Analysis of Crime Report by Data Analytics Using Python

2021 ◽  
pp. 54-79
Author(s):  
G. Maria Jones ◽  
S. Godfrey Winster
Keyword(s):  
Machine Learning ◽  
New Technologies ◽  
Rapid Development ◽  
Performance Measure ◽  
Machine Learning Techniques ◽  
Complex Data ◽  
Time Data ◽  
Related Data ◽  
Learning Techniques ◽  
And Performance

The ever-rapid development of technology in today's world tends to provide us with a dramatic explosion of data, leading to its accumulation and thus data computation has amplified in comparison to the recent past. To manage such complex data, emerging new technologies are enabled specially to identify crime patterns, as crime-related data is escalating. These digital technologies have the potential to manipulate and also alter the pattern. To combat this, machine learning techniques are introduced which have the ability to analyse such voluminous data. In this work, the authors intend to understand and implement machine learning techniques in real time data analysis by means of Python. The detailed explanation in preparing the dataset, understanding, visualizing the data using pandas, and performance measure of algorithm is evaluated.

scholarly journals Heart disease prediction using machine learning techniques : a survey

2018 ◽  
Vol 7 (2.8) ◽  
pp. 684 ◽  
Author(s):  
V V. Ramalingam ◽  
Ayantan Dandapath ◽  
M Karthik Raja
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Learning Techniques ◽  
Vector Machines ◽  
Supervised Learning Algorithms ◽  
Life Threatening

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

scholarly journals Malicious web domain identification using online credibility and performance data by considering the class imbalance issue

2019 ◽  
Vol 119 (3) ◽  
pp. 676-696 ◽  
Author(s):  
Zhongyi Hu ◽  
Raymond Chiong ◽  
Ilung Pranata ◽  
Yukun Bao ◽  
Yuqing Lin
Keyword(s):  
Machine Learning ◽  
Class Imbalance ◽  
Performance Data ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Real World Data ◽  
Content Type ◽  
Domain Identification ◽  
Learning Techniques ◽  
And Performance

Purpose Malicious web domain identification is of significant importance to the security protection of internet users. With online credibility and performance data, the purpose of this paper to investigate the use of machine learning techniques for malicious web domain identification by considering the class imbalance issue (i.e. there are more benign web domains than malicious ones). Design/methodology/approach The authors propose an integrated resampling approach to handle class imbalance by combining the synthetic minority oversampling technique (SMOTE) and particle swarm optimisation (PSO), a population-based meta-heuristic algorithm. The authors use the SMOTE for oversampling and PSO for undersampling. Findings By applying eight well-known machine learning classifiers, the proposed integrated resampling approach is comprehensively examined using several imbalanced web domain data sets with different imbalance ratios. Compared to five other well-known resampling approaches, experimental results confirm that the proposed approach is highly effective. Practical implications This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains but also provides an effective resampling approach for handling the class imbalance issue in the area of malicious web domain identification. Originality/value Online credibility and performance data are applied to build malicious web domain identification models using machine learning techniques. An integrated resampling approach is proposed to address the class imbalance issue. The performance of the proposed approach is confirmed based on real-world data sets with different imbalance ratios.

scholarly journals Estimating Warehouse Rental Price using Machine Learning Techniques

2018 ◽  
Vol 13 (2) ◽  
pp. 235-250 ◽  
Author(s):  
Yixuan Ma ◽  
Zhenji Zhang ◽  
Alexander Ihler ◽  
Baoxiang Pan
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Estate ◽  
Rapid Development ◽  
Supply And Demand ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Logistics Industry ◽  
Real Estate Price ◽  
Learning Techniques

Boosted by the growing logistics industry and digital transformation, the sharing warehouse market is undergoing a rapid development. Both supply and demand sides in the warehouse rental business are faced with market perturbations brought by unprecedented peer competitions and information transparency. A key question faced by the participants is how to price warehouses in the open market. To understand the pricing mechanism, we built a real world warehouse dataset using data collected from the classified advertisements websites. Based on the dataset, we applied machine learning techniques to relate warehouse price with its relevant features, such as warehouse size, location and nearby real estate price. Four candidate models are used here: Linear Regression, Regression Tree, Random Forest Regression and Gradient Boosting Regression Trees. The case study in the Beijing area shows that warehouse rent is closely related to its location and land price. Models considering multiple factors have better skill in estimating warehouse rent, compared to singlefactor estimation. Additionally, tree models have better performance than the linear model, with the best model (Random Forest) achieving correlation coefficient of 0.57 in the test set. Deeper investigation of feature importance illustrates that distance from the city center plays the most important role in determining warehouse price in Beijing, followed by nearby real estate price and warehouse size.

scholarly journals Delivering Precision Medicine to Patients with Spinal Cord Disorders; Insights into Applications of Bioinformatics and Machine Learning from Studies of Degenerative Cervical Myelopathy

2021 ◽  
Author(s):  
Kalum J. Ost ◽  
David W. Anderson ◽  
David W. Cadotte
Keyword(s):  
Machine Learning ◽  
Precision Medicine ◽  
New Technologies ◽  
Machine Learning Techniques ◽  
Massive Datasets ◽  
Learning Framework ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Spinal Cord Disorders ◽  
Degenerative Cervical Myelopathy

With the common adoption of electronic health records and new technologies capable of producing an unprecedented scale of data, a shift must occur in how we practice medicine in order to utilize these resources. We are entering an era in which the capacity of even the most clever human doctor simply is insufficient. As such, realizing “personalized” or “precision” medicine requires new methods that can leverage the massive amounts of data now available. Machine learning techniques provide one important toolkit in this venture, as they are fundamentally designed to deal with (and, in fact, benefit from) massive datasets. The clinical applications for such machine learning systems are still in their infancy, however, and the field of medicine presents a unique set of design considerations. In this chapter, we will walk through how we selected and adjusted the “Progressive Learning framework” to account for these considerations in the case of Degenerative Cervical Myeolopathy. We additionally compare a model designed with these techniques to similar static models run in “perfect world” scenarios (free of the clinical issues address), and we use simulated clinical data acquisition scenarios to demonstrate the advantages of our machine learning approach in providing personalized diagnoses.

scholarly journals COMPARATIVE ANALYSIS AND EVALUATION OF THE APPLICATION OF DEEP LEARNING TECHNIQUES TO CYBERSECURITY DATASETS

10.6036/10007 ◽  
2021 ◽  
Vol 96 (5) ◽  
pp. 528-533
Author(s):  
XAVIER LARRIVA NOVO ◽  
MARIO VEGA BARBAS ◽  
VICTOR VILLAGRA ◽  
JULIO BERROCAL
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Performance ◽  
New Technologies ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Term Memory ◽  
Learning Techniques ◽  
Long Short Term Memory

Cybersecurity has stood out in recent years with the aim of protecting information systems. Different methods, techniques and tools have been used to make the most of the existing vulnerabilities in these systems. Therefore, it is essential to develop and improve new technologies, as well as intrusion detection systems that allow detecting possible threats. However, the use of these technologies requires highly qualified cybersecurity personnel to analyze the results and reduce the large number of false positives that these technologies presents in their results. Therefore, this generates the need to research and develop new high-performance cybersecurity systems that allow efficient analysis and resolution of these results. This research presents the application of machine learning techniques to classify real traffic, in order to identify possible attacks. The study has been carried out using machine learning tools applying deep learning algorithms such as multi-layer perceptron and long-short-term-memory. Additionally, this document presents a comparison between the results obtained by applying the aforementioned algorithms and algorithms that are not deep learning, such as: random forest and decision tree. Finally, the results obtained are presented, showing that the long-short-term-memory algorithm is the one that provides the best results in relation to precision and logarithmic loss.

scholarly journals Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways

2021 ◽  
Vol 8 ◽  
Author(s):  
Hayat Ali Shah ◽  
Juan Liu ◽  
Zhihui Yang ◽  
Jing Feng
Keyword(s):  
Machine Learning ◽  
Synthetic Biology ◽  
Metabolic Pathways ◽  
Machine Learning Techniques ◽  
Research Progress ◽  
Related Data ◽  
Pathway Reconstruction ◽  
Learning Techniques ◽  
Machine Leaning ◽  
Active Research

Prediction and reconstruction of metabolic pathways play significant roles in many fields such as genetic engineering, metabolic engineering, drug discovery, and are becoming the most active research topics in synthetic biology. With the increase of related data and with the development of machine learning techniques, there have many machine leaning based methods been proposed for prediction or reconstruction of metabolic pathways. Machine learning techniques are showing state-of-the-art performance to handle the rapidly increasing volume of data in synthetic biology. To support researchers in this field, we briefly review the research progress of metabolic pathway reconstruction and prediction based on machine learning. Some challenging issues in the reconstruction of metabolic pathways are also discussed in this paper.

scholarly journals SocialTERM-Extractor: Identifying and Predicting Social-Problem-Specific Key Noun Terms from a Large Number of Online News Articles Using Text Mining and Machine Learning Techniques

2019 ◽  
Vol 11 (1) ◽  
pp. 196 ◽  
Author(s):  
Jong Hwan Suh
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Text Mining ◽  
Social Problem ◽  
Social Problems ◽  
Online News ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Related Data ◽  
Learning Techniques

In the digital age, the abundant unstructured data on the Internet, particularly online news articles, provide opportunities for identifying social problems and understanding social systems for sustainability. However, the previous works have not paid attention to the social-problem-specific perspectives of such big data, and it is currently unclear how information technologies can use the big data to identify and manage the ongoing social problems. In this context, this paper introduces and focuses on social-problem-specific key noun terms, namely SocialTERMs, which can be used not only to search the Internet for social-problem-related data, but also to monitor the ongoing and future events of social problems. Moreover, to alleviate time-consuming human efforts in identifying the SocialTERMs, this paper designs and examines the SocialTERM-Extractor, which is an automatic approach for identifying the key noun terms of social-problem-related topics, namely SPRTs, in a large number of online news articles and predicting the SocialTERMs among the identified key noun terms. This paper has its novelty as the first trial to identify and predict the SocialTERMs from a large number of online news articles, and it contributes to literature by proposing three types of text-mining-based features, namely temporal weight, sentiment, and complex network structural features, and by comparing the performances of such features with various machine learning techniques including deep learning. Particularly, when applied to a large number of online news articles that had been published in South Korea over a 12-month period and mostly written in Korean, the experimental results showed that Boosting Decision Tree gave the best performances with the full feature sets. They showed that the SocialTERMs can be predicted with high performances by the proposed SocialTERM-Extractor. Eventually, this paper can be beneficial for individuals or organizations who want to explore and use social-problem-related data in a systematical manner for understanding and managing social problems even though they are unfamiliar with ongoing social problems.

Analysis of Non-Traditional Machining Processes Using Machine Learning

2021 ◽  
pp. 195-202
Author(s):  
Somnath Das
Keyword(s):  
Machine Learning ◽  
Manufacturing Systems ◽  
Complex Dynamics ◽  
Rapid Development ◽  
Machining Process ◽  
Machine Learning Techniques ◽  
Machining Processes ◽  
High Quality ◽  
New Developments ◽  
Learning Techniques

The nature of manufacturing systems faces increasingly complex dynamics to meet the demand for high quality products efficiently. One area, which experienced rapid development in terms not only of promising results but also of usability, is machine learning. New developments in certain domains such as mathematics, computer science, and the availability of easy-to-use tools, often freely available, offer great potential to transform the non-traditional machining domain and its understanding of the increase in manufacturing data. However, the field is very broad and even confusing, which presents a challenge and a barrier that hinders wide application. Here, this chapter helps to present an overview of the available machine learning techniques for improving the non-traditional machining process area. It provides a basis for the subsequent argument that the machine learning is a suitable tool for manufacturers to face these challenges head-on in non-traditional machining processes.

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