Cryptocurrency price prediction poses significant challenges due to the inherent volatility and nonlineardynamics of the market. This study introduces a hybrid stacked modeling framework that integrates machine learning (ML) and deep learning (DL) techniques, capitalizing on their complementary strengths-ML models are effective at capturing nonlinearfeature interactions in structured data, while DL architectures are adept at modeling temporal dependencies in sequential data. The proposed model leverages historical price data, technical indicators, macroeconomic variables, and sentiment metrics, with feature engineering applied to enhance predictive capability. Empirical evaluation was conducted through two experimental setups: (i) short-term, monthly segment analysis and (ii) long-term generalization via five-fold cross-validation. The hybrid model outperformed individual baseline models, achieving up to 18.3% lower RMSE and 6.7% higher directional accuracy. Additionally, it yielded superior risk-adjusted returns, with Sharpe Ratios reaching 0.094 on the Ethereum dataset. Beyond technical improvements, this research offers foresight into digital financial markets, providing a robust tool for investors, institutions, and policymakers navigating the evolving cryptocurrency landscape. The model supports more informed decision-making, enhances market oversight, and contributes to the development of adaptive regulatory frameworks for digital finance.

Efficient container handling and early damage detection are critical for minimizing operational delays, reducing costs, and ensuring safety in global maritime logistics.This work presents a deep learning-based methodology for real-time container tracking and automated damage detection during crane unloading operations at container terminals.We develop and deploy two specialized YOLOv12-based object detection models: one for identifying containers in motion and another for detecting structural damages such as bents, dents, and holes.Our models are trained and evaluated on a real-world dataset curated from video feeds captured at the EUROGATE Container Terminal in Limassol, Cyprus.The system is designed for robust performance under realistic terminal conditions, including variable lighting and motion.Our models achieve high detection accuracy, with a mAP50 of 0.99 for container detection and 0.75 for damage detection, substantially outperforming existing benchmarks.These results highlight the practical potential of our method for improving efficiency and safety in automated maritime logistics.

This study investigates the combined berth allocation problem (BAP) and quay crane allocation problem (QCAP) while considering a multi-quay setting. First, a mixed integer linear programming mathematical model is developed based on various constraints and real port settings. Then, the multi-quay combined BAP and QCAP is solved using both the exact method and a metaheuristic optimization method, namely, the cuckoo search algorithm (CSA). This analysis pertains to a one-week planning scenario, utilizing data from a real port. The results of the comparative analysis show that the proposed CSA can provide a near-optimal solution (< 1.02% from the optimal) at a fraction of the computational time (10 times faster), as compared to the exact solution. This makes it suitable for solving larger instances of the combined BAP and QCAP for bigger terminals and extended planning horizons.

The Internet of things (IoT) ecosystem provides a platform for the connectivity of interrelated smart devices to automate manual processes and reduce labor costs.IoT has brought significant benefits to all industries, including maritime, as various objects (e.g., ports, ships, agents, etc.) are connected to gather and share information within the maritime ecosystem.The innovative technological aspects of IoT are promoting the effective collaboration between the research community and the maritime industry, for enhancing the performance of maritime transportation systems.Therefore, this study discusses recent advances delivered by the IoT and other emerging technologies, like machine learning (ML) and computer vision (CV), for smart maritime transportation systems (SMTSs).In particular, this paper presents two specific use cases of SMTSs, namely, predictive maintenance and container damage/seal inspection.Moreover, the key benefits of integrating IoT with ML and CV are highlighted for the above-mentioned use cases.Finally, a discussion is presented to highlight key opportunities along with foreseeable future challenges in adopting these new technologies by the maritime industry.

Proper utilization of port resources and efficient berth planning play a crucial role in minimizing port congestion and overall handling costs. Therefore, this study focuses on efficient berth planning in maritime container terminals composed of multiple quays. In particular, this study addresses the Multi-Quay Berth Allocation Problem (MQ-BAP), where a continuous berthing layout is considered along with dynamic ship arrivals and practical constraints such as safety time windows and safety distances between ships. Since MQ-BAP is an NP-hard problem, this study proposes a metaheuristic-based approach, the Cuckoo Search Algorithm (CSA) for solving the problem. A comparative study is also performed using real data instances collected from the Port of Limassol, Cyprus, against a genetic algorithm solution proposed in the recent literature, as well as the optimal exact solution implemented using MILP. The results of the experiments show the effectiveness of our proposed CSA approach in handling real-world berth allocation in ports with multiple quays while also considering practical constraints.

Over the last couple of decades, demand for seaborne containerized trade has increased significantly and it is expected to continue growing over the coming years. As an important node in the maritime industry, a maritime container terminal (MCT) should be able to tackle the growing demand for sea trade. Due to the increased number of ships that can arrive simultaneously at an MCT combined with inefficient berth allocation procedures, there are often undesirable situations when the ships have to stay in waiting queues and delay both their berthing and departure. In order to improve port efficiency in terms of reducing the total handling cost and late departures, this study investigates the dynamic and continuous berth allocation problem (DC-BAP), where vessels are assigned dynamically as they arrive at their berth locations assuming a continuous berth layout. First, the DC-BAP is formulated as a mixed-integer linear programming (MILP) model. Since BAP is an NP-hard problem and cannot be solved by mathematical approaches in a reasonable time, this study adopts the recently developed metaheuristic cuckoo search algorithm (CSA) to solve the DC-BAP. For validating the performance of the proposed CSA method, we use a benchmark case study and a genetic algorithm solution proposed in recent literature as well as compare our results against the optimal MILP solution. From the simulation results, it becomes evident that the newly proposed algorithm has higher efficiency over counterparts in terms of optimal berth allocation within reasonable computation time.

Wireless Sensor Networks (WSNs) provide noteworthy advantages over conventional methods for various real-time applications, i.e., healthcare, temperature sensing, smart homes, homeland security, and environmental monitoring. However, limited resources, short life-time network constraints, and security vulnerabilities are the challenging issues in the era of WSNs. Besides, WSNs performance is susceptible to network anomalies, particularly to misdirection attacks. The above-mentioned issues pose our attentions to produce a security-aware application. In this work, therefore, we present a Reinforcement Learning (RL) algorithm for Misdirection Attack Detection and Prevention (RL-MADP) in WSNs. In our proposed approach, other than the flat architecture configuration for WSN, Markov Decision Process (MDP) from RL is considered. Where, each sensor node is fully aware of its environment. It is an online method and incurs minimal computation cost, and performs load-balancing with higher residual energy to prolong the network lifetime.

The 5th generation (5G) of communication networks will facilitate innovative and emerging services and applications having lower latency requirements, increased energy efficiency and reliability. These characteristics of 5G make it capable to act as a potential underlying network for smart city services such as for implementation of demand response in smart grids. More specifically, in terms of demand response, these low latency networks are used for the explicit exchange of messages between utility companies and customers for pricing mechanisms. According to the time to use (ToU) pricing scheme, consumers are offered a specific electricity price for each time interval i.e., off-peak, on-peak and mid-peak blocks. Unlike high energy consumers (HECs), low energy consumers (LECs) are not the reason of high peaks (on-peaks) formation; however, they pay higher rates to the utility during on-peak hours because of one price for all rule. Here, one price for all makes ToU an unjustified pricing scheme. This issue is discussed in this study and a fair pricing scheme is proposed to remove undue financial burden from LECs. The proposed fair pricing scheme (FPS) is based on energy consumption of each category customer. LECs and HECs pay the electricity bill exactly according to their electricity consumption and no one has to bear the financial load of others. Simulation results show that LECs are able to save up to 11.0075% of their total electricity bill. HECs has to pay the penalty of high energy consumption whereas, the utility company is not affected by the implementation of the proposed fair pricing scheme.

The recent emergence of Internet-of-Things (IoT) technologies in mission-critical applications in the maritime industry has led to the introduction of the Internet-of-Ships (IoS) paradigm. IoS is a novel application domain of IoT that refers to the network of smart interconnected maritime objects, which can be any physical device or infrastructure associated with a ship, a port, or the transportation itself, with the goal of significantly boosting the shipping industry toward improved safety, efficiency, and environmental sustainability. In this article, we provide a comprehensive survey of the IoS paradigm, its architecture, its key elements, and its main characteristics. Furthermore, we review the state of the art for its emerging applications, including safety enhancements, route planning and optimization, collaborative decision making, automatic fault detection and preemptive maintenance, cargo tracking, environmental monitoring, energy-efficient operations, and automatic berthing. Finally, the presented open challenges and future opportunities for research in the areas of satellite communications, security, privacy, maritime data collection, data management, and analytics, provide a road map toward optimized maritime operations and autonomous shipping.

In this paper, we propose a home energy management (HEM) scheme in the residential area for electricity cost and peak to average ratio (PAR) reduction. Furthermore, reduction in imported electricity from the external grid is also the objective of this study. Our proposed scheme schedules smart appliances as well as electrical vehicles (EVs) charging\discharging optimally according to the consumer preferences. Each consumer has its own grid-connected microgrid for electricity generation; which consists of wind turbine, solar panel, micro gas turbine (MGT) and energy storage system (ESS). Furthermore, the scheduling problem is mathematically formulated and solved by mixed integer linear programming (MILP). We also provide the comparison of the optimal solutions, while considering EVs with and without discharging capabilities. Findings from simulations affirm our proposed scheme in terms of above-mentioned objectives.