A. Reyana, Sandeep Kautish, A.S. Vibith and S.B. Goyal
In the traffic monitoring system, the detection of stirring vehicles is monitored by fitting static cameras in the traffic scenarios. Background subtraction a commonly used method…
Abstract
Purpose
In the traffic monitoring system, the detection of stirring vehicles is monitored by fitting static cameras in the traffic scenarios. Background subtraction a commonly used method detaches poignant objects in the foreground from the background. The method applies a Gaussian Mixture Model, which can effortlessly be contaminated through slow-moving or momentarily stopped vehicles.
Design/methodology/approach
This paper proposes the Enhanced Gaussian Mixture Model to overcome the addressed issue, efficiently detecting vehicles in complex traffic scenarios.
Findings
The model was evaluated with experiments conducted using real-world on-road travel videos. The evidence intimates that the proposed model excels with other approaches showing the accuracy of 0.9759 when compared with the existing Gaussian mixture model (GMM) model and avoids contamination of slow-moving or momentarily stopped vehicles.
Originality/value
The proposed method effectively combines, tracks and classifies the traffic vehicles, resolving the contamination problem that occurred by slow-moving or momentarily stopped vehicles.
Details
Keywords
Bernardo Nicoletti and Andrea Appolloni
The paper uses foundation models to integrate the green approach in Logistics 5.0. Such integration is innovative in logistics and leads to a more sustainable and prosperous…
Abstract
Purpose
The paper uses foundation models to integrate the green approach in Logistics 5.0. Such integration is innovative in logistics and leads to a more sustainable and prosperous future. By harnessing the power of foundation models and incorporating sustainable principles, this paper can systematize the logistics industry’s environmental framework, increase its social responsibility and ensure its long-term economic viability.
Design/methodology/approach
Generalizing environmental sustainability goals requires a multi-layered innovation approach incorporating corporate philosophy, products, processes and business models. In this paper, this comprehensive approach is not just a strategy but a necessity in the current global context. This paper uses the sustainability-oriented innovation (SOI) method, crucial for achieving explicit environmental, social and economic impacts.
Findings
Artificial intelligence, especially foundation models, can contribute to green logistics by optimizing routes, reducing packaging waste, improving warehouse layouts and other functions presented in the paper. At the same time, they can also consider social, economic and governance goals.
Research limitations/implications
Artificial intelligence algorithms present challenges such as high initial investment, regulatory compliance and technological integration.
Practical implications
The paper contains implications for developing environmentally sustainable logistics, which is currently one of the most significant challenges. The framework presented can apply to logistics companies.
Originality/value
This paper fulfills an identified need to study sustainability in logistics. The framework is entirely original and not present in the literature. It is essential to help design and implement innovative logistics approaches.
Details
Keywords
Budati Anil Kumar, George Ghinea, S.B. Goyal, Krishna Kant Singh and Shayla Islam
Mazen El-Masri and Eiman Mutwali Abdelmageed Hussain
Blockchain is evolving to become a platform for securing Internet of things (IoT) ecosystems. Still, challenges remain. The purpose of this literature review is to highlight the…
Abstract
Purpose
Blockchain is evolving to become a platform for securing Internet of things (IoT) ecosystems. Still, challenges remain. The purpose of this literature review is to highlight the applicability of blockchain as a medium to secure IoT ecosystems. A two-dimensional framework anchored on (1) IoT layers and (2) security goals is used to organize the existent IoT security threats and their corresponding countermeasures identified in the reviewed literature. The framework helped in mapping the IoT security threats with the inherent features of blockchain and accentuate their prominence to IoT security.
Design/methodology/approach
An approach integrating computerized natural language processing (NLP) with a systematic literature review methodology was adopted. A large corpus of 2,303 titles and abstracts of blockchain articles was programmatically analyzed in order to identify the relevant literature. The identified literature was subjected to a systematic review guided by a well-established method in IS research.
Findings
The literature evidently highlights the prominence of blockchain as a mean to IoT security due to the distinctive features it encompasses. The authors’ investigation revealed that numerous existent threats are better addressed with blockchain than conventional mechanisms. Nevertheless, blockchain consumes resources such as electricity, time, bandwidth and disk space at a rate that is not yet easily accessible to common IoT ecosystems.
Research limitations/implications
Results suggest that a configurational approach that aligns IoT security requirements with the resource requirements of different blockchain features is necessary in order to realize the proper balance between security, efficiency and feasibility.
Practical implications
Practitioners can make use of the classified lists of convention security mechanisms and the IoT threats they address. The framework can help underline the countermeasures that best achieve their security goals. Practitioners can also use the framework to identify the most important features to seek for in a blockchain technology that can help them achieve their security goals.
Originality/value
This study proposes a novel framework that can help classify IoT threats based on the IoT layer impacted and the security goal at risk. Moreover, it applies a combined man-machine approach to systematically analyze the literature.