Predictive maintenance (PdM) is essential for reducing equipment downtime and enhancing operational efficiency. However, PdM datasets frequently suffer from significant class imbalance and are often limited to single-label classification, which fails...
This paper evaluates data stream classifiers from the perspective of connected devices, focusing on the use case of Human Activity Recognition. We measure both the classification performance and resource consumption (runtime, memory, and power) of fi...
Several malware variants have attacked systems and data over time. Ransomware is among the most harmful malware since it causes huge losses. In order to get a ransom, ransomware is software that locks the victim’s machine or encrypts his person...
Network intrusion, such as denial of service, probing attacks, and phishing, comprises some of the complex threats that have put the online community at risk. The increase in the number of these attacks has given rise to a serious interest in the res...
The integration of renewable energy into residential microgrids presents significant challenges due to solar generation intermittency and variability in household electricity demand. Traditional forecasting methods, reliant on historical data, fail t...
Data prefetching is essential for modern file storage systems operating in large-scale cloud and data-intensive environments, where high performance increasingly depends on intelligent, adaptive mechanisms. Traditional rule-based methods and recently...
Cyber-attack detection via on-gadget embedded models and cloud systems are widely used for the Internet of Medical Things (IoMT). The former has a limited computation ability, whereas the latter has a long detection time. Fog-based attack detection i...
As new cyberattacks are launched against systems and networks on a daily basis, the ability for network intrusion detection systems to operate efficiently in the big data era has become critically important, particularly as more low-power Internet-of...
The number of cyber-attacks is increasing daily, and attackers are coming up with new ways to harm their target by disseminating viruses and other malware. With new inventions and technologies appearing daily, there is a chance that a system might be...
Diagnostic practices for schizophrenia are unreliable due to the lack of a stable biomarker. However, machine learning holds promise in aiding in the diagnosis of schizophrenia and other neurological disorders. Dysregulated miRNAs were extracted from...
Currently, the computational power present in the sensors forming a wireless sensor network (WSN) allows for implementing most of the data processing and analysis directly on the sensors in a decentralized way. This shift in paradigm introduces a shi...
Power system failures or outages due to short-circuits or “faults” can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteris...
Network selection plays a pivotal role in ensuring efficient handover management. Some existing approaches for network selection may use one criterion, such as RSSI (Received Signal Strength Indicator) or SINR (Signal to Interference Noise Ratio). Ho...
The rapid growth of Internet of Medical Things (IoMT) devices has revolutionized diagnostics and patient care within smart healthcare networks. However, this progress has also expanded the attack surface due to the heterogeneity and interconnectivity...
In botany and agriculture, classifying leaves is a crucial process that yields vital information for studies on biodiversity, ecological studies, and the identification of plant species. The Cope Leaf Dataset offers a comprehensive collection of leaf...
Wetlands are important environmental resources that are vulnerable to droughts and floods. Studying drought-flood events and their driving factors is essential for wetland resource planning and management. However, climate change and human activities...
The aim of this study is to compare the classification accuracy depending on the number of texture features used. This study used 400 computed tomography (CT) images of trabecular spinal tissue from 100 patients belonging to two groups (50 control pa...
Machine learning applications are having a great impact on the global economy by transforming the data processing method and decision making. Agriculture is one of the fields where the impact is significant, considering the global crisis for food sup...
Due to the increasing urban development, it has become important for municipalities to permanently understand land use and ecological processes, and make cities smart and sustainable by implementing technological tools for land monitoring. An importa...