Harris Papadopoulos

Frederick University · Department of Computer Science and Engineering

Central to the development of a successful waste sorting robot lies an accurate and fast object detection system. This study assesses the performance of the most representative deep-learning models for the real-time localisation and classification of Construction and Demolition Waste (CDW). For the investigation, both single-stage (SSD, YOLO) and t...

Unlike the typical classification setting where each instance is associated with a single class, in multi-label learning each instance is associated with multiple classes simultaneously. Therefore the learning task in this setting is to predict the subset of classes to which each instance belongs. This work examines the application of a recently de...

Interference from other users, due to the long-distance propagation of HF signals, causes spectral congestion, which is considered a primary detrimental effect on HF (3–30 MHz) communication systems. More than a year of HF electric field measurements from a calibrated monopole HF antenna collected by a dedicated measurement system installed in Cypr...

This demonstration paper introduces the Smart Out-of-Home Advertising Platform (SOAP), which leverages Geographic Information Systems (GIS) data and state-of-the-art Artificial Intelligence (AI) approaches to provide: (i) a documented, data-informed pricing model for billboards, which can be used to justify billboard prices to advertisers; and (ii)...

This paper presents a Multi-Objective Optimization (MOO) approach for Out-of-Home (OOH) advertising campaign billboard selection. In particular, it exploits a large variety of features from different sources, such as Geographic Information Systems (GIS) and demographics data, for the construction of billboard profiles that take into account all fac...

We extend our previous work on Inductive Conformal Prediction (ICP) for multi-label text classification and present a novel approach for addressing the computational inefficiency of the Label Powerset (LP) ICP, arrising when dealing with a high number of unique labels. We present experimental results using the original and the proposed efficient LP...

We deal with the Natural Language Processing (NLP) task of Sentiment Analysis (SA) ontext, by applying Inductive Conformal Prediction (ICP) on a transformers based model.SA, which is the interpretation and classification of emotions, also referred to as emotionalartificial intelligence, can be set up as a Text Classification (TC) problem. Transform...

Over the last years, Indoor Localization Systems (ILS) evolved, due to the inability of Global Positioning Systems (GPS) to localize in indoor environments. A variety of studies tackle indoor localization with technologies such as Bluetooth Beacons and RFID that require costly installation, or techniques such as Google Wi-Fi/Cell DB and fingerprint...

Indoor localization systems (ILS) evolved over the last years, mainly due to the fact that Global Positioning Systems (GPS) lack precision or fail entirely to localize smartphone users in indoor environments. Many studies attempted to alleviate this issue by utilizing techniques developed on top of technologies, such as Bluetooth beacons and RFID t...

Automatic face recognition (AFR) has gained the attention of many institutes and researchers in the past two decades due to its wide range of applications. This attention resulted in the development of a variety of techniques for the particular task with a high recognition accuracy when the environment is well-controlled. In the case of moderately...

This study examines the use of the Conformal Prediction (CP) framework for the provision of confidence information in the detection of seizures in electroencephalograph (EEG) recordings. The detection of seizures is an important task since EEG recordings of seizures are of primary interest in the evaluation of epileptic patients. However, manual re...

The impressive growth of smartphone devices in combination with the rising ubiquity of using mobile platforms for sensitive applications such as Internet banking, have triggered a rapid increase in mobile malware. In recent literature, many studies examine Machine Learning techniques, as the most promising approach for mobile malware detection, wit...

We propose an approach for providing well-calibrated confidence measures for determining cerebrovascular risk stratification based on characteristics from noninvasive ultrasound imaging of carotid plaques. An important challenge we address is the class imbalance problem inherent in the particular task. The proposed approach is based on a novel fram...

This paper presents an analysis of the characteristics of HF (1.6-30 MHz) spectral occupancy and describes the development of neural network models to predict the likelihood of interference in the HF spectrum over a significant part of Northern Europe. The analysis and developed models are based upon several years of 24-h measurements recorded at f...

The evolution of ubiquitous smartphone devices has given rise to great opportunities with respect to the development of applications and services, many of which rely on sensitive user information. This explosion on the demand of smartphone applications has made them attractive to cybercriminals that develop mobile malware to gain access to sensitiv...

The Conformal Prediction (CP) framework can be used for obtaining reliable confidence measures in Machine Learning applications. The confidence measures are guaranteed to be valid under the assumption that the data used are identically and independently distributed (i.i.d.). In this work, we extend the CP framework for multi-label classification, w...

Non-invasive ultrasound imaging of carotid plaques can provide information on the characteristics of the arterial wall including the size and consistency of atherosclerotic plaques. As presented until now this can be used to determine cerebrovascular risk stratification. The aim of this study is to continue the effort being done to estimate the ris...

Vesicoureteral Reflux (VUR) is a pediatric disorder in which urine flows backwards from the bladder to the upper urinary tract. Its detection is of great importance as it increases the risk of a Urinary Tract Infection, which can then lead to a kidney infection since bacteria may have direct access to the kidneys. Unfortunately the detection of VUR...

Cross-Conformal Prediction (CCP) is a recently proposed approach for overcoming the computational inefficiency problem of Conformal Prediction (CP) without sacrificing as much informational efficiency as Inductive Conformal Prediction (ICP). In effect CCP is a hybrid approach combining the ideas of cross-validation and ICP. In the case of classific...

The impact of the upper atmosphere on navigation, communication as well as surveillance systems is defined by the state of the ionosphere and in particular by variations in its electron density profile along the signal propagation path. The requirement for the accurate specification of the electron density profile stems from the fact that the elect...

Unlike the typical classification setting where each instance is associated with a single class, in multi-label learning each instance is associated with multiple classes simultaneously. Therefore the learning task in this setting is to predict the subset of classes to which each instance belongs. This work examines the application of a recently de...

Venn Predictors (VPs) are machine learning algorithms that can provide well calibrated multiprobability outputs for their predictions. An important drawback of Venn Predictors is their computational inefficiency, especially in the case of large datasets. In this work, we investigate and propose Inductive Venn Predictors (IVPs), which can overcome t...

This book constitutes the refereed proceedings of four AIAI 2014 workshops, co-located with the 10th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2014, held in Rhodes, Greece, in September 2014: the Third Workshop on Intelligent Innovative Ways for Video-to-Video Communications in Modern Smart...

This book constitutes the refereed proceedings of the 10th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2014, held in Rhodes, Greece, in September 2014. The 33 revised full papers and 29 short papers presented were carefully reviewed and selected from numerous submissions. They are organized in...

Osteoporosis is a disease of bones that results in an increased risk of bone fracture. The diagnosis of Osteoporosis is usually performed by measuring the Bone Mineral Density (BMD) using Dual-Energy X-ray Absorptiometry (DEXA) scanning. In this work, we introduce the use of Venn Prediction in order to assess the risk of Osteoporosis before a DEXA...

A medical database of 589 women thought to have osteoporosis has been analyzed. A hybrid algorithm consisting of Artificial Neural Networks and Genetic Algorithms was used for the assessment of osteoporosis. Osteoporosis is a common disease, especially in women, and a timely and accurate diagnosis is important for avoiding fractures. In this paper,...

This paper presents the development of an Artificial Neural Network electron density profiler based on electron density profiles collected from radio occultation (RO) measurements from LEO (Low Earth Orbit) satellites to improve the spatial and temporal modeling of ionospheric electron density over Europe. The significance in the accurate determina...

The evolution of Smartphone devices with their powerful computing capabilities and their ever increasing number of sensors has recently introduced an unprecedented array of applications and games. The Smartphone users who are constantly moving and sensing are able to provide large amounts of opportunistic/ participatory data that can contribute to...

Vesicoureteral Reflux (VUR) is a pediatric disorder in which urine flows backwards from the bladder into one or both ureters and, in some cases, into one or both kidneys. This has potentially very serious consequences as in the case of a Urinary Tract Infection, which is the main symptom of VUR, bacteria have direct access to the kidneys and can ca...

Venn Prediction (VP) is a new machine learning framework for producing well-calibrated probabilistic predictions. In particular it provides well-calibrated lower and upper bounds for the conditional probability of an example belonging to each possible class of the problem at hand. This paper proposes five VP methods based on Neural Networks (NNs),...

This book constitutes the refereed proceedings of the 9th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2013, held in Paphos, Cyprus, in September/October 2013. The 26 revised full papers presented together with a keynote speech at the main event and 44 papers of 8 colocated workshops were caref...

Venn Prediction (VP) is a machine learning framework that can be used to develop methods that provide well-calibrated probabilistic outputs. Unlike other probabilistic methods, the VP framework guarantees validity under the assumption that the data are independently and identically distributed (i.i.d.). Well-calibrated probabilistic outputs are of...

Feature selection has been recently used in the area of software engineering for improving the accuracy and robustness of software cost models. The idea behind selecting the most informative subset of features from a pool of available cost drivers stems from the hypothesis that reducing the dimensionality of datasets will significantly minimise the...

This paper presents the application of Neural Networks for the spatial and temporal modeling of (critical frequency) foF2 data over Europe. foF2 is the most important parameter in describing the electron density profile of the ionosphere since it represents the critical point of maximum electron density in the profile and therefore can be used to d...

Venn Predictors (VPs) are machine learning algorithms that can provide well calibrated multiprobability outputs for their predictions. The only drawback of Venn Predictors is their computational inefficiency, especially in the case of large datasets. In this work, we propose an Inductive Venn Predictor (IVP) which overcomes the computational ineffi...

Conformal Predictors (CPs) are Machine Learning algorithms that can provide reliable confidence measures to their predictions. In this work, we make use of the Conformal Prediction framework for the assessment of stroke risk based on ultrasound images of atherosclerotic carotid plaques. For this application, images were recorded from 137 asymptomat...

The problem of interpolating ionospheric characteristics is very crucial in ionospheric research as instruments are not always able to record measurements either due to failure of equipment or due to processes that deem a measurement impossible. As a result time-series of ionospheric characteristics are interrupted with short or long data gaps that...

This book constitutes the refereed proceedings of the 8th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2012, held in Halkidiki, Greece, in September 2012. The 44 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 98 submissions. The papers are org...

This book constitutes the refereed proceedings of the Workshops held at the 8th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2012, in Halkidiki, Greece, in September 2012. The book includes a total of 66 interesting and innovative research papers from the following 8 workshops: the Second Artif...

Conformal Prediction (CP) is a novel machine learning concept which uses past experience to determine precise levels of confidence in new predictions. Traditional machine learning algorithms, such as Neural Networks (NN), Support Vector Machines (SVM), etc. output simple, bare predictions without an indication (confidence) of how likely each predic...

Vertical Total Electron Content (vTEC) is an ionospheric characteristic used to derive the signal delay imposed by the ionosphere on near-vertical trans-ionospheric links. The major aim of this paper is to design a prediction model based on the main factors that influence the variability of this parameter on a diurnal, seasonal and long-term time-s...

This paper proposes an extension to conventional regression neural networks (NNs) for replacing the point predictions they produce with prediction intervals that satisfy a required level of confidence. Our approach follows a novel machine learning framework, called Conformal Prediction (CP), for assigning reliable confidence measures to predictions...

In this paper we apply Conformal Prediction (CP) to the k-Nearest Neighbours Regression (k-NNR) algorithm and propose ways of extending the typical nonconformity measure used for regression so far. Unlike traditional regression methods which produce point predictions, Conformal Predictors output predictive regions that satisfy a given confidence le...

Conformal Predictors (CPs) are machine learning algorithms that can provide predictions complemented with valid confidence measures. In medical diagnosis, such measures are highly desirable, as medical experts can gain additional information for each machine diagnosis. A risk assessment in each prediction can play an important role for medical deci...

This paper presents the application of Neural Networks for the interpolation of (critical frequency) foF2 data over Cyprus in the presence of sporadic E layer which is a frequent phenomenon during summer months causing inevitable gaps in the foF2 data series. This ionospheric characteristic (foF2) constitutes the most important parameter in HF (Hig...

A major drawback of most existing medical decision support systems is that they do not provide any indication about the uncertainty of each of their predictions. This paper addresses this problem with the use of a new machine learning framework for producing valid probabilistic predictions, called Venn Prediction (VP). More specifically, VP is comb...

The two-volume set IFIP AICT 363 and 364 constitutes the refereed proceedings of the 12th International Conference on Engineering Applications of Neural Networks, EANN 2011, and the 7th IFIP WG 12.5 International Conference, AIAI 2011, held jointly in Corfu, Greece, in September 2011. The 52 revised full papers and 28 revised short papers presented...

Software cost estimation is one of the prerequisite managerial activities carried out at the software development initiation stages and also repeated throughout the whole software life-cycle so that amendments to the total cost are made. In software cost estimation typically, a selection of project attributes is employed to produce effort estimatio...

Conformal Predictors (CPs) are machine learning algorithms that can provide predictions complemented with valid confidence measures. In medical diagnosis, such measures are highly desirable, as medical experts can gain additional information for each machine diagnosis. A risk assessment in each prediction can play an important role for medical deci...

Total Electron Content (TEC) is an ionospheric characteristic used to derive the signal delay imposed by the ionosphere on trans-ionospheric links and subsequently overwhelm its negative impact in accurate position determination. In this paper, an Evolutionary Algorithm (EA), and particularly a Genetic Programming (GP) based model is designed. The...

This research effort deals with the application of Artificial Neural Networks (ANNs) in order to help the diagnosis of cases with an orthopaedic disease, namely osteoporosis. Probabilistic Neural Networks (PNNs) and Learning Vector Quantization (LVQ) ANNs, were developed for the estimation of osteoporosis risk. PNNs and LVQ ANNs are both feed-forwa...

Non-invasive ultrasound imaging of carotid plaques allows for the development of plaque image analysis in order to assess the risk of stroke. In our work, we provide reliable confidence measures for the assessment of stroke risk, using the Conformal Prediction framework. This framework provides a way for assigning valid confidence measures to predi...

In this paper we extend regression Neural Networks (NNs) based on the Conformal Prediction (CP) framework for accompanying predictions with reliable measures of confidence. We follow a modification of the original CP approach, called Inductive Conformal Prediction (ICP), which enables us to overcome the computational inefficiency problem of CP. Unl...

Osteoporosis is the most common bone disease and is characterised by low bone mineral density and micro-architectural deterioration of bone tissue, which lead to an increased risk of fracture. The large majority of people who have osteoporosis are not aware of this until a fracture occurs. However if osteoporosis is detected early and treated accor...

This paper presents the application of neural networks for the prediction of the Total Electron Content (TEC) over Cyprus. This ionospheric characteristic constitutes an important parameter in trans-ionospheric links since it is used to derive the signal delay imposed by the ionosphere. The model is based on TEC measurements obtained over a period...

This paper addresses the problem of reliably predicting an important HF communication systems parameter, the critical frequency of the F2 ionospheric layer, with the use of a new machine learning technique, called Conformal Prediction (CP). CP accompanies the predictions of traditional machine learning algorithms with measures of confidence. The pr...

Conformal Prediction provides a framework for extending traditional machine learning algorithms, in order to complement predictions with reliable measures of confidence. The provision of such measures is significant for medical diagnostic systems, as more informed diagnoses can be made by medical experts. In this paper, we introduce a conformal pre...

Conformal Prediction provides a framework for extending traditional machine learning algorithms, in order to complement predictions with reliable measures of confidence. The provision of such measures is significant for medical diagnostic systems, as more informed diagnoses can be made by medical experts. In this paper, we introduce a conformal pre...

The Amateur Service is allocated approximately 3 MHz of spectrum in the HF band (3-30MHz) which is primarily used for long range communications via the ionosphere. However only a fraction of this resource is usually available due to unfavourable propagation conditions in the ionosphere imposed by solar activity on the HF channel. In this respect in...

This paper presents the application of Neural Networks for the prediction of the critical frequency foF2 of the ionospheric F2 layer over Cyprus. This ionospheric characteristic (foF2) constitutes the most important parameter in HF (High Frequency) communications since it is used to derive the optimum operating frequency in HF links. The model is b...

This paper presents the application of Neural Networks as a means of optimising the reliability of HF groundwave communication systems by predicting the detrimental effect of interference from other users. In the design and perfor-mance evaluation of such systems, it is essential to use real-istic models of co-channel interference. In response to t...

Most current machine learning systems for medical decision support do not produce any indication of how reliable each of their predictions is. However, an indication of this kind is highly desirable especially in the medical field. This paper deals with this problem by applying a recently developed technique for assigning confidence measures to pre...

Medical decision support is an area in which a lot of machine learning research has been conducted and several diagnostic and prognostic systems have been developed. The majority of these systems only produce bare predictions, without any indication of how reliable each of these predictions is. An indication of this kind however, is highly desirabl...

This paper presents the development of Neural Network models to predict the likelihood of interference experienced by Broadcast users in the HF spectrum (3-30 MHz). The models are based upon several years of measurements recorded at Linkoping (Sweden) across the HF band, covering a substantial part of a sunspot cycle. The dataset used for the model...

This paper deals with the problem of software effort estimation through the use of a new machine learning technique for producing reliable confidence measures in predictions. More specifically, we propose the use of Conformal Predictors (CPs), a novel type of prediction algorithms, as a means for providing effort estimations for software projects i...

In the design and performance evaluation of practical HF communication systems, it is essential to use procedures that assess the detrimental effect of interference from other users in a near real time mode. These procedures can extend system capability to estimate interference background, in the context of real time channel evaluation (RTCE) in or...

This chapter presented the Inductive Conformal Prediction (ICP) approach for producing confidence measures with predictions and described its application to Neural Networks. ICPs accompany each of their predictions with probabilistically valid measures of confidence. Furthermore, they do not need the relatively large amount of processing time spend...

In this paper we apply Conformal Prediction (CP) to the k-Nearest Neighbours Regression (k-NNR) algorithm and propose a way of extending the typical nonconformity measure used for regression so far. Unlike traditional regression methods which produce point predictions, Conformal Predictors output predictive regions that satisfy a given confidence l...

Conformal prediction (CP) is a method that can be used for complementing the bare predictions produced by any traditional machine learning algorithm with measures of confidence. CP gives good accuracy and confidence values, but unfortunately it is quite computationally inefficient. This computational inefficiency problem becomes huge when CP is cou...

The existing methods of predicting with confidence give good accuracy and confidence values, but quite often are computationally inefficient. Some partial solutions have been suggested in the past. Both the original method and these solutions were based on transductive inference. In this paper we make a radical step of replacing transductive infere...

The existing methods of predicting with confidence give good accuracy and confidence values, but quite often are computationally inefficient. Some partial solutions have been suggested in the past. Both the original method and these solutions were based on transductive inference. In this paper we make a radical step of replacing transductive infere...