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Phenotypic evaluation of brown Swiss dairy cattle using images processing
(IEEE Computer Society, 2020-11-01)
Phenotypic evaluation of Brown Swiss cows is a method used in the Peruvian Andean Region to identify and select breeding females. Selection is based on their closeness to ideal dairy conformation. This task is perform by a specialists in stock judging. Under this context, the aim of the present study was to demonstrate the feasibility to perform a partial phenotypic evaluation of Brown Swiss cows by overlapping templates through development of a cow detection model and a decision making support system for identification and automatic classification of Brown Swiss cattle. TensorFlow Object Detection API was used to detect the cow in real time. The learning transfer approach was used for training, and MobilNet was selected as a pre-trained architecture. As result a mobile app was developed to determine whether an animal has Brown Swiss breed phenotypic characteristics through an automatic adjustment and calibration of a cow's template.
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Mobiloscope: a technological solution for early mastitis detection in dairy cattle
(Institute of Electrical and Electronics Engineers Inc., 2021-12-23)
One of the most critical challenges in dairy farms is the Mastistis condition causing economic losses associated with milk production reduction and veterinary treatment expenses. Although it exists different methodologies for diagnosing animals with mastitis, these tests are usually indirect; others require laboratory analysis taking a lot of time to obtain the result, limiting its viability and monitoring in the field. To solve this problem, we propose a Mobiloscope, which is a portable, practical, effective, and low-cost diagnostic system for sub-clinical mastitis. Hence, this device provides an early detection in-situ and at a low cost to cover farmers' unsatisfied demand for having innovative tools that allow them to carry out better sub-clinical mastitis early detection. Our system comprises four components: (i) the holder for the electronic device and the screen to display the graphic interface; (ii) a part where the battery for the micro-computer will be housed; (iii) a dedicated part for the microscope and sample holder; and (iv) a holder for the light source. Despite the need to validate the prototype for commercial purposes, our prototype is able to estimate the number of somatic cells. Therefore, our mobiloscope could help the farmers to make an in-situ analysis of milk quality at a low-cost.
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Technical workflow development for integrating drone surveys and entomological sampling to characterise aquatic larval habitats of Anopheles funestus in agricultural landscapes in Côte d’Ivoire
(Hindawi Limited, 2021-01-01)
Land-use practices such as agriculture can impact mosquito vector breeding ecology, resulting in changes in disease transmission. The typical breeding habitats of Africa’s second most important malaria vector Anopheles funestus are large, semipermanent water bodies, which make them potential candidates for targeted larval source management. This is a technical workflow for the integration of drone surveys and mosquito larval sampling, designed for a case study aiming to characterise An. funestus breeding sites near two villages in an agricultural setting in Côte d’Ivoire. Using satellite remote sensing data, we developed an environmentally and spatially representative sampling frame and conducted paired mosquito larvae and drone mapping surveys from June to August 2021. To categorise the drone imagery, we also developed a land cover classification scheme with classes relative to An. funestus breeding ecology. We sampled 189 potential breeding habitats, of which 119 (63%) were positive for the Anopheles genus and nine (4.8%) were positive for An. funestus. We mapped 30.42 km2 of the region of interest including all water bodies which were sampled for larvae. These data can be used to inform targeted vector control efforts, although its generalisability over a large region is limited by the fine-scale nature of this study area. This paper develops protocols for integrating drone surveys and statistically rigorous entomological sampling, which can be adjusted to collect data on vector breeding habitats in other ecological contexts. Further research using data collected in this study can enable the development of deep-learning algorithms for identifying An. funestus breeding habitats across rural agricultural landscapes in Côte d’Ivoire and the analysis of risk factors for these sites.
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Discovery of urban mobility patterns
(Springer International Publishing, 2021-01-01)
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A graph-based differentially private algorithm for mining frequent sequential patterns
(MDPI, 2022-02-01)
Currently, individuals leave a digital trace of their activities when they use their smartphones, social media, mobile apps, credit card payments, Internet surfing profile, etc. These digital activities hide intrinsic usage patterns, which can be extracted using sequential pattern algorithms. Sequential pattern mining is a promising approach for discovering temporal regularities in huge and heterogeneous databases. These sequences represent individuals’ common behavior and could contain sensitive information. Thus, sequential patterns should be sanitized to preserve individuals’ privacy. Hence, many algorithms have been proposed to accomplish this task. However, these techniques add noise to the candidate support before they are validated as, frequently, and thus, they cannot be applied without having access to all the users’ sequences data. In this paper, we propose a differential privacy graph-based technique for publishing frequent sequential patterns. It is applied at the post-processing stage; hence it may be used to protect frequent sequential patterns after they have been extracted, without the need to access all the users’ sequences. To validate our proposal, we performed a detailed assessment of its utility as a pattern mining algorithm and calculated the impact of the sanitization mechanism on a recommender system. We further evaluated its information loss disclosure risk and performed a comparison with the DP-FSM algorithm.
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Survey of text mining techniques applied to judicial decisions prediction
(MDPI, 2022-10-01)
This paper reviews the most recent literature on experiments with different Machine Learning, Deep Learning and Natural Language Processing techniques applied to predict judicial and administrative decisions. Among the most outstanding findings, we have that the most used data mining techniques are Support Vector Machine (SVM), K Nearest Neighbours (K-NN) and Random Forest (RF), and in terms of the most used deep learning techniques, we found Long-Term Memory (LSTM) and transformers such as BERT. An important finding in the papers reviewed was that the use of machine learning techniques has prevailed over those of deep learning. Regarding the place of origin of the research carried out, we found that 64% of the works belong to studies carried out in English-speaking countries, 8% in Portuguese and 28% in other languages (such as German, Chinese, Turkish, Spanish, etc.). Very few works of this type have been carried out in Spanish-speaking countries. The classification criteria of the works have been based, on the one hand, on the identification of the classifiers used to predict situations (or events with legal interference) or judicial decisions and, on the other hand, on the application of classifiers to the phenomena regulated by the different branches of law: criminal, constitutional, human rights, administrative, intellectual property, family law, tax law and others. The corpus size analyzed in the reviewed works reached 100,000 documents in 2020. Finally, another important finding lies in the accuracy of these predictive techniques, reaching predictions of over 60% in different branches of law.
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Recommender systems using temporal restricted sequential patterns
(Springer Science+Business Media, 2022-04-28)
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Mapping Malaria Vector Habitats in West Africa: Drone Imagery and Deep Learning Analysis for Targeted Vector Surveillance
(MDPI, 2023-06-01)
Disease control programs are needed to identify the breeding sites of mosquitoes, which transmit malaria and other diseases, in order to target interventions and identify environmental risk factors. The increasing availability of very-high-resolution drone data provides new opportunities to find and characterize these vector breeding sites. Within this study, drone images from two malaria-endemic regions in Burkina Faso and Côte d’Ivoire were assembled and labeled using open-source tools. We developed and applied a workflow using region-of-interest-based and deep learning methods to identify land cover types associated with vector breeding sites from very-high-resolution natural color imagery. Analysis methods were assessed using cross-validation and achieved maximum Dice coefficients of 0.68 and 0.75 for vegetated and non-vegetated water bodies, respectively. This classifier consistently identified the presence of other land cover types associated with the breeding sites, obtaining Dice coefficients of 0.88 for tillage and crops, 0.87 for buildings and 0.71 for roads. This study establishes a framework for developing deep learning approaches to identify vector breeding sites and highlights the need to evaluate how results will be used by control programs.
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AP-Traj2: Transformer-based Trajectory Prediction with Graph-Enhanced Attention Mechanism
(Slovenian Society Informatika, 2025-12-15)
Trajectory prediction is essential for understanding human mobility patterns, with applications such as itinerary recommendation and urban planning. It involves analyzing sequences of visited locations to forecast the user's next destination. Traditional approaches have often relied on Markov chains or recurrentneural networks (RNNs). More recently, Transformer neural networks have gained attention for sequential prediction tasks due to their superior parallelization and training efficiency. In this study, we propose AP-Traj2 (Attention and Possible directions for TRAJectory prediction 2), a model designed to enhance prediction accuracy by leveraging attention mechanisms and graph-based movement modeling. AP-Traj2 employs self-attention to capture dependencies among visited locations, explores feasible next steps through a graph of possible directions, and incorporates contextual information via location embeddings. Experiments conducted on GPS, CDR, and WiFi datasets demonstrate that AP-Traj2 improves the average matchratio by approximately 50% over state-of-the-art methods. Moreover, it achieves significantly faster training times, with reductions of up to 72% in the best-case scenario. Unlike existing approaches that focus primarily on neural network architecture, this work emphasizes the importance of data preprocessing andfiltering, highlighting their substantial impact on model performance.
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