1,721,041 research outputs found
Stima immediata e non invasiva della concentrazione dell’emoglobina nel sangue
No full textLa presente invenzione è relativa ad un sistema a basso costo per stimare la concentrazione di emoglobina nel sangue per mezzo del colore della congiuntiva palpebrale. La stima è basata sull'acquisizione di un'immagine della congiuntiva palpebrale per mezzo del trovato senza catturare alcuna immagine di riferimento o colore di riferimento. La presente invenzione risulta possedere le seguenti funzionalità e accorgimenti tecnologici dedicati alla risoluzione delle problematiche da cui sono affetti altri metodi e sistemi analoghi descritti nella sezione ‘background e stato della tecnica preesistente’:
• La concentrazione di emoglobina è determinata per mezzo del trovato in modo non invasivo, affidabile e ripetibile indipendentemente dalle condizioni di illuminazione ambientale, in tempo reale, in posizioni remote, e con strumenti di basso costo, di diffusione di massa, e senza l'utilizzo di una immagine di riferimento colore. L’uso del trovato può essere fatto anche autonomamente dal paziente.
• Le immagini catturate con il trovato, i dati del paziente ed i valori di emoglobina stimati possono essere inviati in tempo reale ad un medico per un ulteriore controllo. Il metodo ed il dispositivo inventato consentono di ridurre il numero di soggetti candidati per la trasfusione di sangue perché la valutazione con decisione finale di tipo binario si/no può essere fatta semplicemente considerando il valore stimato di emoglobina estratto mediante l'invenzione
Bioelectronic technologies and artificial intelligence for medical diagnosis and healthcare
Full text linkThe application of electronic findings to biology and medicine has significantly impacted health and wellbeing. Recent technology advances have allowed the development of new systems that can provide diagnostic information on portable point-of-devices or smartphones. The decreasing size of electronics technologies down to the atomic scale and the advances in system, cell, and molecular biology have the potential to increase the quality and reduce the costs of healthcare.
Clinicians have pervasive access to new data from complex sensors; imaging tools; and a multitude of other sources, including personal health e-records and smart environments. Humans are from being able to process this unprecedented volume of available data without advanced tools. Artificial intelligence (AI) can help clinicians to identify patterns from this huge amount of data to inform better choices for patients.
In this Special Issue, some original research papers focusing on recent advances have been collected, covering novel theories, innovative methods, and meaningful applications that could potentially lead to significant advances in the field
Crop planting layout optimization in sustainable agriculture: A constraint programming approach
Full text linkIn sustainable agriculture, intercropping systems represent a valuable approach. These systems involve placing mutually beneficial plant types in close proximity to each other, with the goal of exploiting biodiversity to reduce pesticide and water usage, as well as improve soil nutrient utilization. Despite its potential, the optimization of intercropping systems has received limited attention in previous studies. One of the first steps in the design of an intercropping system is the solution of the crop planting layout problem, which involves meeting crop demand while maximizing positive interactions between adjacent plants. We perform a complexity analysis of this problem and solve it through constraint programming, an artificial intelligence technique, which relies on automated reasoning, constraint propagation and search heuristics. To this aim, we present two constraint programming models based on integer variables and interval variables, respectively. Through a computational study on real-life instances, we examine the impact of different modelling approaches on the difficulty of solving the crop planting layout problem with standard constraint programming solvers. This research work has also provided the groundwork for a sowing robotic arm (under development), aiming to automate intercropping systems and assist farm workers
Sim-to-Real RNN-Based Framework for the Precise Positioning of Autonomous Mobile Robots
No full textThis work proposes a recurrent neural network-based sim-to-real method to learn mobile robot localization using lidar data in dynamic environments. The main aim of the algorithm is to estimate a Cartesian position error relative to a saved position by means of stored lidar readings in a two-dimensional environment, using lidar data as input. To achieve this, we propose a method that first trains a model on synthetic and augmented LiDAR data to embed rigid transformations into the deep learning model and then fine-tunes the model on real positions using real-world data and external camera measures to produce training labels. This pre-training and fine-tuning approach considerably reduces the time, the computation power, and the amount of real-world data needed to have an accurate model, allowing running the fine-positioning model on the edge of autonomous mobile robots(AMRs). After optimizing the model architecture and hyperparameters, the devised model is tested in different scenarios, comparing the precise positioning capability of AMRs with that of a classical iterative closest point and advanced Monte Carlo localization
Iron deficiency anemia detection using machine learning models: A comparative study of fingernails, palm and conjunctiva of the eye images
No full textAnemia is one of the global public health challenges that particularly affect children and pregnant women. A study by WHO indicates that 42% of children below the age of 6 and 40% of pregnant women worldwide are anemic. This affects the world's total population by 33%, due to the cause of iron deficiency. The non-invasive technique, such as the use of machine learning algorithms is one of the methods used in the diagnosis or detection of clinical diseases, which anemia detection cannot be overlooked in recent days. In this study, a machine learning approach was used to detect iron-deficiency anemia with the application of Naïve Bayes, CNN, SVM, k-NN, and decision tree algorithms. This enabled us to compare the conjunctiva of the eyes, the palpable palm, and the color of the fingernail images to justify which of them has a higher accuracy for detecting anemia in children. The method utilized was categorized into three different stages: dataset collection, dataset preprocessing, and model development for anemia detection. The CNN achieved a higher accuracy of 99.12%, while the SVM had the least accuracy of 95.4%. The performance of the models justifies that the non-invasive approach is an effective mechanism for anemia detection
Patch-based probabilistic identification of plant roots using convolutional neural networks
Full text linkRecently, computer vision and artificial intelligence are being used as enabling technologies for plant phenotyping studies, since they allow the analysis of large amounts of data gathered by the sensors. Plant phenotyping studies can be devoted to the evaluation of complex plant traits either on the aerial part of the plant as well as on the underground part, to extract meaningful information about the growth, development, tolerance, or resistance of the plant itself. All plant traits should be evaluated automatically and quantitatively measured in a non-destructive way. This paper describes a novel approach for identifying plant roots from images of the root system architecture using a convolutional neural network (CNN) that operates on small image patches calculating the probability that the center point of the patch is a root pixel. The underlying idea is that the CNN model should embed as much information as possible about the variability of the patches that can show chaotic and heterogeneous backgrounds. Results on a real dataset demonstrate the feasibility of the proposed approach, as it overcomes the current state of the art
TestGraphia, Document Analysis-Based Diagnosis of Dysgraphia
No full textDysgraphia is a disease related to handwriting and affects the size and distance of the characters and orthography. Patients with dysgraphia may have difficulties in motor skills and can hardly write down what they think. Dysgraphia symptoms are not rare but often are temporary. There are no specific causes known that can lead to dysgraphia, and then it is impossible to prevent it. For these reasons, it is crucial to find out dysgraphia; traditional methods are based on specific paper forms and rules to diagnose a suspect of dysgraphia. In this paper, we present a document analysis-based diagnosis process to support doctors to formulate a diagnosis. The medical examination can be completed in a short time. This system allows also large screening activities reducing any effort and permits a remote doctor-patient relationship: while it is being developed as a web-application, and due to its ease of use, it could be used at home, eventually revealing early symptoms
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
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