developed to solve constrained optimization problems. The performance of these methods outperforms the performance of single operator and/or algorithm-based ones. However, they do not perform consistently for all the problems tested in the literature. Also, the process of using the appropriate selection of algorithms and operators may be time-consuming since their designs are undertaken mainly through trial and error. In this paper, we propose an improved optimization algorithm that uses the benefits of multiple deferential evolution operators, with the best one is emphasized based on the quality and diversity of the population. The performance of the proposed algorithm is tested by solving 57 real-world constrained problems with different dimensions, number of equality and equality constraints, with its results showing a high success rate and that it outperformed different state-of-the-art algorithms.

The resource constraint project scheduling problem (RCPSP) is one of the complex combinatorial optimization problems. Several approaches have been proposed over the decades to solve RCPSPs optimally within reasonable computational times. In this paper, a genetic algorithm based memetic algorithm (MA) is proposed to solve RCPSP with makespan minimization as the objective. The proposed algorithm incorporates Nawaz, Enscore, and Ham (NEH) heuristic-based initialization with carefully designed crossover and local search techniques. The performance of the proposed approach is evaluated by solving 1560 problem instances from the popular project scheduling library (PSPLIB) and the results for different datasets have been compared with selected state-of-the-art algorithms. The comparison demonstrates the satisfactory performance of the proposed approach. Extensive experiments reveal that the proposed algorithm is very simple to implement and highly effective when compared to state-of-the-art methods.