Hybrid PSO Algorithm with Iterated Local Search Operator for Equality Constraints Problems

This paper presents a hybrid PSO algorithm (Par-ticle Swarm Optimization) with an ILS (Iterated Local Search) operator for handling equality constraints problems in mono-objective optimization problems. The ILS can be used to locally search around the best solutions in some generations, exploring the attraction basins in small portions of the feasible set. This process can compensate the difficulty of the evolutionary algorithm to generate good solutions in zero-volume regions. The greatest advantage of the operator is the simple implementation. Experiments performed on benchmark problems shows improvement in accuracy, reducing the gap for the tested problems

A Benchmark for Iris Location and a Deep Learning Detector Evaluation

The iris is considered as the biometric trait with the highest unique probability. The iris location is an important task for biometrics systems, affecting directly the results obtained in specific applications such as iris recognition, spoofing and contact lenses detection, among others. This work defines the iris location problem as the delimitation of the smallest squared window that encompasses the iris region. In order to build a benchmark for iris location we annotate (iris squared bounding boxes) four databases from different biometric applications and make them publicly available to the community. Besides these 4 annotated databases, we include 2 others from the literature. We perform experiments on these six databases, five obtained with near infra-red sensors and one with visible light sensor. We compare the classical and outstanding Daugman iris location approach with two window based detectors: 1) a sliding window detector based on features from Histogram of Oriented Gradients (HOG) and a linear Support Vector Machines (SVM) classifier; 2) a deep learning based detector fine-tuned from YOLO object detector. Experimental results showed that the deep learning based detector outperforms the other ones in terms of accuracy and runtime (GPUs version) and should be chosen whenever possible.Comment: Accepted for presentation at the International Joint Conference on Neural Networks (IJCNN) 201

Voronoi distance based prospective space-time scans for point data sets: a dengue fever cluster analysis in a southeast Brazilian town

<p>Abstract</p> <p>Background</p> <p>The Prospective Space-Time scan statistic (PST) is widely used for the evaluation of space-time clusters of point event data. Usually a window of cylindrical shape is employed, with a circular or elliptical base in the space domain. Recently, the concept of Minimum Spanning Tree (MST) was applied to specify the set of potential clusters, through the Density-Equalizing Euclidean MST (DEEMST) method, for the detection of arbitrarily shaped clusters. The original map is cartogram transformed, such that the control points are spread uniformly. That method is quite effective, but the cartogram construction is computationally expensive and complicated.</p> <p>Results</p> <p>A fast method for the detection and inference of point data set space-time disease clusters is presented, the Voronoi Based Scan (VBScan). A Voronoi diagram is built for points representing population individuals (cases and controls). The number of Voronoi cells boundaries intercepted by the line segment joining two cases points defines the Voronoi distance between those points. That distance is used to approximate the density of the heterogeneous population and build the Voronoi distance MST linking the cases. The successive removal of edges from the Voronoi distance MST generates sub-trees which are the potential space-time clusters. Finally, those clusters are evaluated through the scan statistic. Monte Carlo replications of the original data are used to evaluate the significance of the clusters. An application for dengue fever in a small Brazilian city is presented.</p> <p>Conclusions</p> <p>The ability to promptly detect space-time clusters of disease outbreaks, when the number of individuals is large, was shown to be feasible, due to the reduced computational load of VBScan. Instead of changing the map, VBScan modifies the metric used to define the distance between cases, without requiring the cartogram construction. Numerical simulations showed that VBScan has higher power of detection, sensitivity and positive predicted value than the Elliptic PST. Furthermore, as VBScan also incorporates topological information from the point neighborhood structure, in addition to the usual geometric information, it is more robust than purely geometric methods such as the elliptic scan. Those advantages were illustrated in a real setting for dengue fever space-time clusters.</p

Regularization Through Simultaneous Learning: A Case Study on Plant Classification

In response to the prevalent challenge of overfitting in deep neural networks, this paper introduces Simultaneous Learning, a regularization approach drawing on principles of Transfer Learning and Multi-task Learning. We leverage auxiliary datasets with the target dataset, the UFOP-HVD, to facilitate simultaneous classification guided by a customized loss function featuring an inter-group penalty. This experimental configuration allows for a detailed examination of model performance across similar (PlantNet) and dissimilar (ImageNet) domains, thereby enriching the generalizability of Convolutional Neural Network models. Remarkably, our approach demonstrates superior performance over models without regularization and those applying dropout regularization exclusively, enhancing accuracy by 5 to 22 percentage points. Moreover, when combined with dropout, the proposed approach improves generalization, securing state-of-the-art results for the UFOP-HVD challenge. The method also showcases efficiency with significantly smaller sample sizes, suggesting its broad applicability across a spectrum of related tasks. In addition, an interpretability approach is deployed to evaluate feature quality by analyzing class feature correlations within the network's convolutional layers. The findings of this study provide deeper insights into the efficacy of Simultaneous Learning, particularly concerning its interaction with the auxiliary and target datasets

Towards better heartbeat segmentation with deep learning classification

The confidence of medical equipment is intimately related to false alarms. The higher the number of false events occurs, the less truthful is the equipment. In this sense, reducing (or suppressing) false positive alarms is hugely desirable. In this work, we propose a feasible and real-time approach that works as a validation method for a heartbeat segmentation third-party algorithm. The approach is based on convolutional neural networks (CNNs), which may be embedded in dedicated hardware. Our proposal aims to detect the pattern of a single heartbeat and classifies them into two classes: a heartbeat and not a heartbeat. For this, a seven-layer convolution network is employed for both data representation and classification. We evaluate our approach in two well-settled databases in the literature on the raw heartbeat signal. The first database is a conventional on-the-person database called MIT-BIH, and the second is one less uncontrolled off-the-person type database known as CYBHi. To evaluate the feasibility and the performance of the proposed approach, we use as a baseline the Pam-Tompkins algorithm, which is a well-known method in the literature and still used in the industry. We compare the baseline against the proposed approach: a CNN model validating the heartbeats detected by a third-party algorithm. In this work, the third-party algorithm is the same as the baseline for comparison purposes. The results support the feasibility of our approach showing that our method can enhance the positive prediction of the Pan-Tompkins algorithm from 97.84%/90.28% to 100.00%/96.77% by slightly decreasing the sensitivity from 95.79%/96.95% to 92.98%/95.71% on the MIT-BIH/CYBHi databases

Chimerical dataset creation protocol based on Doddington Zoo : a biometric application with face, eye, and ECG.

Multimodal systems are a workaround to enhance the robustness and effectiveness of biometric systems. A proper multimodal dataset is of the utmost importance to build such systems. The literature presents some multimodal datasets, although, to the best of our knowledge, there are no previous studies combining face, iris/eye, and vital signals such as the Electrocardiogram (ECG). Moreover, there is no methodology to guide the construction and evaluation of a chimeric dataset. Taking that fact into account, we propose to create a chimeric dataset from three modalities in this work: ECG, eye, and face. Based on the Doddington Zoo criteria, we also propose a generic and systematic protocol imposing constraints for the creation of homogeneous chimeric individuals, which allow us to perform a fair and reproducible benchmark. Moreover, we have proposed a multimodal approach for these modalities based on state-of-the-art deep representations built by convolutional neural networks. We conduct the experiments in the open-world verification mode and on two different scenarios (intra-session and inter-session), using three modalities from two datasets: CYBHi (ECG) and FRGC (eye and face). Our multimodal approach achieves impressive decidability of 7.20 ? 0.18, yielding an almost perfect verification system (i.e., Equal Error Rate (EER) of 0.20% ? 0.06) on the intra-session scenario with unknown data. On the inter-session scenario, we achieve a decidability of 7.78 ? 0.78 and an EER of 0.06% ? 0.06. In summary, these figures represent a gain of over 28% in decidability and a reduction over 11% of the EER on the intra-session scenario for unknown data compared to the best-known unimodal approach. Besides, we achieve an improvement greater than 22% in decidability and an EER reduction over 6% in the inter-session scenario

Biomass of the forage in Tifton 85 pastures fertilized with nitrogen and managed under continuous stocking

The present study, carried out at the Division for Research and Development of Adamantina, SP/Brazil, at Agência Paulista de Tecnologia dos Agronegócios (APTA) – Polo da Alta Paulista, aimed to evaluate the effect of nitrogen fertilization on the morphological composition, tiller density, leaf area index, light interception and forage accumulation of Tifton 85 pastures subjected to continuous grazing. Treatments corresponded to three nitrogen and control doses (0, 100, 200 and 400 kg N/ha/year), arranged in a randomized block design with four replications. No effect of time of evaluation was verified on the vegetative, dead and total tiller densities of Tifton 85 under continuous grazing with variable stocking rate. The pastures of Cynodon cv. Tifton 85 were affected by the time of evaluation and nitrogen doses, with marked effect on the pasture morphological composition, leaf area index and light interception, which are determinant to forage accumulation. Tifton 85 grass reached the necessary conditions to achieve the maximum growth rate of the culture when managed under continuous grazing keeping the pasture at a height of 15 cm.O presente ensaio foi realizado na Unidade de Pesquisa e Desenvolvimento de Adamantina, SP, da Agência Paulista de Tecnologia dos Agronegócios (APTA) – Polo da Alta Paulista, com objetivo de avaliar o efeito da adubação nitrogenada na composição morfológica, densidade populacional de perfilhos, índice de área foliar, interceptação luminosa e acúmulo de forragem em pastos de Tifton 85 submetidos ao regime de lotação contínua. Os tratamentos corresponderam a três doses de nitrogênio e o controle (0, 100, 200 e 400 kg/ha/ano de N), dispostos em delineamento experimental de blocos casualizados com quatro repetições. Não foi constatado efeito da época de avaliação na densidade populacional de perfilhos vegetativo, morto e total de Tifton 85 em lotação continua com carga variável. Os parâmetros avaliados nos pastos de Cynodon cv. Tifton 85 sofrem influência da época de avaliação e das doses de nitrogênio com efeito marcante na composição morfológica do pasto, índice de área foliar e interceptação luminosa que são determinantes no acúmulo de forragem. O capim Tifton 85 alcançou as condições necessárias para atingir a taxa de crescimento da cultura máxima quando manejado sob lotação continua mantendo-se o pasto a 15 cm de altura.Londrina, P

Perspectivas atuais sobre o uso de psilocibina no manejo da depressão resistente: revisão sistemática

A depressão resistente ao tratamento é um desafio global, impactando negativamente a qualidade de vida dos pacientes. Nesse contexto, a psilocibina, um composto psicodélico presente em certos cogumelos, desperta interesse como possível intervenção terapêutica. Seu potencial para influenciar positivamente o humor e a cognição, através da ativação dos receptores de serotonina no cérebro, sugere uma nova abordagem no tratamento da depressão resistente. Este estudo busca analisar as perspectivas atuais sobre o uso da psilocibina nesse contexto, destacando a necessidade de mais pesquisas sobre seus efeitos e segurança para sua integração clínica. Este estudo, baseado em uma revisão sistemática da literatura científica, abrange o período de 2016 a 2024, utilizando as bases de dados PubMed (Medline), Cochrane Library e Scientific Electronic Library Online (SciELO). No primeiro estudo, os efeitos agudos da psilocibina foram detectáveis de 30 a 60 minutos após a administração, atingindo o pico em 2 a 3 horas e diminuindo após pelo menos 6 horas. A substância foi bem tolerada, com eventos adversos leves e transitórios. Houve uma redução significativa nos sintomas depressivos, ansiedade e anedonia após o tratamento com doses altas. O segundo estudo envolveu 233 participantes distribuídos em grupos de doses diferentes. Houve uma redução significativa nos sintomas depressivos após o tratamento, com doses mais altas apresentando uma diferença estatisticamente maior em comparação com a dose mais baixa e o grupo de controle. Eventos adversos, como dor de cabeça e náusea, foram comuns entre os participantes. O terceiro estudo abordou as perspectivas futuras para o tratamento com psilocibina para depressão resistente. Recomendações incluíram equilibrar o tempo dos pacientes e terapeutas, aumentar gradualmente a intensidade das sessões e integrar a terapia sustentada ao tratamento. O envolvimento de pacientes experientes e estudos naturalísticos adicionais foi destacado como importante para abordagens mais personalizadas. Em resumo, a psilocibina mostra potencial como tratamento para a depressão resistente, com redução significativa dos sintomas depressivos e boa tolerabilidade. No entanto, são necessárias mais pesquisas para confirmar sua eficácia e segurança, destacando a importância de estudos adicionais e ensaios clínicos controlados