Machine learning for internet of things classification using network traffic parameters

With the growth of the internet of things (IoT) smart objects, managing these objects becomes a very important challenge, to know the total number of interconnected objects on a heterogeneous network, and if they are functioning correctly; the use of IoT objects can have advantages in terms of comfort, efficiency, and cost. In this context, the identification of IoT objects is the first step to help owners manage them and ensure the security of their IoT environments such as smart homes, smart buildings, or smart cities. In this paper, to meet the need for IoT object identification, we have deployed an intelligent environment to collect all network traffic traces based on a diverse list of IoT in real-time conditions. In the exploratory phase of this traffic, we have developed learning models capable of identifying and classifying connected IoT objects in our environment. We have applied the six supervised machine learning algorithms: support vector machine, decision tree (DT), random forest (RF), k-nearest neighbors, naive Bayes, and stochastic gradient descent classifier. Finally, the experimental results indicate that the DT and RF models proved to be the most effective and demonstrate an accuracy of 97.72% on the analysis of network traffic data and more particularly information contained in network protocols. Most IoT objects are identified and classified with an accuracy of 99.21%

Artificial Intelligence in Green Management and the Rise of Digital Lean for Sustainable Efficiency

Artificial intelligence (AI) is a powerful management technology that offers analyzes and insights superior to those made by humans. It cuts costs and saves time by automating repetitive processes, forecasting customer demand and optimizing supply chains while taking into consideration their impact on sustainability and the environment. Organizations become more efficient through the integration of AI, which increases performance and decision-making. This essay examines the use of AI in management and the advent of digital lean, which combines lean manufacturing with technological innovation. The environmental benefits of AI, including energy efficiency and sustainable manufacturing, are also advocated. To realize the environmental sustainability benefits of AI, issues such as data privacy and scalability, as well as the need for responsible AI cooperation and practices, are highlighted

A modified sailfish optimizer to solve dynamic berth allocation problem in conventional container terminal

During the past two decades, there has been an increase on maritime freight traffic particularly in container flow. Thus, the Berth Allocation Problem (BAP) can be considered among the primary optimization problems encountered in port terminals. In this paper, we address the Dynamic Berth Allocation Problem (DBAP) in a conventional layout terminal which differs from the popular discrete layout terminal in that each berth can serve multiple vessels simultaneously if their total length is equal or less than the berth length. Then, a Modified Sailfish Optimizer (MSFO) meta-heuristic based on hunting sailfish behavior is developed as an alternative for solving this problem. Finally, computational experiments and comparisons are presented to show the efficiency of our method against other methods presented in the literature in one hand. We also discuss the productivity of a container terminal based on different scenarios which can happen

Artificial Intelligence in Green Management and the Rise of Digital Lean for Sustainable Efficiency

Artificial intelligence (AI) is a powerful management technology that offers analyzes and insights superior to those made by humans. It cuts costs and saves time by automating repetitive processes, forecasting customer demand and optimizing supply chains while taking into consideration their impact on sustainability and the environment. Organizations become more efficient through the integration of AI, which increases performance and decision-making. This essay examines the use of AI in management and the advent of digital lean, which combines lean manufacturing with technological innovation. The environmental benefits of AI, including energy efficiency and sustainable manufacturing, are also advocated. To realize the environmental sustainability benefits of AI, issues such as data privacy and scalability, as well as the need for responsible AI cooperation and practices, are highlighted

A New Approach for Sequencing Loading and Unloading Operations in the Seaside Area of a Container Terminal

Due to the considerable growth in the worldwide container transportation, optimization of container terminal operations is becoming highly needed to rationalize the use of logistics resources. For this reason, we focus our study on the Quay Crane Scheduling Problem (QCSP), which is a core task of managing maritime container terminals. From this planning problem arise two decisions to be made: The first one concerns tasks assignment to quay crane and the second one consists of finding the handling sequence of tasks such that the turnaround time of cargo vessels is minimized. In this paper, we provide a mixed-integer programming (MIP) model that takes into account non-crossing constraints, safety margin constraints and precedence constraints. The QCSP has been shown NP-complete, thus, we used the Ant Colony Optimization (ACO), a probabilistic technique inspired from ants’ behaviour, to find a feasible solution of such problem. The results obtained from the computational experiments indicate that the proposed method is able to produce good results while reducing the computational time