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5G Communication Infrastructure for Smart Grids: A Protection Use Case
(IEEE, 26.10.2023)
articleA smart grid is an advanced electricity network enabled by the Internet of Things (IoT), Information and Communication Technology (ICT), and advanced grid technologies. 5G and beyond networks are expected to serve as the ... -
A Review on the Classification of Partial Discharges in Medium-Voltage Cables : Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques
(MDPI, 28.02.2024)
articleMedium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) ... -
A Systematic Review of Machine Learning Techniques for GNSS Use Cases
(IEEE, 03.11.2022)
articleIn terms of the availability and accuracy of positioning, navigation, and timing (PNT), the traditional Global Navigation Satellite System (GNSS) algorithms and models perform well under good signal conditions. In order ... -
Achievable rate approximation for massive MIMO with limited number of interfering clients
(Springer, 15.01.2022)
articleMassive MIMO has become a core technology for the next generation of wireless communications, and the non-linear group decoding schemes can achieve better spectral and energy efficiency, which in turn leads to higher data ... -
Advanced-stage tongue squamous cell carcinoma : a machine learning model for risk stratification and treatment planning
(Taylor & Francis, 15.02.2023)
articleBackground A significant number of tongue squamous cell carcinoma (TSCC) patients are diagnosed at late stage. Objectives We primarily aimed to develop a machine learning (ML) model based on ensemble ML paradigm to ... -
Advancing Sustainable Maritime with AI/ML Enhanced Hardware-in-the-Loop Testing
(IEEE, 25.06.2024)
articleThis paper explores the potential of Hardware-in-the-Loop (HIL) testing and simulations in advancing sustainable maritime. HIL testing is a technique that combines physical components and a virtual real-time system. HIL ... -
An Extreme Learning Machine Model for Venue Presence Detection
(Springer, 19.01.2023)
bookPartValue-added services allocation or denial in a particular venue for a given user is of high significance. It will get more prominent as we move to 5G and 6G networks’ roll out, as we will get other means to have better ... -
An interpretable machine learning prognostic system for risk stratification in oropharyngeal cancer
(Elsevier, 13.10.2022)
articleBackground The optimal management of oropharyngeal squamous cell carcinoma (OPSCC) includes both surgical and non-surgical, that is, (chemo)radiotherapy treatment options and their combinations. These approaches carry ... -
Application of artificial intelligence for overall survival risk stratification in oropharyngeal carcinoma : A validation of ProgTOOL
(Elsevier, 23.04.2023)
articleBackground In recent years, there has been a surge in machine learning-based models for diagnosis and prognostication of outcomes in oncology. However, there are concerns relating to the model’s reproducibility and ... -
Application of Machine Learning to GNSS/IMU Integration for High Precision Positioning on Smartphone
(The Institute of Navigation, 09 / 2022)
articleThis paper describes our solution for the Google smartphone decimeter challenge (GSDC), which was held from May to August 2022. The GSDC is a competition for improving positioning accuracy of smartphones. The global ... -
Artificial Intelligence-Based Condition Monitoring and Predictive Maintenance of Medium Voltage Cables : An Integrated System Development Approach
(IEEE, 03.12.2024)
articleIn order to minimize power supply outages in electrical distribution systems, the reliable operation of medium-voltage (MV) cables is of paramount importance. These cables may experience unplanned downtime and failures, ... -
Artificial Intelligence-Driven Radiomics in Head and Neck Cancer : Current Status and Future Prospects
(Elsevier, 09.05.2024)
articleBackground Radiomics is a rapidly growing field used to leverage medical radiological images by extracting quantitative features. These are supposed to characterize a patient’s phenotype, and when combined with artificial ... -
Benchmarking Q-learning methods for intelligent network orchestration in the edge
(IEEE, 13.05.2020)
conferenceObjectWe benchmark Q-learning methods, with various action selection strategies, in intelligent orchestration of the network edge. Q-learning is a reinforcement learning technique that aims to find optimal action policies by ... -
Cancer Modeling-on-a-Chip with Future Artificial Intelligence Integration
(Wiley, 13.11.2019)
articleCancer is one of the leading causes of death worldwide, despite the large efforts to improve the understanding of cancer biology and development of treatments. The attempts to improve cancer treatment are limited by the ... -
Clinical significance of tumor-stroma ratio in head and neck cancer : a systematic review and meta-analysis
(BioMed Central, 30.04.2021)
articleBackground The clinical significance of tumor-stroma ratio (TSR) has been examined in many tumors. Here we systematically reviewed all studies that evaluated TSR in head and neck cancer. Methods Four databases (Scopus, ... -
Collaborative machine learning-guided overall survival prediction of oral squamous cell carcinoma
(Taylor & Francis, 31.12.2024)
articleBackground There is a lack of prognosticators of overall survival (OS) for Oral Squamous Cell Carcinoma (OSCC). Objectives We examined collaborative machine learning (cML) in estimating the OS of OSCC patients. The ... -
Comparison Between High Throughput and Efficiency of 802.11 Wireless Standards
(IEEE, 30.12.2022)
articleThe High-Efficiency (HE) standard is considered as generation Wi-FI 6, which is an improvement of legacy 802.11 standards. The new Wi-Fi 6 of 802.11ax standard aims to improve throughput and minimize latency. One of the ... -
Comparison of nomogram with machine learning techniques for prediction of overall survival in patients with tongue cancer
(Elsevier, 01.01.2021)
articleBackground: The prediction of overall survival in tongue cancer is important for planning of personalized care and patient counselling. Objectives: This study compares the performance of a nomogram with a machine learning ... -
Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer
(Elsevier, 01.04.2020)
articleBackground The proper estimate of the risk of recurrences in early-stage oral tongue squamous cell carcinoma (OTSCC) is mandatory for individual treatment-decision making. However, this remains a challenge even for ... -
Cyber Security in Power Systems Using Meta-Heuristic and Deep Learning Algorithms
(IEEE, 22.02.2023)
articleSupervisory Control and Data Acquisition system linked to Intelligent Electronic Devices over a communication network keeps an eye on smart grids’ performance and safety. The lack of algorithms protecting the power system ...
