Browsing Faculty of Engineering by Subject "Machine learning"
Now showing items 1-20 of 23
-
Advancing home healthcare through machine learning: Predicting service time for enhanced patient care
(IEEE, 2023)Providing healthcare services at home is crucial for patients who require long-term care or face mobility or other health-related constraints that prevent them from traveling to healthcare facilities. Effective data analysis ... -
Automated defect prioritization based on defects resolved at various project periods
(Elsevier, 2021-09)Defect prioritization is mainly a manual and error-prone task in the current state-of-the-practice. We evaluated the effectiveness of an automated approach that employs supervised machine learning. We used two alternative ... -
Comparative study of credit risk evaluation for unbalanced datasets using deep learning classifiers
(IEEE, 2023)Credit risk assessment deals with calculating the risk of a loan not being repaid. For this reason, a lot of research effort is directed at credit risk analysis. In this study, machine learning models such as Light ... -
A data-driven matching algorithm for ride pooling problem
(Elsevier, 2022-04)This paper proposes a data-driven matching algorithm for the problem of ride pooling, which is a transportation mode enabling people to share a vehicle for a trip. The problem is considered as a variant of matching problem, ... -
DiBLIoT: A distributed blacklisting protocol for iot device classification using the hashgraph consensus algorithm
(IEEE Computer Society, 2022)Industrial applications require highly reliable, secure, low-power and low-delay communications. However, wireless communication links in the industrial environment suffer from various channel impairments which can compromise ... -
DILAF: A framework for distributed analysis of large-scale system logs for anomaly detection
(Wiley, 2019-02)System logs constitute a rich source of information for detection and prediction of anomalies. However, they can include a huge volume of data, which is usually unstructured or semistructured. We introduce DILAF, a framework ... -
Disentangling human trafficking types and the identification of pathways to forced labor and sex: an explainable analytics approach
(Springer, 2023-07)Terms such as human trafficking and modern-day slavery are ephemeral but reflect manifestations of oppression, servitude, and captivity that perpetually have threatened the basic right of all humans. Operations research ... -
Evaluation of distributed machine learning algorithms for anomaly detection from large-scale system logs: a case study
(IEEE, 2018)Anomaly detection is a valuable feature for detecting and diagnosing faults in large-scale, distributed systems. These systems usually provide tens of millions of lines of logs that can be exploited for this purpose. ... -
Evaluation of linguistic and prosodic features for detection of Alzheimer’s disease in Turkish conversational speech
(Springer Science+Business Media, 2015-12)Automatic diagnosis and monitoring of Alzheimer’s disease can have a significant impact on society as well as the well-being of patients. The part of the brain cortex that processes language abilities is one of the earliest ... -
Feature extraction for enhancing data-driven urban building energy models
(European Council on Computing in Construction (EC3), 2023)Building energy demand assessment plays a crucial role in designing energy-efficient building stocks. However, most studies adopting a data-driven approach feel the deficiency of datasets with building-specific information ... -
Finecloud: Fine-grained cloud service advisory using machine learning
(IEEE, 2022)Motivated by real customer problems, we investigated utilization of cloud services at different layers including infrastructure (IaaS), application services (PaaS) and databases (DaaS). We found several issues such as ... -
Incremental analysis of large-scale system logs for anomaly detection
(IEEE, 2019)Anomalies during system execution can be detected by automated analysis of logs generated by the system. However, large scale systems can generate tens of millions of lines of logs within days. Centralized implementations ... -
Land subsidence susceptibility mapping using interferometric synthetic aperture radar (InSAR) and machine learning models in a semiarid region of iran
(MDPI, 2023-04)Most published studies identify groundwater extraction as the leading cause of land subsidence (LS). However, the causes of LS are not only attributable to groundwater extraction. Other land-use practices can also affect ... -
Machine learning to predict junction temperature based on optical characteristics in solid-state lighting devices: A test on WLEDs
(MDPI, 2022-08)While junction temperature control is an indispensable part of having reliable solid-state lighting, there is no direct method to measure its quantity. Among various methods, temperature-sensitive optical parameter-based ... -
MaLeFICE: Machine learning support for continuous performance improvement in computational engineering
(Wiley, 2022-04-25)Computer aided engineering (CAE) practices improved drastically within the last decade due to ease of access to computing resources and open-source software. However, increasing complexity of hardware and software settings ... -
Minimizing false positive rate for DoS attack detection: A hybrid SDN-based approach
(Elsevier, 2020-06)Denial of Service attacks (DoS) are considered to be a major threat against today's communication networks. Recently, a novel networking paradigm that provides enhanced programming abilities has been proposed to attain an ... -
A new deep learning restricted boltzmann machine for energy consumption forecasting
(MDPI, 2022-08)A key issue in the desired operation and development of power networks is the knowledge of load growth and electricity demand in the coming years. Mid-term load forecasting (MTLF) has an important rule in planning and ... -
A new era of modeling MOF-based membranes: Cooperation of theory and data science
(Wiley, 2024-01)Membrane-based separation can offer significant energy savings over conventional separation methods. Given their highly customizable and porous structures, metal–organic frameworks- (MOFs) are considered as next-generation ... -
On the use of machine learning for predicting defect fix time violations
(Science and Technology Publications, 2022)Accurate prediction of defect fix time is important for estimating and coordinating software maintenance efforts. Likewise, it is useful to predict whether or not the initially estimated defect fix time will be exceeded ... -
A platform for personal e-mobility with route forecasting
(IEEE, 2020)This study reports a new e-mobility platform to construct effective usage of charging points by electric vehicle users to eliminate long charge durations. The e-mobility platform includes such subsystems as smartphones, ...
Share this page