scholarly journals Crime forecasting: a machine learning and computer vision approach to crime prediction and prevention

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Neil Shah ◽  
Nandish Bhagat ◽  
Manan Shah
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Police Officers ◽  
Preventive Measures ◽  
Crime Prediction ◽  
Prediction And Prevention ◽  
Before And After ◽  
Psychological Harm ◽  
Current Scenario ◽  
Crime Detection

AbstractA crime is a deliberate act that can cause physical or psychological harm, as well as property damage or loss, and can lead to punishment by a state or other authority according to the severity of the crime. The number and forms of criminal activities are increasing at an alarming rate, forcing agencies to develop efficient methods to take preventive measures. In the current scenario of rapidly increasing crime, traditional crime-solving techniques are unable to deliver results, being slow paced and less efficient. Thus, if we can come up with ways to predict crime, in detail, before it occurs, or come up with a “machine” that can assist police officers, it would lift the burden of police and help in preventing crimes. To achieve this, we suggest including machine learning (ML) and computer vision algorithms and techniques. In this paper, we describe the results of certain cases where such approaches were used, and which motivated us to pursue further research in this field. The main reason for the change in crime detection and prevention lies in the before and after statistical observations of the authorities using such techniques. The sole purpose of this study is to determine how a combination of ML and computer vision can be used by law agencies or authorities to detect, prevent, and solve crimes at a much more accurate and faster rate. In summary, ML and computer vision techniques can bring about an evolution in law agencies.

MACHINE LEARNING APPLICATION: IMAGE PROCESSING

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Ayush Gupta
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Early Stage ◽  
Human Beings ◽  
Significant Information ◽  
Helpful Tool ◽  
Early Stages ◽  
Literature Reviews ◽  
Current Scenario

-In the current scenario of the data world, the data holds significant information if processed correctly. The data can be in the form of images which can prove to be a boon in deriving the useful insights from it in order to get the knowledge of things at an early stage itself. But the matter of concern is deriving the information from the images will be a tedious task for human beings and would incur a heavy cost and time. So, an easy and cheaper technique is to teach a machine efficiently to do the task for us. The concept of using Machines to do human tasks is known as Machine Learning. In this paper, I present various literature reviews regarding image processing in Machine learning and how image processing has helped in identifying the issues at early stages so that they can be resolved easily without causing much harm. Also, image processing has been a helpful tool in computer vision.

scholarly journals Survey of Machine Learning Applications of Convolutional Neural Networks to Medical Image Analysis

2021 ◽  
Vol 9 (11) ◽  
pp. 1186-1196
Author(s):  
Dr. K. Naveen Kumar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Learning Techniques ◽  
Before And After

Abstract: Recently, a machine learning (ML) area called deep learning emerged in the computer-vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in many fields, including medical image analysis, have started actively participating in the explosively growing field of deep learning. In this paper, deep learning techniques and their applications to medical image analysis are surveyed. This survey overviewed 1) standard ML techniques in the computer-vision field, 2) what has changed in ML before and after the introduction of deep learning, 3) ML models in deep learning, and 4) applications of deep learning to medical image analysis. The comparisons between MLs before and after deep learning revealed that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is learning image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The survey of deep learningalso revealed that there is a long history of deep-learning techniques in the class of ML with image input, except a new term, “deep learning”. “Deep learning” even before the term existed, namely, the class of ML with image input was applied to various problems in medical image analysis including classification between lesions and nonlesions, classification between lesion types, segmentation of lesions or organs, and detection of lesions. ML with image input including deep learning is a verypowerful, versatile technology with higher performance, which can bring the current state-ofthe-art performance level of medical image analysis to the next level, and it is expected that deep learning will be the mainstream technology in medical image analysis in the next few decades. “Deep learning”, or ML with image input, in medical image analysis is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical image analysis in the next few decades. Keywords: Deep learning, Convolutional neural network, Massive-training artificial neural network, Computer-aided diagnosis, Medical image analysis, Classification (key words)

Highlighted of Ramadan Fasting on Diabetes-Study of Complications, Biochemical Aspects and Anthropometry

2019 ◽  
Vol 26 (3) ◽  
pp. 227-234 ◽  
Author(s):  
Meriem Bencharif ◽  
Ibrahim Sersar ◽  
Maroua Bentaleb ◽  
Fatima Zohra Boutata ◽  
Youcef Benabbas
Keyword(s):  
Nutrition Education ◽  
Preventive Measures ◽  
Glucose Monitoring ◽  
Public Information ◽  
Blood Glucose Monitoring ◽  
Loss Of Consciousness ◽  
Ramadan Fasting ◽  
Balanced Diet ◽  
Before And After ◽  
And Gender

Abstract Background and aims: The diabetic exempted from fasting by religion, wishing or not to observe the fast, is exposed like any other during Ramadan to a change in lifestyle. The objective of this study was to highlight the effects of Ramadan fasting on diabetes. Material and methods: Multicentre study on 899 diabetics was carried to collect data on the behaviour of diabetics with regard to the fast of Ramadan, biochemicals and anthropometry parameters. Results. The sample consists of 541 diabetic fasters (DTMF) and 358 no fasters. The causes of interruption of fasting were: hypoglycemia (82.4%), dehydration (44.5%), hyperglycemia (12.6%), high blood pressure (13.7%), loss of consciousness (8.3%). The risk factors related to fasting for DTMF were the type of diabetes and gender. Discussion and modifications about dietary, blood glucose monitoring and nutritional education sessions showed a protective effect against the occurrence of hypo and hyperglycemia and loss of consciousness. Decreasing differences were noted for Hb1Ac, LDL and Total-Cholesterol between before and after Ramadan. The weight of DTMF decreased in post-Ramadan (p=0.0000). Conclusion. There is a need to consider regular preventive measures based on public information on the effects of diabetes related complications and the benefits of a balanced diet combined with regular physical activity in nutrition education sessions.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

Occupational risk and early signs of disadaptation in young traffic police officers working in a shift schedule

2020 ◽  
Vol 60 (11) ◽  
pp. 884-887
Author(s):  
Ekaterina F. Chernikova
Keyword(s):  
Working Conditions ◽  
Police Officers ◽  
Road Safety ◽  
Preventive Measures ◽  
Ethical Standards ◽  
Traffic Police ◽  
Professional Activities ◽  
Early Signs ◽  
Scientific Justification ◽  
Years Of Service

Introduction. In the course of their professional activities, traffic police inspectors of State Road Safety Inspectorate (SRSI) are exposed to harmful working conditions. The aim of study is to provide scientific justification for the periods of service of inspectors that are important for the diagnosis of early signs of professionally caused diseases. Materials and methods. The study was carried out in a group of traffic police inspectors of the traffic police in accordance with the ethical standards set forth in the Declaration of Helsinki, 1975 (with additions, 1983), a positive conclusion of the ethical committee. The age of the traffic police officers was 24-50 years old, the experience was 1-19 years (average values 34±0.46 and 8.21±0.40 years). Results. In the course of the study, a class of working conditions was determined-3.4. low-level traffic police officers showed signs of disadaptation, a high risk of morbidity with temporary disability and professionally caused pathology of the cardiovascular, musculoskeletal, nervous, endocrine, and digestive systems. Conclusions. It is advisable to conduct an in-depth preventive medical examination of inspectors after 1-2 years of service. The first 4 years of service are important for preventive measures.

scholarly journals Spinal Muscular Atrophy after Nusinersen Therapy: Improved Physiology in Pediatric Patients with No Significant Change in Urine, Serum, and Liquor 1H-NMR Metabolomes in Comparison to an Age-Matched, Healthy Cohort

Metabolites ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 206
Author(s):  
Leon Deutsch ◽  
Damjan Osredkar ◽  
Janez Plavec ◽  
Blaž Stres
Keyword(s):  
Machine Learning ◽  
Spinal Muscular Atrophy ◽  
1H Nmr ◽  
Pediatric Patients ◽  
Muscular Atrophy ◽  
Male And Female ◽  
Before And After ◽  
Healthy Cohort ◽  
Males And Females ◽  
Urine Serum

Spinal muscular atrophy (SMA) is a genetically heterogeneous group of rare neuromuscular diseases and was until recently the most common genetic cause of death in children. The effects of 2-month nusinersen therapy on urine, serum, and liquor 1H-NMR metabolomes in SMA males and females were not explored yet, especially not in comparison to the urine 1H-NMR metabolomes of matching male and female cohorts. In this prospective, single-centered study, urine, serum, and liquor samples were collected from 25 male and female pediatric patients with SMA before and after 2 months of nusinersen therapy and urine samples from a matching healthy cohort (n = 125). Nusinersen intrathecal application was the first therapy for the treatment of SMA by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA). Metabolomes were analyzed using targeted metabolomics utilizing 600 MHz 1H-NMR, parametric and nonparametric multivariate statistical analyses, machine learning, and modeling. Medical assessment before and after nusinersen therapy showed significant improvements of movement, posture, and strength according to various medical tests. No significant differences were found in metabolomes before and after nusinersen therapy in urine, serum, and liquor samples using an ensemble of statistical and machine learning approaches. In comparison to a healthy cohort, 1H-NMR metabolomes of SMA patients contained a reduced number and concentration of urine metabolites and differed significantly between males and females as well. Significantly larger data scatter was observed for SMA patients in comparison to matched healthy controls. Machine learning confirmed urinary creatinine as the most significant, distinguishing SMA patients from the healthy cohort. The positive effects of nusinersen therapy clearly preceded or took place devoid of significant rearrangements in the 1H-NMR metabolomic makeup of serum, urine, and liquor. Urine creatinine was successful at distinguishing SMA patients from the matched healthy cohort, which is a simple systemic novelty linking creatinine and SMA to the physiology of inactivity and diabetes, and it facilitates the monitoring of SMA disease in pediatric patients through non-invasive urine collection.

scholarly journals A Systematic Survey of ML Datasets for Prime CV Research Areas—Media and Metadata

Data ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 12
Author(s):  
Helder F. Castro ◽  
Jaime S. Cardoso ◽  
Maria T. Andrade
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Qualitative Analysis ◽  
Continuous Growth ◽  
Scientific Databases ◽  
Extensive Survey ◽  
Research Areas ◽  
Current State ◽  
Global View ◽  
Cooperative Production

The ever-growing capabilities of computers have enabled pursuing Computer Vision through Machine Learning (i.e., MLCV). ML tools require large amounts of information to learn from (ML datasets). These are costly to produce but have received reduced attention regarding standardization. This prevents the cooperative production and exploitation of these resources, impedes countless synergies, and hinders ML research. No global view exists of the MLCV dataset tissue. Acquiring it is fundamental to enable standardization. We provide an extensive survey of the evolution and current state of MLCV datasets (1994 to 2019) for a set of specific CV areas as well as a quantitative and qualitative analysis of the results. Data were gathered from online scientific databases (e.g., Google Scholar, CiteSeerX). We reveal the heterogeneous plethora that comprises the MLCV dataset tissue; their continuous growth in volume and complexity; the specificities of the evolution of their media and metadata components regarding a range of aspects; and that MLCV progress requires the construction of a global standardized (structuring, manipulating, and sharing) MLCV “library”. Accordingly, we formulate a novel interpretation of this dataset collective as a global tissue of synthetic cognitive visual memories and define the immediately necessary steps to advance its standardization and integration.

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