Un enfoque para la detección de enfermedades de las plantas utilizando técnicas de aprendizaje profundo

Agriculture is the backbone of Indian economy. Conventional farming systems are no longer being followed by our generation, due to lack of knowledge and expertise. Advancement of technologies pave a path that make a transition from traditional farming methods to smart agriculture by automating the processes involved. Challenges faced by today’s agriculture are depletion of soil nutrients and diseases caused by pests which lead to low productivity, irrigation problems, soil erosion, shortage of storage facilities, availability of quality seeds, lack of transportation, poor marketing etc. Among all these challenges in agriculture, prediction of diseases remains a major issue to be addressed. Identifying diseases based on visual inspection is the traditional way of farming which needs knowledge and experience to handle. Automating the process of detecting and identifying through visual inspection (cognitive) is the motivation behind this work. This is made possible with the availability of images of the plant or parts of plants, since most diseases are reflected on the leaves. A deep learning network architecture named Plant Disease Detection Network PDDNet-cv and a transfer learning approach of identifying diseases in plants were proposed. Our proposed system is compared with VGG19, ResNet50, InceptionResNetV2, the state-of-the-art methods reported in [9, 13, 5] and the results show that our method is significantly performing better than the existing systems. Our proposed PDDNet-cv has achieved average classification accuracy of 99.09% in detecting different classes of diseases. The proposed not so deep architecture is performing well compared to other deep learning architectures in terms of performance and computational time.La agricultura es la columna vertebral de la economía india. Los sistemas agrícolas convencionales ya no están siendo seguidos por nuestra generación, debido a la falta de conocimiento y experiencia. El avance de las tecnologías allana un camino que hace una transición de los métodos agrícolas tradicionales a la agricultura inteligente mediante la automatización de los procesos involucrados. Los desafíos que enfrenta la agricultura actual son el agotamiento de los nutrientes del suelo, las enfermedades causadas por plagas que conducen a una baja productividad, los problemas de riego, la erosión del suelo, la escasez de instalaciones de almacenamiento, la disponibilidad de semillas de calidad, la falta de transporte, la mala comercialización, etc. Entre todos estos desafíos en la agricultura, la predicción de enfermedades sigue siendo un tema importante que debe abordarse. La identificación de enfermedades basadas en la inspección visual es la forma tradicional de cultivo que necesita el conocimiento y la experiencia para manejarlas que obtiene un buen rendimiento. Automatizar el proceso de detección e identificación a través de la inspección visual (cognitiva) es la motivación detrás de este trabajo. Esto es posible gracias a la disponibilidad de imágenes de la planta o partes de plantas, ya que la mayoría de las enfermedades se reflejan en las hojas. Se propuso una arquitectura de red de aprendizaje profundo llamada Red de Detección de Enfermedades de las plantas por sus siglas en inglés (Plant Disease Detection Network PDDNet-cv) y un enfoque de aprendizaje por transferencia para identificar enfermedades en las plantas. Nuestro sistema propuesto se compara con VGG19, ResNet50, InceptionResNetV2, los métodos de vanguardia reportados en [9, 13, 5] y los resultados muestran que nuestro método está funcionando significativamente mejor que los sistemas existentes. Nuestra propuesta PDDNet-cv ha logrado una precisión de clasificación promedio del 99,09% en la detección de diferentes clases de enfermedades. La arquitectura no tan profunda propuesta, está funcionando bien en comparación con otras arquitecturas de aprendizaje profundo en términos de rendimiento y tiempo computacional

INTEGRATING SENSOR DATA AND MACHINE LEARNING FOR PREDICTIVE MAINTENANCE IN INDUSTRY 4.0

The availability of manufacturing machinery is crucial for having a productive production line. So, for industrialists, being successful in the field of maintenance is crucial if they want to make sure that key equipment is performing as it should and that unscheduled downtime is kept to a minimum. Predictive maintenance skills are viewed as being essential with the rise of complex industrial processes. The assistance that contemporary value chains may provide for a company's maintenance role is another area of focus. The development of sensors and Industry 4.0 technologies has greatly improved access to data from equipment, processes, and products. Electric motor condition monitoring and predictive maintenance help the industry avoid significant financial losses brought on by unforeseen motor breakdowns and significantly increase system dependability. This research offers Enhanced Nave Bayes Artificial Neural Network-based machine learning architecture for Predictive Maintenance. The system was tested in an industrial setting by building a data collection and analysis system using sensors, analyzing the data with a machine learning approach, and comparing the results to those generated by a simulation tool. With the help of the Azure Cloud, the Data Analysis Tool may access information collected by a wide variety of sensors, machine PLCs, and communication protocols. Preliminary results show that the method correctly predicts a wide range of machine states

OPTIMIZING PRODUCTION SCHEDULING THROUGH HYBRID DYNAMIC GENETIC-ADAPTIVE IMPROVED GRAVITATIONAL OPTIMIZATION ALGORITHM

Mass customization is becoming the more and more of emphasis on the production optimization. In many manufacturing and service organizations, production planning and scheduling are characterized as the daily decision-making procedures. The significance of the choices made is therefore to shown in the areas of work orders, manufacturing, transportation, and distribution of the finished goods. Production scheduling is the process of regulating, determining, and maximizing the restricted resources of the production system. In this study, a novel Hybrid Dynamic Genetic-Adaptive Improved Gravitational Optimization Algorithm (HDG-AIGOA) approach is introduced to optimize the production schedule. In this case, the AIGOA classification effectiveness is increased by using the HDG method. The small and benchmark iMOPSE dataset has been used to assess the success of suggested approach. The noisy data from raw data samples are removed using the Adaptive Median Filter (AMF) filter. To extract the properties from the segmented data, a Kernel Principal Component Analysis (KPCA) is performed. The results of the research show that the recommended methodology beats earlier approaches in terms of the accuracy, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Square Error (MSE). Our proposed method might consider to improve the production scheduling in an dynamic environment

SSNCSE-NLP @ EVALITA2020: Textual and Contextual Stance Detection from Tweets Using Machine Learning Approach

Opinions expressed via online social media platforms can be used to analyse the stand taken by the public about any event or topic. Recognizing the stand taken is the stance detection, in this paper an automatic stance detection approach is proposed that uses both deep learning based feature extraction and hand crafted feature extraction. BERT is used as a feature extraction scheme along with stylistic, structural, contextual and community based features extracted from tweets to build a machine learning based model. This work has used multilayer perceptron to detect the stances as favour, against and neutral tweets. The dataset used is provided by SardiStance task with tweets in Italian about Sardines movement. Several variants of models were built with different feature combinations and are compared against the baseline model provided by the task organisers. The models with BERT and the same combined with other contextual features proven to be the best performing models that outperform the baseline model performance

Large-scale Language Model Rescoring on Long-form Data

In this work, we study the impact of Large-scale Language Models (LLM) on Automated Speech Recognition (ASR) of YouTube videos, which we use as a source for long-form ASR. We demonstrate up to 8\% relative reduction in Word Error Eate (WER) on US English (en-us) and code-switched Indian English (en-in) long-form ASR test sets and a reduction of up to 30\% relative on Salient Term Error Rate (STER) over a strong first-pass baseline that uses a maximum-entropy based language model. Improved lattice processing that results in a lattice with a proper (non-tree) digraph topology and carrying context from the 1-best hypothesis of the previous segment(s) results in significant wins in rescoring with LLMs. We also find that the gains in performance from the combination of LLMs trained on vast quantities of available data (such as C4) and conventional neural LMs is additive and significantly outperforms a strong first-pass baseline with a maximum entropy LM. Copyright 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Comment: 5 pages, accepted in ICASSP 202

Engaging communication experts in a Delphi process to identify patient behaviors that could enhance communication in medical encounters

<p>Abstract</p> <p>Background</p> <p>The communication literature currently focuses primarily on improving physicians' verbal and non-verbal behaviors during the medical interview. The Four Habits Model is a teaching and research framework for physician communication that is based on evidence linking specific communication behaviors with processes and outcomes of care. The Model conceptualizes basic communication tasks as "Habits" and describes the sequence of physician communication behaviors during the clinical encounter associated with improved outcomes. Using the Four Habits Model as a starting point, we asked communication experts to identify the verbal communication behaviors of patients that are important in outpatient encounters.</p> <p>Methods</p> <p>We conducted a 4-round Delphi process with 17 international experts in communication research, medical education, and health care delivery. All rounds were conducted via the internet. In round 1, experts reviewed a list of proposed patient verbal communication behaviors within the Four Habits Model framework. The proposed patient verbal communication behaviors were identified based on a review of the communication literature. The experts could: approve the proposed list; add new behaviors; or modify behaviors. In rounds 2, 3, and 4, they rated each behavior for its fit (agree or disagree) with a particular habit. After each round, we calculated the percent agreement for each behavior and provided these data in the next round. Behaviors receiving more than 70% of experts' votes (either agree or disagree) were considered as achieving consensus.</p> <p>Results</p> <p>Of the 14 originally-proposed patient verbal communication behaviors, the experts modified all but 2, and they added 20 behaviors to the Model in round 1. In round 2, they were presented with 59 behaviors and 14 options to remove specific behaviors for rating. After 3 rounds of rating, the experts retained 22 behaviors. This set included behaviors such as asking questions, expressing preferences, and summarizing information.</p> <p>Conclusion</p> <p>The process identified communication tasks and verbal communication behaviors for patients similar to those outlined for physicians in the Four Habits Model. This represents an important step in building a single model that can be applied to teaching patients and physicians the communication skills associated with improved satisfaction and positive outcomes of care.</p

Changing Directions: Steering science, technology and innovation towards the Sustainable Development Goals

Science, technology and innovation are failing to address the world’s most urgent sustainability challenges, according to a major new report from the STRINGS project. ‘Changing Directions: Steering science, technology and innovation towards the Sustainable Development Goals’ is the final report of an in-depth study involving collaborators from across the globe. It highlights a glaring mismatch between the priorities of the world’s scientific communities and the United Nations’ Sustainable Development Goals, which were set up to drive change across all areas of social justice and environmental issues

Combinatorial polymeric conjugated micelles with dual cytotoxic and antiangiogenic effects for the treatment of ovarian cancer

Emerging treatment paradigms like targeting the tumor microenvironment and/or dosing as part of a metronomic regimen are anticipated to produce better outcomes in ovarian cancer, but current drug delivery systems are lacking. We have designed and evaluated paclitaxel (PTX) and rapamycin (RAP) micellar systems that can be tailored for various dosing regimens and target tumor microenvironment. Individual and mixed PTX/RAP (MIX-M) micelles are prepared by conjugating drugs to a poly­(ethylene glycol)-<i>block</i>-poly­(β-benzyl l-aspartate) using a pH-sensitive linker. The micelles release the drug(s) at pH 5.5 indicating preferential release in the acidic endosomal/lysosomal environment. Micelles exhibit antiproliferative effects in ovarian cell cancer lines (SKOV-3 (human caucasian ovarian adenocarcinoma) and ES2 (human ovarian clear cell carcinoma)) and an endothelial cell line (HUVEC; human umbilical vein endothelial cells) with the MIX-M being synergistic. The micelles also inhibited endothelial migration and tube formation. In healthy mice, micelles at 60 mg/kg/drug demonstrated no acute toxicity over 21 days. ES2 xenograft model efficacy studies at 20 mg/kg/drug dosed every 4 days and evaluated at 21 days indicate that the individual micelles exhibit antiangiogenic effects, while the MIX-M exhibited both antiangiogenic and apoptotic induction that results in significant tumor volume reduction. On the basis of our results, MIX-M micelles can be utilized to achieve synergistic apoptotic and antiangiogenic effects when treated at frequent low doses