A systematic review of fundamental and technical analysis of stock market predictions

Nti, Isaac Kofi; Adekoya, Adebayo Felix; Weyori, Benjamin Asubam

doi:10.1007/s10462-019-09754-z

Cited by 332 publications

(184 citation statements)

References 131 publications

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“…26 However, the Decision Tree (DT) algorithm was adopted for modeling our dataset based on its simplicity and efficiency with small data size, as reported in the literature. 26 However, the Decision Tree (DT) algorithm was adopted for modeling our dataset based on its simplicity and efficiency with small data size, as reported in the literature.…”

Section: Predictive Modelmentioning

confidence: 99%

“…The root mean square error (RMSE) and mean absolute percentage error (MAPE) as expressed in Equations (8) and (9) 26 were employed to evaluate the performance characteristics of the decision tree as follows: The root mean square error (RMSE) and mean absolute percentage error (MAPE) as expressed in Equations (8) and (9) 26 were employed to evaluate the performance characteristics of the decision tree as follows:…”

Section: Predictive Modelmentioning

confidence: 99%

“…26 However, the Decision Tree (DT) algorithm was adopted for modeling our dataset based on its simplicity and efficiency with small data size, as reported in the literature. 26,27 A DT is a form of a flow-chart-like tree structure that uses a branch off method to spell out every distinct likely result of a decision. Every individual node within the tree embodies a test on a precise variable, and each branch is the outcome of that test.…”

Section: Predictive Modelmentioning

confidence: 99%

“…where x ′ , the new value obtained after normalization; x, the value to be normalized, x min and x max are the minimum and maximum value of the dataset. The root mean square error (RMSE) and mean absolute percentage error (MAPE) as expressed in Equations (8) and (9) 26 were employed to evaluate the performance characteristics of the decision tree as follows:…”

Section: Predictive Modelmentioning

confidence: 99%

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Assessment and prediction of earthing resistance in domestic installation

Nti

Appiah

Nyarko‐Boateng

2020

Engineering Reports

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Before the introduction of the Ghana Electrical Wiring Regulations 2012 (L.I. 2008), most residential wiring within Sunyani Municipality was carried out by unlicensed artisans. This situation imposed a need to investigate the effectiveness of the earthing systems carried in the Sunyani municipality. Therefore, the current study sought to evaluate earthing resistance effectiveness within the Sunyani Municipality by measuring the earthing resistance of 1004 homes with an earthing resistance tester from January 2016 to April 2018. Again, we predict future resistance based on measured values. Out of 1004 buildings surveyed, 834 had an earthing system in place. Twenty‐eight buildings, recorded an earth electrode test within 0.0 to 10 Ω representing 3.36%. A high percentage (96.64%) had their earth electrode resistance above the acceptable value by the Energy Commission of Ghana. The results revealed that the overall effectiveness of earthing in the domestic installation in Sunyani Municipality is below average compared with Ghana Energy Commission benchmark.

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Section: Predictive Modelmentioning

confidence: 99%

Section: Predictive Modelmentioning

confidence: 99%

“…26 However, the Decision Tree (DT) algorithm was adopted for modeling our dataset based on its simplicity and efficiency with small data size, as reported in the literature. 26,27 A DT is a form of a flow-chart-like tree structure that uses a branch off method to spell out every distinct likely result of a decision. Every individual node within the tree embodies a test on a precise variable, and each branch is the outcome of that test.…”

Section: Predictive Modelmentioning

confidence: 99%

Section: Predictive Modelmentioning

confidence: 99%

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Assessment and prediction of earthing resistance in domestic installation

Nti

Appiah

Nyarko‐Boateng

2020

Engineering Reports

Self Cite

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“…ML is a subcategory of artificial intelligence, which, uses statistical and computational tools to offer humanlike abilities to computers [17,18]. Thus, ML offers automation and enhancement of the learning process of machines based on their experiences without being programmed (no human assistance) [19].…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

A Web-Based Skin Disease Diagnosis Using Convolutional Neural Networks

Akyeramfo-Sam¹,

Philip²,

Yeboah³

et al. 2019

IJITCS

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Skin diseases are reported to be the most common disease in humans among all age groups and a significant root of infection in sub-Saharan Africa. The diagnosis of skin diseases using conventional approaches involves several tests. Due to this, the diagnosis process is seen to be intensely laborious, time-consuming and requires an extensive understanding of the domain. The enhancement of computer vision through artificial intelligence has led to a more straightforward and quicker way of detecting patterns in images, which can be harnessed to equip diagnosis process. Despite the breakthrough in technology, the dermatological process in Ghana is yet to be automated, making the diagnosis process complicated and time-consuming. Hence, this study sought to propose a web-based skin disease detection system named medilab-plus using a convolutional neural network classifier built upon the Tensorflow framework for detecting (atopic dermatitis, acne vulgaris, and scabies) skin diseases. Experimental results of the proposed system exhibited classification accuracy of 88% for atopic dermatitis, 85% for acne vulgaris, and 84.7% for scabies. Again, the computational time (0.0001 seconds) of the proposed system implies that any dermatologist, who decides to implement this study, can attend to not less than 1,440 patients a day compared to the manual diagnosis process. It is estimated that the proposed system will enhance accuracy and offer fasting diagnosis results than the traditional method, which makes this system a trustworthy and resourceful for dermatological disease detection. Additionally, the system can serve as a realtime learning platform for students studying dermatology in medical schools in Ghana.

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