There is growing interest in developing reliable, non-invasive, and cost-effective methods for early diagnosis of neurodegenerative diseases such as Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). In this regard, handwriting-based tasks have shown potential in differentiating MCI and AD patients from healthy controls (HCs). However, previous work has reported mixed results when using different symbols and data representations. We address this research gap by developing computational models (convolutional and recurrent neural networks) to differentiate MCI and AD from HCs with off-line (scanned images) and on-line (discrete time series) house drawings. Notably, we observed that augmenting on-line data and then converting it to off-line format, a method we refer to as "OnOff-line", yielded the best performance results in binary classification tasks. These findings highlight the effectiveness of on-line representations in capturing handwriting dynamics more accurately. Ultimately, our work opens new avenues for future research to enhance automated diagnostic of MCI and AD from handwriting analysis.

Traditional helpdesk ticketing systems (TS) often fail to manage huge amounts of support tickets efficiently, resulting in longer response times and lower user satisfaction. This paper proposes a new TS approach by integrating a Discord-based community with a TS and chatbot. The suggested system takes advantage of the open-source features provided by the Discord platform to create and handle support requests from within Discord channels, taking advantage of the platform’s real-time communication features and the support of a large community. The system’s initial review shows that 88% of the users are delighted through the employment of Discord, providing scalable solutions that can be applied to various community-driven support situations [9].

Alzheimer's disease (AD) is a cognitive disorder, marked by memory loss and impaired reasoning, that requires early detection methods to better manage and potentially slow down the disease's progression. Recent advances in machine learning have offered new possibilities for AD detection using handwriting analysis, however previous work has considered only one type of input source, e.g. clock or pentagon drawings. Here we propose to develop an efficient method for detecting AD's early symptoms using Deep Feature Concatenation (DFC) models considering multiple handwriting sources: pentagon drawings, self-reported sentences, and signatures. Substantial performance improvements were observed when considering all input sources together with data augmentation techniques. For example, classification accuracy increased from 60% (best model, without data augmentation) to 80% (DFC and data augmentation). Our findings show that the use of diverse input sources can lead to an efficient and cost-effective method for early AD detection. Looking forward into the future, our study highlights the potential of DFC in supporting home-based healthcare diagnoses which is a crucial step in integrating artificial intelligence into healthcare practices.

Dementia is a disease characterized by memory impairment and a gradual disability in performing daily activities. Automated screening for early detection of dementia can lead to more adequate and timely treatment. Our work focuses on predicting various stages of dementia severity using pre-trained Deep Learning (DL) models and a public Clock Drawing Test (CDT) dataset. However, the relationship between sample size and model performance is not yet well understood. This may lead to an overreliance on a large number of samples for model training, which may eventually deter reliable outcomes. We found that the classification performance of DL models tends to plateau once a certain number of samples is reached, therefore, it is possible to work on a small data regime with DL models in this task. This research not only advances the field of medical image analysis for dementia screening but also offers broader implications for DL applications in healthcare. Ultimately, the understanding of how sample size affects model performance can guide future research and support more intelligent and efficient utilization of DL models in addressing complex health-related challenges.

The rise of Artificial Intelligence has led to advancements in daily life, including applications in industries, telemedicine, farming, and smart cities. It is necessary to have human-AI synergies to guarantee user engagement and provide interactive expert knowledge, despite AI’s success in "less technical" fields. In this article, the possible synergies between humans and AI to explain the development of art history and artistic style transfer are discussed. This study is part of the "Smart Photobooth" project that is able to automatically transform a user’s picture into a well-known artistic style as an interactive approach to introduce the fundamentals of the history of art to the common people and provide them with a concise explanation of the various art painting styles. This study investigates human-AI synergies by combining the explanation produced by an explainable AI mechanism with a human expert’s insights to provide reasons for school students and a larger audience.

Granting loans is one of the major concerns of financial institutions due to the risks of default borrowers. Default prediction by the neural networks is a popular technique for credit risk modeling. Neural networks generally offer the accurate predictions that help banks to prevent financial losses and grow their business by approving more creditworthy borrowers. Although neural networks are capable of capturing the complex, non-linear relationships between a large number of features and output, these models act as black boxes. This is a graduation project paper that is focused on loan default risk prediction by multi-layer perceptron neural network and building up explainability to some degree in the trained neural networks through sensitivity analysis. The architecture of a multi-layer perceptron neural network with the best result is used to help the credit-risk manager in explaining why an applicant is a defaulter or non-defaulter. The prediction of a trained multi-layer perceptron neural network is explained by mapping input features and target variables directly using a model-agnostic explanation as well as a model-specific explanation. Lastly, a comparison is performed between two explanation methods.

In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person's expressions by discovering both textual and contextual information.In particular, we construct a hierarchical neural network that leverages valuable information from a person's past expressions, and offer a better understanding of the sentiment from the expresser's perspective.Additionally, we investigate how a person's sentiment changes over time so that recent incidents or opinions may have more effect on the person's current sentiment than the old ones.Psychological studies have also shown that individual variation exists in how easily people change their sentiments.In order to model such traits, we develop a modified attention mechanism with Hawkes process applied on top of a recurrent network for a userspecific design.Implemented with automatically labeled Twitter data, the proposed model has shown positive results employing different input formulations for representing the concerned information.

peer reviewed

Abstract: Spacecrafts provide a large set of on-board components information such as their temperature, power and pressure. This information is constantly monitored by engineers, who capture the outliers and determine whether the situation is abnormal or not. However, due to the large quantity of information, only a small part of the data is being processed or used to perform anomaly prediction. A common accepted research concept for anomaly prediction as described in literature yields on using projections, based on probabilities, estimated on learned patterns from the past (Fujimaki et al., 2005) and data mining methods to enhance the conventional diagnosis approach (Li et al., 2010). Most of them conclude on the need to build a status vector. We propose an algorithm for efficient outlier detection that builds an identity chart of the patterns using the past data based on their curve fitting information. It detects the functional units of the patterns without apriori knowledge with the intent to learn its structure and to reconstruct the sequence of events described by the signal. On top of statistical elements, each pattern is allotted a characteristics chart. This pattern identity enables fast pattern matching across the data. The extracted features allow classification with regular clustering methods like support vector machines (SVM). The algorithm has been tested and evaluated using real satellite telemetry data. The outcome and performance show promising results for faster anomaly prediction. 1

In this paper, we introduce an explorative but adaptive-associative information management system in the presence of a natural conversation. We take advantage of explorative mind-maps, which have been demonstrated in [10] and which are altogether a management framework that emerges automatically from the data input stream it gets. An explorative mind-map is a non-verificative but dynamic system that basis on the natural paradigm: it changes its complexity continuously and fosters symbolic cells according to internal activation states. Generally, the structure mirrors a mental state where the oblivion of associated facts arrive once the stimulation decreases. Considering two mind-maps A1,2and B1,2for two conversational partners A and B, the mind-map*1represents the self-conversation and*2the conversational stream of the conversational partner. If we merge these mind-maps, we may apply the out-coming results for the computation of trust.