1,721,002 research outputs found
Neural Networks in the Educational Sector: Challenges and Opportunities
No full textGiven their increasing diffusion, deep learning networks have long been considered an important subject on which teaching efforts should be concentrated, to support a fast and effective training. In addition to that role, the availability of rich data coming from several sources underlines the potential of neural networks used as an analysis tool to identify critical aspects, plan upgrades and adjustments, and ultimately improve learning experience. Analysis and forecasting methods have been widely used in this context, allowing policy makers, managers and educators to make informed decisions. The capabilities of recurring neural networks-in particular Long Short-Term Memory networks-in the analysis of natural language have led to their use in measuring the similarity of educational materials. Massive Online Open Courses provide a rich variety of data about the learning behaviors of online learners. The analysis of learning paths provides insights related to the optimization of learning processes, as well as the prediction of outcomes and performance. Another active area of research concerns the recommendation of suitable personalized, adaptive, learning paths, based on varying sources, including even the tracing of eye-path movements. In this way, the transition from passive learning to active learning can be achieved. Challenges and opportunities in the application of neural networks in the educational sector are presented
A distance-based network activity correlation framework for defeating anonymization overlays
No full textAs the effectiveness of modern Internet-based anonymization infrastructures grows, law enforcement agencies are experiencing a progressive erosion of their surveillance capabilities. This can severely undermine their efforts to prevent and investigate various types of unlawful activities, potentially increasing the impunity of organized criminal networks. Balancing the legitimate privacy needs of individuals with the imperative to maintain public safety and combat criminal behavior in the digital world remains a complex tradeoff for both policymakers and technologists who need to find a systematic and reliable way to link the traffic traces associated with criminal activities to their anonymized origins. Accordingly, this paper presents a simple but very effective de-anonymization approach capable of associating traffic traces captured at the edge of the overlay infrastructures, in correspondence with the true origins, to those captured in correspondence with the destinations. The approach is based on determining the minimum-distance pairs within a complete bipartite graph in which the traffic traces are the nodes. Experiments with different distance functions, applied in varied ways, show that the resulting framework appears to be a promising solution that is scalable and easily deployable on real-life network equipment
A perspective on quantum Fintech
No full textRecently, FinTech has emerged as a hot topic, utilizing technology to enhance and innovate financial services and products. This expanding topic requires theoretical models and innovative methodologies to forward new business challenges. In today's world, where vast amounts of data are generated every day, the need for computers capable of accurate predictive computations is becoming increasingly critical. Consequently, many financial institutions are turning to quantum computing, which promises to analyze large datasets and deliver results more quickly and accurately than any classical computer ever could. On the other hand, knowledge about quantum computing is not yet widely diffused in finance communities. In this work, after providing a gentle introduction to quantum mechanics, we review the state of the art of quantum computing in Fintech, touching such themes as stochastic modeling, optimization, and machine learning. Theoretical results, as well as practical solution, are discussed with the associated challenges
Predictive and adaptive Drift Analysis on Decomposed Healthcare Claims using ART based Topological Clustering
No full textFraud in healthcare services dissipates funds that are important for improving the quality of life of people, thus enhancing the interest in predictive fraud analysis. The predictive analysis of fraudulent activity can be done by looking for unusual patterns in healthcare claims. However, unusual patterns may also occur due to sudden changes, isolated events, or concept drifts that frequently happen in healthcare which should not be considered fraud. Furthermore, analyzing drifts also supports predicting future trends and behaviors. In this study, we propose a novel approach, Drift Analysis on Decomposed Healthcare Claims (DADHC), to analyze the hidden patterns that hinder the performance of fraud prediction and detection. Our proposed model decomposes the series of healthcare claims into regular and irregular patterns using Psuedo Additive Decomposition (PAD) integrated with Simple Moving Average (SMA) smoothing technique. Then ART (Adaptive Resonance Theory) based Topological Clustering (TC) is used to analyze unusual patterns and identify the actual fraudulent activities. Our proposed model also incorporates correntropy based vigilance testing in ART to enhance adaptivity. Empirical evaluation on CMS Part B claims shows that our proposed approach has significantly improved detection accuracy compared to existing models due to the drift analysis
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators
No full textForex (foreign exchange) is a special financial market that entails both high risks and high profit opportunities for traders. It is also a very simple market since traders can profit by just predicting the direction of the exchange rate between two currencies. However, incorrect predictions in Forex may cause much higher losses than in other typical financial markets. The direction prediction requirement makes the problem quite different from other typical time-series forecasting problems. In this work, we used a popular deep learning tool called “long short-term memory” (LSTM), which has been shown to be very effective in many time-series forecasting problems, to make direction predictions in Forex. We utilized two different data sets—namely, macroeconomic data and technical indicator data—since in the financial world, fundamental and technical analysis are two main techniques, and they use those two data sets, respectively. Our proposed hybrid model, which combines two separate LSTMs corresponding to these two data sets, was found to be quite successful in experiments using real data
To trust or not to trust? An assessment of trust in AI-based systems: Concerns, ethics and contexts
No full textArtificial intelligence (AI) characterizes a new generation of technologies capable of interacting with the environment and aiming to simulate human intelligence. The success of integrating AI into organizations critically depends on workers' trust in AI technology. Trust is a central component of the interaction between people and AI, as incorrect levels of trust may cause misuse, abuse or disuse of the technology. The European Commission's High-level Expert Group on AI (HLEG) have adopted the position that we should establish a relationship of trust with AI and should cultivate trustworthy AI. This article investigates the links between trust in AI, concerns related to AI use, and the ethics related to such use. We used data collected in 2019 from more than 30,000 individuals across the EU28. The data focuses on living conditions, trust, and AI uses and concerns. An econometric model is used. The endogenous variable is an ordered measure of trust in AI. We use an ordered logit model to highlight the factors associated with an increased level of trust in AI in Europe. The results show that many concerns related to AI use are linked to AI trust, and the ability to try out AI applications will also have an impact on initial trust. To enhance trust, practitioners can try to maximize the technological features in AI systems. The representation of the AI as a humanoid or a loyal pet (e.g., a dog) will facilitate initial trust formation. Moreover, findings reveal an unequal degree of trust in AI across countries
Using neural networks to obtain indirect information about the state variables in an alcoholic fermentation process
Full text linkThis work provides a manual design space exploration regarding the structure, type, and inputs of a multilayer neural network (NN) to obtain indirect information about the state variables in the alcoholic fermentation process. The main benefit of our application is to help experts reduce the time needed for making the relevant measurements and to increase the lifecycles of sensors in bioreactors. The novelty of this research is the flexibility of the developed application, the use of a great number of variables, and the comparative presentation of the results obtained with different NNs (feedback vs. feed-forward) and different learning algorithms (Back-Propagation vs. Levenberg–Marquardt). The simulation results show that the feedback neural network outperformed the feed-forward neural network. The NN configuration is relatively flexible (with hidden layers and a number of nodes on each of them), but the number of input and output nodes depends on the fermentation process parameters. After laborious simulations, we determined that using pH and CO2 as inputs reduces the prediction errors of the NN. Thus, besides the most commonly used process parameters like fermentation temperature, time, the initial concentration of the substrate, the substrate concentration, and the biomass concentration, by adding pH and CO2, we obtained the optimum number of input nodes for the network. The optimal configuration in our case was obtained after 1500 iterations using a NN with one hidden layer and 12 neurons on it, seven neurons on the input layer, and one neuron as the output. If properly trained and validated, this model can be used in future research to accurately predict steady-state and dynamic alcoholic fermentation process behaviour and thereby improve process control performance
Monitoring system of a heat pump installation for heating a rural house using low-grade heat from a surface watercourse
No full textIncreasing the efficiency of heat pump systems primarily used for heat supply to buildings is an important topic. This is especially true for systems constructed according to non-standard schemes and which use low-grade heat from various sources that are rarely considered for these purposes. Such studies require special, often expensive, data acquisition systems. In this paper, a low-cost computer-based monitoring system is presented. The monitoring system incorporates solutions which are new or seldom used. It is shown that modern semiconductor thermistors can replace commonly used platinum temperature sensors and thermocouples. A proposal for processing frequency output signals from sensors through an analog-to-digital converter and a way to reduce the number of required input channels are described. The monitoring system allows optimization of various types of heat-pump-based installations. The system has been used for quite a long time to monitor the operation of the heat pump installation using low-grade heat from a surface watercourse. With its help, the feasibility of using the previously proposed submersible floating heat exchanger is justified and the optimal scheme for its placement in the watercourse is determined
- …
