A Comparative Study of Classification Algorithms using Data Mining: Crime and Accidents in Denver City the USA

Gupta, Amit; Syed, Ali; Mohammad, Azeem; Halgamuge, Malka N.

doi:10.14569/ijacsa.2016.070753

Cited by 35 publications

(28 citation statements)

References 5 publications

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“…Furthermore, studying diverse approaches for fog computing structure [63], decision making using prediction or pattern analysis [74], [75], [76], big data databases [77] could be an exciting way to make the Internet of Things (IoT) into the future dominating technology.…”

Section: Discussionmentioning

confidence: 99%

Adoption of the Internet of Things (IoT) in Agriculture and Smart Farming towards Urban Greening: A Review

Madushanki¹,

Halgamuge²,

Wirasagoda³

et al. 2019

IJACSA

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160

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It is essential to increase the productivity of agricultural and farming processes to improve yields and costeffectiveness with new technology such as the Internet of Things (IoT). In particular, IoT can make agricultural and farming industry processes more efficient by reducing human intervention through automation. In this study, the aim to analyze recently developed IoT applications in the agriculture and farming industries to provide an overview of sensor data collections, technologies, and sub-verticals such as water management and crop management. In this review, data is extracted from 60 peer-reviewed scientific publications (2016-2018) with a focus on IoT sub-verticals and sensor data collection for measurements to make accurate decisions. Our results from the reported studies show water management is the highest subvertical (28.08%) followed by crop management (14.60%) then smart farming (10.11%). From the data collection, livestock management and irrigation management resulted in the same percentage (5.61%). In regard to sensor data collection, the highest result was for the measurement of environmental temperature (24.87%) and environmental humidity (19.79%). There are also some other sensor data regarding soil moisture (15.73%) and soil pH (7.61%). Research indicates that of the technologies used in IoT application development, Wi-Fi is the most frequently used (30.27%) followed by mobile technology (21.10%). As per our review of the research, we can conclude that the agricultural sector (76.1%) is researched considerably more than compared to the farming sector (23.8%). This study should be used as a reference for members of the agricultural industry to improve and develop the use of IoT to enhance agricultural production efficiencies. This study also provides recommendations for future research to include IoT systems' scalability, heterogeneity aspects, IoT system architecture, data analysis methods, size or scale of the observed land or agricultural domain, IoT security and threat solutions/protocols, operational technology, data storage, cloud platform, and power supplies.12 | P a g e www.ijacsa.thesai.orgThis review provides an overall picture of currently developed IoT applications in agriculture and farming between 2016 and 2018.

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Section: Discussionmentioning

confidence: 99%

Adoption of the Internet of Things (IoT) in Agriculture and Smart Farming towards Urban Greening: A Review

Madushanki¹,

Halgamuge²,

Wirasagoda³

et al. 2019

IJACSA

Self Cite

160

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“…Our study does not cover memory consumption in this case nonetheless it can be noted from [21] the large number of trees that consumes a lot of RAM space. Machine learning seems to be a great tool since it verifies some unexplained correlations in different attributes in any application [24]- [27].…”

Section: Discussionmentioning

confidence: 99%

Impact of Different Data Types on Classifier Performance of Random Forest, Naïve Bayes, and K-Nearest Neighbors Algorithms

Singh¹,

Halgamuge²,

Lakshmiganthan³

2017

ijacsa

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Abstract-This study aims to evaluate impact of three different data types (Text only, Numeric Only and Text + Numeric) on classifier performance (Random Forest, k-Nearest Neighbor (kNN) and Naïve Bayes (NB) algorithms). The classification problems in this study are explored in terms of mean accuracy and the effects of varying algorithm parameters over different types of datasets. This content analysis has been examined through eight different datasets taken from UCI to train models for all three algorithms. The results obtained from this study clearly show that RF and kNN outperform NB. Furthermore, kNN and RF perform relatively the same in terms of mean accuracy nonetheless kNN takes less time to train a model. The changing numbers of attributes in datasets have no effect on Random Forest, whereas Naïve Bayes mean accuracy fluctuates up and down that leads to a lower mean accuracy, whereas, kNN mean accuracy increases and ends with higher accuracy. Additionally, changing number of trees has no significant effects on mean accuracy of the Random forest, however, the time to train the model has increased greatly. Random Forest and k-Nearest Neighbor are proved to be the best classifiers for any type of dataset. Thus, Naïve Bayes can outperform other two algorithms if the feature variables are in a problem space and are independent. Besides Random forests, it takes highest computational time and Naïve Bayes takes lowest. The k-Nearest Neighbor requires finding an optimal number of k for improved performance at the cost of computation time. Similarly, changing the number of attributes that effect Naïve Bayes and k-Nearest Neighbor performance nevertheless not the Random forest. This study can be extended by researchers who use the parametric method to analyze results.

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“…Machine learning concepts have also been used in many research communities, including medicine [24], [25], crime prediction [26] and education [3]. However, no single study exists which adequately covers machine learning concept in bioelectromagnetics domain.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning for Bioelectromagnetics: Prediction Model using Data of Weak Radiofrequency Radiation Effect on Plants

Halgamuge¹

2017

ijacsa

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Abstract-Plant sensitivity and its bio-effects on non-thermal weak radio-frequency electromagnetic fields (RF-EMF) identifying key parameters that affect plant sensitivity that can change/unchange by using big data analytics and machine learning concepts are quite significant. Despite its benefits, there is no single study that adequately covers machine learning concept in Bioelectromagnetics domain yet. This study aims to demonstrate the usefulness of Machine Learning algorithms for predicting the possible damages of electromagnetic radiations from mobile phones and base station on plants and consequently, develops a prediction model of plant sensitivity to RF-EMF. We used rawdata of plant exposure from our previous review study (extracted data from 45 peer-reviewed scientific publications published between 1996-2016 with 169 experimental case studies carried out in the scientific literature) that predicts the potential effects of RF-EMF on plants. We also used values of six different attributes or parameters for this study: frequency, specific absorption rate (SAR), power flux density, electric field strength, exposure time and plant type (species). The results demonstrated that the adaptation of machine learning algorithms (classification and clustering) to predict 1) what conditions will RF-EMF exposure to a plant of a given species may not produce an effect; 2) what frequency and electric field strength values are safer; and 3) which plant species are affected by RF-EMF. Moreover, this paper also illustrates the development of optimal attribute selection protocol to identify key parameters that are highly significant when designing the in-vitro practical standardized experimental protocols. Our analysis also illustrates that Random Forest classification algorithm outperforms with highest classification accuracy by 95.26% (0.084 error) with only 4% of fluctuation among algorithm measured. The results clearly show that using K-Means clustering algorithm, demonstrated that the Pea, Mungbean and Duckweeds plants are more sensitive to RF-EMF (p ≤ 0.0001). The sample size of reported 169 experimental case studies, perhaps low significant in a statistical sense, nonetheless, this analysis still provides useful insight of exploiting Machine Learning in Bioelectromagnetics domain. As a direct outcome of this research, more efficient RF-EMF exposure prediction tools can be developed to improve the quality of epidemiological studies and the long-term experiments using whole organisms.

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A Comparative Study of Classification Algorithms using Data Mining: Crime and Accidents in Denver City the USA

Cited by 35 publications

References 5 publications

Adoption of the Internet of Things (IoT) in Agriculture and Smart Farming towards Urban Greening: A Review

Adoption of the Internet of Things (IoT) in Agriculture and Smart Farming towards Urban Greening: A Review

Impact of Different Data Types on Classifier Performance of Random Forest, Naïve Bayes, and K-Nearest Neighbors Algorithms

Machine Learning for Bioelectromagnetics: Prediction Model using Data of Weak Radiofrequency Radiation Effect on Plants

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