In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in ...this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.
Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The ...stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the 'Automatic and Intelligent Data Collector and Classifier' framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The 'Custom-Net' model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the 'Custom-Net'. Furthermore, the impact of transfer learning on the 'Custom-Net' and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the 'Custom-Net' extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the 'Custom-Net' model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of 'Custom-Net' is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality.
In this article, we propose a model of face detection in risk situations to help rescue teams speed up the search of people who might need help. The proposed lightweight convolutional neural network ...(CNN) architecture is designed to detect faces of people in mines, avalanches, under water, or other dangerous situations when their face might not be very visible over surrounding background. We have designed a novel light architecture cooperating with the proposed sliding window procedure. The designed model works with maximum simplicity to support mobile devices. An output from processing presents a box on face location in the screen of device. The model was trained by using Adam and tested on various images. Results show that proposed lightweight CNN detects human faces over various textures with accuracy above 99% and precision above 98% what proves the efficiency of our proposed model.
Mixed reality environments give better chances to provide constant help to the people in need. Applied there artificial intelligence models will provide ad hoc monitoring measures, which may be the ...best chance to protect life in dangerous conditions. In this article, we present our research on mixed reality system developed to detect symptoms of unusual poses at work, home, or other environments. Recurrent neural network is using sensor readings to evaluate the situation by the minimum necessary number of body sensors working as safe indicators. Research results show that the developed system is working with very high accuracy of 99.89% using just two body sensors working in a separate mode. The system can work without any special infrastructure or development in various environments to help workers and elder people in dangerous situations.
In the paper, we investigate spatial relationship on the labor market of Pozna´n agglomeration (Poland) with unique data on job vacancies. We have developed spatial panel models to assess the search ...and matching process with a particular focus on spatial spillovers. In general, spatial models may provide different findings than regular panel models regarding returns to scale in matching technology. Moreover, we have identified global spillover effects as well as other factors that impact the job-worker matching. We underline the role of data on job vacancies: the data retrieved from commercial job portals produced much more reliable estimates than underestimated registered data.
In an apparent paradox, morbidity and mortality are lower in obese patients undergoing cardiac surgery, although the nature of this association is unclear. We sought to determine whether the obesity ...paradox observed in cardiac surgery is attributable to reverse epidemiology, bias, or confounding.
Data from the National Adult Cardiac Surgery registry for all cardiac surgical procedures performed between April 2002 and March 2013 were extracted. A parallel systematic review and meta-analysis (MEDLINE, Embase, SCOPUS, Cochrane Library) through June 2015 were also accomplished. Exposure of interest was body mass index categorized into 6 groups according to the World Health Organization classification.
A total of 401 227 adult patients in the cohort study and 557 720 patients in the systematic review were included. A U-shaped association between mortality and body mass index classes was observed in both studies, with lower mortality in overweight (adjusted odds ratio, 0.79; 95% confidence interval, 0.76-0.83) and obese class I and II (odds ratio, 0.81; 95% confidence interval, 0.76-0.86; and odds ratio, 0.83; 95% confidence interval, 0.74-0.94) patients relative to normal-weight patients and increased mortality in underweight individuals (odds ratio, 1.51; 95% confidence interval, 1.41-1.62). In the cohort study, a U-shaped relationship was observed for stroke and low cardiac output syndrome but not for renal replacement therapy or deep sternal wound infection. Counter to the reverse epidemiology hypotheses, the protective effects of obesity were less in patients with severe chronic renal, lung, or cardiac disease and greater in older patients and in those with complications of obesity, including the metabolic syndrome and atherosclerosis. Adjustments for important confounders did not alter our results.
Obesity is associated with lower risks after cardiac surgery, with consistent effects noted in multiple analyses attempting to address residual confounding and reverse causation.
Generating the extended endoplasmic reticulum (ER) network depends on microtubules, which act as tracks for motor-driven ER tubule movement, generate the force to extend ER tubules by means of ...attachment to growing microtubule plus-ends and provide static attachment points. We have analysed ER dynamics in living VERO cells and find that most ER tubule extension is driven by microtubule motors. Surprisingly, we observe that ~50% of rapid ER tubule movements occur in the direction of the centre of the cell, driven by cytoplasmic dynein. Inhibition of this movement leads to an accumulation of lamellar ER in the cell periphery. By expressing dominant-negative kinesin-1 constructs, we show that kinesin-1 drives ER tubule extension towards the cell periphery and that this motility is dependent on the KLC1B kinesin light chain splice form but not on KLC1D. Inhibition of kinesin-1 promotes a shift from tubular to lamellar morphology and slows down the recovery of the ER network after microtubule depolymerisation and regrowth. These observations reconcile previous conflicting studies of kinesin-1 function in ER motility in vivo. Furthermore, our data reveal that cytoplasmic dynein plays a role in ER motility in a mammalian cultured cell, demonstrating that ER motility is more complex than previously thought.
The practical increase of interest in intelligent technologies has caused a rapid development of all activities in terms of sensors and automatic mechanisms for smart operations. The implementations ...concentrate on technologies which avoid unnecessary actions on user side while examining health conditions. One of important aspects is the constant inspection of the skin health due to possible diseases such as melanomas that can develop under excessive influence of the sunlight. Smart homes can be equipped with a variety of motion sensors and cameras which can be used to detect and identify possible disease development. In this work, we present a smart home system which is using in-built sensors and proposed artificial intelligence methods to diagnose the skin health condition of the residents of the house. The proposed solution has been tested and discussed due to potential use in practice.
The paper presents a new security aspect for a Mobile Ad-Hoc Network (MANET)-based IoT model using the concept of artificial intelligence. The Black Hole Attack (BHA) is considered one of the most ...affecting threats in the MANET in which the attacker node drops the entire data traffic and hence degrades the network performance. Therefore, it necessitates the designing of an algorithm that can protect the network from the BHA node. This article introduces Ad-hoc On-Demand Distance Vector (AODV), a new updated routing protocol that combines the advantages of the Artificial Bee Colony (ABC), Artificial Neural Network (ANN), and Support Vector Machine (SVM) techniques. The combination of the SVM with ANN is the novelty of the proposed model that helps to identify the attackers within the discovered route using the AODV routing mechanism. Here, the model is trained using ANN but the selection of training data is performed using the ABC fitness function followed by SVM. The role of ABC is to provide a better route for data transmission between the source and the destination node. The optimized route, suggested by ABC, is then passed to the SVM model along with the node's properties. Based on those properties ANN decides whether the node is a normal or an attacker node. The simulation analysis performed in MATLAB shows that the proposed work exhibits an improvement in terms of Packet Delivery Ratio (PDR), throughput, and delay. To validate the system efficiency, a comparative analysis is performed against the existing approaches such as Decision Tree and Random Forest that indicate that the utilization of the SVM with ANN is a beneficial step regarding the detection of BHA attackers in the MANET-based IoT networks.
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