Journal of Emerging Technology and Digital Transformation

The Smart Shipbuilding: Integrating Lean Tools with Industry 4.0 for Agile High-Performance Production

2026-02-02T09:36:41+00:00

Shipbuilders around the world continue to face significant challenges stemming from prolonged lead times, delayed work orders, and high defect rates in processes such as block fabrication, outfitting, painting, and electrical works. These issues are largely attributed to outdated practices in design, planning, quality control, and resource utilization, many of which have remained unchanged for decades. While lean manufacturing has been successfully adopted in various industries, its implementation in shipbuilding remains limited due to the perception that lean tools are exclusive to automotive production systems, such as the Toyota Production System (TPS). Proposed study offers an advanced integrated methodology that combines lean tools, quality management systems (QMS), and Industry 4.0 technologies to improve operational efficiency in shipbuilding industry. The proposed model demonstrates enhanced responsiveness, flexibility, and performance where comparative analysis proves its effectiveness before and after implementation that confirms measurable improvements in productivity and defect reduction, supporting its applicability for modernization across the shipbuilding sector.

Urdu Fake News Detection Using LSTM and Hybrid CNN-LSTM Models

2026-02-05T10:51:41+00:00

Fake news is an increasing point of interest among the research community as it can be transmitted due to numerous media in one of the shortest periods of time. False information, particularly on those languages that lack substantial resources, has turned into a large issue that is increasingly getting worse due to social media and internet crimes. It is difficult to find fake news in Urdu as it is a complex language and there are not many label datasets, which is why it is not a well-researched discipline. The accuracy of the pre-existing machine learning studies in the field of the Urdu fake news detection has been insufficient. In their present work, the authors make use of the deep learning-based methodology of Long Short-Term Memory (LSTM) and a composite method (CNN-LSTM), i.e., both TF-IDF and word embeddings employing LSTMs to address this problem. The LSTM-based word embedding new method was doing significantly better than the best research, having an 95.85% accuracy and a 94.65% F1 score in the D2 dataset. The model was very accurate with 93.87% and F1 score of 94.21% on D1 data. These findings are an important step toward investigating fake news in Urdu and a promising source to reduce the negative impact of information warping in the online society.

Spatio-Temporal Traffic Prediction and Proactive Resource Management for Scalable LoRaWAN Networks

2026-02-05T10:36:24+00:00

Large scale Internet of Things (IoT) deployments such as Low Power Wide Area Networks (LPWANs) and the LoRaWAN have become central to the large-scale Internet of Things (IoT) deployment because of their long-range communications and low energy use. Nevertheless, the scalability and reliability of LoRaWAN networks is inherently limited by the unslotted ALOHA-based medium access scheme, fixed resource setup and severe regulatory constraints of duty-cycle. The difficulties are magnified in crowded and mobile setups like smart cities, where non-stationary and heavy traffic conditions result in severe packet collisions, high latency and poor quality of service (QoS). In this paper, a smart, predictive, and duty-cycle-aware resource management framework is proposed to be utilized by LoRaWAN networks that would reorganize network operation by transitioning it to reactive control to proactive decision-making. The proposed solution combines a hybrid Spatio-temporal traffic prediction model with Graph Convolutional Networks (GCN) and Gated Recurrent Units (GRU) as well as an adaptive resource management module that is placed at the edge of the network. Through learning of spatial contours between the entities in the network and time-based traffic dynamics, the framework effectively predicts short time congestion and pre-emptively modulates spreading factors, channels, and transmission scheduling and maintains the entire regulatory adherence. Extensive tests based on actual traffic traces and high-density simulation evidence that the suggested framework is far superior to the traditional LoRaWAN Adaptive Data Rate (ADR) schemes and the established machine learning model. GNCN-GRU model provides a 18% decrease in the error of traffic prediction relative to the conventional recurrent models with the resource adaptation being proactive which minimizes the packet collisions by up to 30 percent in the ultra-dense situation. Besides, the framework maintains up to 21% increase in the ratio of packet delivery at the 1000 nodes per gateway, and it ensures a sub-500ms latency of mission-critical traffic despite a rigid duty-cycle limit. The experiments of edge deployment prove the viability of the method, with a latency of inference of less than 42ms and a minimum of computational cost. Comprehensively, the findings indicate that edge Spatio-temporal intelligence is feasible and applicable to scalable, reliable, and regulation-friendly LoRaWAN operation, and the next-generation smart city and industrial IoT applications are feasible.

Effective Rainfall Prediction LSTM Model for Enhancing Textile Industry Sustainability

2026-02-16T13:50:22+00:00

This study presents an innovative deep learning model designed to accurately predict rainfall patterns, aimed at enhancing the sustainability of the textile industry. By leveraging advanced machine learning techniques, the model analyzes historical weather data, climatic factors, and seasonal trends to provide reliable rainfall forecasts. This research article focuses on predicting rainfall frequency using deep learning and suggests measures that can be taken by the Pakistan cities Faisalabad, Multan and Tando Adam textile industries to minimize the associated negative effects. Rainfall prediction is important for decision-making among stakeholders who are affected by wet weather conditions. These predictions enable textile manufacturers to optimize water usage, reduce waste, and plan production schedules more effectively. After selecting relevant key performance indicators and using this data to train weighted and stateful LSTM and CNN models, a validation accuracy of between 89% and 100%, precision of 83%, and a recall of 86% was predicted for various classes, which almost matches the ground truth data of two years of weather data. The results showed that LSTMs and CNNs can provide high performance in real-time rainfall prediction applications. The integration of this predictive model not only fosters resource efficiency but also supports the industry's transition towards sustainable practices in the face of climate variability. The findings of Random Forest, XGB Regression, and Decision Boosted Tree machine learning models underscore in addressing environmental challenges within the textile sector.

Machine Learning-Based IoT Device Fingerprinting for Enhanced Network Intrusion Detection

2026-02-05T10:45:56+00:00

Even though the IoT devices have brought about improvements in the networks, they have also caused new challenges in detecting the threats at the network level. Because IoT devices are so far and wide, traditional intrusion detection systems are not likely to identify most of their threats. An approach to IoT devices fingerprinting based on machine learning is proposed here to make network-level intrusion detection more accurate and efficient. The system identifies unique characteristics of devices in network traffic which It uses to categorize IoT devices and identify nefarious actions. This occurs by analyzing characteristics such as the communication method, types of traffic it carries and the supported protocols. traffic is classified into various categories by using Random Forest (RF), Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN) algorithms. The evaluation of the performances reveals that the system suggested in this project improves the correct detection, reduces the occurrence of false alarms and improves the overall performance of IDS used in IoT networks. Applying device fingerprinting and machine learning helps to establish a strong protection in the IoT ecosystem, according to the research.