scholarly journals Multi-Input Convolutional Neural Networks for Automatic Pollen Classification

2021 ◽  
Vol 11 (24) ◽  
pp. 11707
Author(s):  
Mihai Boldeanu ◽  
Horia Cucu ◽  
Corneliu Burileanu ◽  
Luminița Mărmureanu
Keyword(s):  
Network Architecture ◽  
Relative Reduction ◽  
Pollen Monitoring ◽  
Current State ◽  
Novel Approach ◽  
Manual Counting ◽  
Pollen Species ◽  
Multiple Input ◽  
Particle Measurement ◽  
Using Data

Pollen allergies are a cause of much suffering for an increasing number of individuals. Current pollen monitoring techniques are lacking due to their reliance on manual counting and classification of pollen by human technicians. In this study, we present a neural network architecture capable of distinguishing pollen species using data from an automated particle measurement device. This work presents an improvement over the current state of the art in the task of automated pollen classification, using fluorescence spectrum data of aerosol particles. We obtained a relative reduction in the error rate of over 48%, from 27% to 14%, for one of the datasets, with similar improvements for the other analyzed datasets. We also use a novel approach for doing hyperparameter tuning for multiple input networks.

Digitalization in the system of anti-corruption measures

2020 ◽  
Vol 22 (5) ◽  
pp. 51-55
Author(s):  
OLEG N. KORCHAGIN ◽  
◽  
ANASTASIA V. LYADSKAYA ◽  
Keyword(s):  
Big Data ◽  
Law Enforcement ◽  
Public Administration ◽  
Network Architecture ◽  
Digital Technologies ◽  
Current State ◽  
Foreign Countries ◽  
Separate Area ◽  
Private Business Sector ◽  
The Given

The article is devoted to the current state of digitalization aimed at solving urgent problems of combating corruption in the field of public administration and private business sector. The work considers the experience of foreign countries and the influence of digital technologies on the fight against corruption. It is noted that the digitalization of public administration is becoming one of the decisive factors for increasing the efficiency of the anti-corruption system and improving management mechanisms. Big Data, if integrated and structured according to the given parameters, allows the implementation of legislative, law enforcement, control and supervisory and law enforcement activities reliably and transparently. Big Data tools allow us to analyze processes, identify dependencies and predict corruption risks. The author describes the most significant problems that complicate the transfer of offline technologies into the online environment. The paper analyzes promising directions for the development of digital technologies that would lead to solving the arising problems, as well as to implement tasks that previously seemed unreachable. The article also describes current developments in the field of collecting and managing large amounts of data, the “Internet of Things”, modern network architecture, and other advances in the field of IT; the work provides applied examples of their potential use in the field of combating corruption. The study gives reasons that, in the context of combating corruption, digitalization should be allocated in a separate area of activity that is controlled and regulated by the state.

scholarly journals Extending Geodemographics Using Data Primitives: A Review and a Methodological Proposal

2021 ◽  
Vol 10 (6) ◽  
pp. 386
Author(s):  
Jennie Gray ◽  
Lisa Buckner ◽  
Alexis Comber
Keyword(s):  
Social Dynamics ◽  
Feature Space ◽  
Social Processes ◽  
Multiple Time ◽  
Current State ◽  
Cluster Label ◽  
Static Data ◽  
The Social ◽  
Using Data

This paper reviews geodemographic classifications and developments in contemporary classifications. It develops a critique of current approaches and identifiea a number of key limitations. These include the problems associated with the geodemographic cluster label (few cluster members are typical or have the same properties as the cluster centre) and the failure of the static label to describe anything about the underlying neighbourhood processes and dynamics. To address these limitations, this paper proposed a data primitives approach. Data primitives are the fundamental dimensions or measurements that capture the processes of interest. They can be used to describe the current state of an area in a multivariate feature space, and states can be compared over multiple time periods for which data are available, through for example a change vector approach. In this way, emergent social processes, which may be too weak to result in a change in a cluster label, but are nonetheless important signals, can be captured. As states are updated (for example, as new data become available), inferences about different social processes can be made, as well as classification updates if required. State changes can also be used to determine neighbourhood trajectories and to predict or infer future states. A list of data primitives was suggested from a review of the mechanisms driving a number of neighbourhood-level social processes, with the aim of improving the wider understanding of the interaction of complex neighbourhood processes and their effects. A small case study was provided to illustrate the approach. In this way, the methods outlined in this paper suggest a more nuanced approach to geodemographic research, away from a focus on classifications and static data, towards approaches that capture the social dynamics experienced by neighbourhoods.

scholarly journals Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zekun Xu ◽  
Eric Laber ◽  
Ana-Maria Staicu ◽  
B. Duncan X. Lascelles
Keyword(s):  
High Frequency ◽  
Activity Patterns ◽  
Treatment Strategies ◽  
Therapeutic Effects ◽  
Activity Levels ◽  
Activity Data ◽  
Novel Approach ◽  
High Frequency Activity ◽  
Using Data ◽  
Chronic Pain Conditions

AbstractOsteoarthritis (OA) is a chronic condition often associated with pain, affecting approximately fourteen percent of the population, and increasing in prevalence. A globally aging population have made treating OA-associated pain as well as maintaining mobility and activity a public health priority. OA affects all mammals, and the use of spontaneous animal models is one promising approach for improving translational pain research and the development of effective treatment strategies. Accelerometers are a common tool for collecting high-frequency activity data on animals to study the effects of treatment on pain related activity patterns. There has recently been increasing interest in their use to understand treatment effects in human pain conditions. However, activity patterns vary widely across subjects; furthermore, the effects of treatment may manifest in higher or lower activity counts or in subtler ways like changes in the frequency of certain types of activities. We use a zero inflated Poisson hidden semi-Markov model to characterize activity patterns and subsequently derive estimators of the treatment effect in terms of changes in activity levels or frequency of activity type. We demonstrate the application of our model, and its advance over traditional analysis methods, using data from a naturally occurring feline OA-associated pain model.

scholarly journals Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

2021 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Parag Narkhede ◽  
Rahee Walambe ◽  
Shruti Mandaokar ◽  
Pulkit Chandel ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Network Architecture ◽  
Cost Effective ◽  
Sensor Data ◽  
Dense Layer ◽  
Gaseous Emissions ◽  
Multiple Sensors ◽  
Single Output ◽  
Novel Approach ◽  
Single Sensor

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.

scholarly journals Lightweight Stacked Hourglass Network for Human Pose Estimation

2020 ◽  
Vol 10 (18) ◽  
pp. 6497
Author(s):  
Seung-Taek Kim ◽  
Hyo Jong Lee
Keyword(s):  
Receptive Field ◽  
Pose Estimation ◽  
Network Architecture ◽  
Human Pose Estimation ◽  
Current State ◽  
Reduction Methods ◽  
Substantial Progress ◽  
Human Pose ◽  
Residual Block ◽  
Point Detection

Human pose estimation is a problem that continues to be one of the greatest challenges in the field of computer vision. While the stacked structure of an hourglass network has enabled substantial progress in human pose estimation and key-point detection areas, it is largely used as a backbone network. However, it also requires a relatively large number of parameters and high computational capacity due to the characteristics of its stacked structure. Accordingly, the present work proposes a more lightweight version of the hourglass network, which also improves the human pose estimation performance. The new hourglass network architecture utilizes several additional skip connections, which improve performance with minimal modifications while still maintaining the number of parameters in the network. Additionally, the size of the convolutional receptive field has a decisive effect in learning to detect features of the full human body. Therefore, we propose a multidilated light residual block, which expands the convolutional receptive field while also reducing the computational load. The proposed residual block is also invariant in scale when using multiple dilations. The well-known MPII and LSP human pose datasets were used to evaluate the performance using the proposed method. A variety of experiments were conducted that confirm that our method is more efficient compared to current state-of-the-art hourglass weight-reduction methods.

scholarly journals Using Evidence-Based Practice and Data-Based Decision Making in Inclusive Education

2021 ◽  
Vol 11 (3) ◽  
pp. 129
Author(s):  
Gabrielle Wilcox ◽  
Cristina Fernandez Conde ◽  
Amy Kowbel
Keyword(s):  
Decision Making ◽  
Inclusive Education ◽  
Evidence Based ◽  
Teacher Capacity ◽  
Evidence Based Practices ◽  
Full Inclusion ◽  
Current State ◽  
Inclusive Settings ◽  
Using Data ◽  
Data Based Decision Making

There are longstanding calls for inclusive education for all regardless of student need or teacher capacity to meet those needs. Unfortunately, there are little empirical data to support full inclusion for all students and even less information on the role of data-based decision making in inclusive education specifically, even though there is extensive research on the effectiveness of data-based decision making. In this article, we reviewed what data-based decision making is and its role in education, the current state of evidence related to inclusive education, and how data-based decision making can be used to support decisions for students with reading disabilities and those with intellectual disabilities transitioning to adulthood. What is known about evidence-based practices in supporting reading and transition are reviewed in relationship to the realities of implementing these practices in inclusive education settings. Finally, implications for using data-based decisions in inclusive settings are discussed.

scholarly journals SD-UNet: Stripping down U-Net for Segmentation of Biomedical Images on Platforms with Low Computational Budgets

Diagnostics ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 110 ◽  
Author(s):  
Pius Kwao Gadosey ◽  
Yujian Li ◽  
Enock Adjei Agyekum ◽  
Ting Zhang ◽  
Zhaoying Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Tumor Segmentation ◽  
Biomedical Data ◽  
Electron Microscopic ◽  
Brain Tumor Segmentation ◽  
Current State ◽  
Expected Performance ◽  
Computational Resources ◽  
Medical Segmentation

During image segmentation tasks in computer vision, achieving high accuracy performance while requiring fewer computations and faster inference is a big challenge. This is especially important in medical imaging tasks but one metric is usually compromised for the other. To address this problem, this paper presents an extremely fast, small and computationally effective deep neural network called Stripped-Down UNet (SD-UNet), designed for the segmentation of biomedical data on devices with limited computational resources. By making use of depthwise separable convolutions in the entire network, we design a lightweight deep convolutional neural network architecture inspired by the widely adapted U-Net model. In order to recover the expected performance degradation in the process, we introduce a weight standardization algorithm with the group normalization method. We demonstrate that SD-UNet has three major advantages including: (i) smaller model size (23x smaller than U-Net); (ii) 8x fewer parameters; and (iii) faster inference time with a computational complexity lower than 8M floating point operations (FLOPs). Experiments on the benchmark dataset of the Internatioanl Symposium on Biomedical Imaging (ISBI) challenge for segmentation of neuronal structures in electron microscopic (EM) stacks and the Medical Segmentation Decathlon (MSD) challenge brain tumor segmentation (BRATs) dataset show that the proposed model achieves comparable and sometimes better results compared to the current state-of-the-art.

scholarly journals Beam division multiple access for millimeter wave massive MIMO: Hybrid zero-forcing beamforming with user selection

2022 ◽  
Vol 12 (1) ◽  
pp. 445
Author(s):  
Hong Son Vu ◽  
Kien Truong ◽  
Minh Thuy Le
Keyword(s):  
Multiple Access ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Zero Forcing ◽  
Multiuser Interference ◽  
Novel Approach ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Promising Solution

<p>Massive multiple-input multiple-output (MIMO) systems are considered a promising solution to minimize multiuser interference (MUI) based on simple precoding techniques with a massive antenna array at a base station (BS). This paper presents a novel approach of beam division multiple access (BDMA) which BS transmit signals to multiusers at the same time via different beams based on hybrid beamforming and user-beam schedule. With the selection of users whose steering vectors are orthogonal to each other, interference between users is significantly improved. While, the efficiency spectrum of proposed scheme reaches to the performance of fully digital solutions, the multiuser interference is considerably reduced.</p>

Strategic 360 Feedback for Organization Development

2019 ◽  
pp. 213-234 ◽  
Author(s):  
Allan H. Church ◽  
W. Warner Burke
Keyword(s):  
Transformational Leadership ◽  
Human Resources ◽  
Organization Development ◽  
Large Scale ◽  
Model Case ◽  
Case Examples ◽  
Current State ◽  
360 Feedback ◽  
Organizational Framework ◽  
Using Data

This chapter focuses on the application of Strategic 360 Feedback specifically for large-scale organization development (OD) and change interventions. The emphasis is on (a) ways in which using data-based feedback for OD efforts is similar to and different from other applications and (b) the origins, evolution, and current state of the method as a key tool for OD practitioners. The chapter begins with an overview of the role and key differentiators of strategic 360 Feedback for OD and change-related interventions. Following a discussion of the importance of linking the process to a broader strategic organizational framework such as the Burke–Litwin model, case examples are provided for using 360 Feedback as either a transactional (managerial) or a transformational (leadership) lever for change. The chapter concludes with summary observations about the evolution and potential future of 360 Feedback for OD interventions, with an emphasis on trends in technology and the digitization of human resources.

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