Human activity recognition using mixture of heterogeneous features and sequential minimal optimization

Naveed, Humza; Khan, Gulraiz; Khan, Asad; Siddiqi, Aiman; Khan, Muhammad Usman Ghani

doi:10.1007/s13042-018-0870-1

Cited by 31 publications

(12 citation statements)

References 32 publications

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“…One of them is that the model has useful analytics, and is sensitive and dependable [ 49 , 50 ]. Another reason is that it can allow policymakers to conduct analysis with their specific feature inputs or relevant procurement conditions [ 12 ]. The model can distinguish the differences in the prices of medicines in the pharmaceutical market by using relevant features: the characteristics of medicine product [ 27 ], the competitive potential of manufacturers or suppliers [ 8 ], the region-based health services system [ 5 ], and the procurement conditions [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

“…The SMO algorithm has been used in many fields [ 12 , 23 , 24 ] and demonstrated to get very good performance with sparse data inputs, even which imbalanced data, because it requires much shorter kernel computation time. It has also been demonstrated to have a high predictive capability to find out optimal values and unknown patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The individual features and the outcome can have a little relationship if the prediction is accurate. More specifically, a technique of sequential minimal optimization (SMO) [ 11 ] which is an effective algorithm in support vector machine learning, is widely used to classify several datasets based on the features given [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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How Well Does a Sequential Minimal Optimization Model Perform in Predicting Medicine Prices for Procurement System?

Pentrakan

Yang

Wong

2021

IJERPH

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The lack of an efficient approach in managing pharmaceutical prices in the procurement system led to a substantial burden on government budgets. In Thailand, although the reference price policy was implemented to contain the drug expenditure, there have been some challenges with the price dispersion of medicines and pricing information transparency. This phenomenon calls for the development of a potential algorithm to estimate appropriate prices for medical products. To serve this purpose, in this paper, we first developed the model by the sequential minimal optimization (SMO) algorithm for predicting the range of the prices for each medicine, using the Waikato environment for knowledge analysis software, and applying feature selection techniques also to examine improving predictive accuracy. We used the dataset comprised of 2424 records listed on the procurement system in Thailand from January to March 2019 in the application and used a 10-fold cross-validation test to validate the model. The results demonstrated that the model derived by the SMO algorithm with the gain ratio selection method provided good performance at an accuracy of approximately 92.62%, with high sensitivity and precision. Additionally, we found that the model can distinguish the differences in the prices of medicines in the pharmaceutical market by using eight major features—the segmented buyers, the generic product groups, trade product names, procurement methods, dosage forms, pack sizes, manufacturers, and total purchase budgets—that provided the highest predictive accuracy. Our findings are useful to health policymakers who could employ our proposed model in monitoring the situation of medicine prices and providing feedback directly to suggest the best possible price for hospital purchasing managers based on the feature inputs in their procurement system.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

How Well Does a Sequential Minimal Optimization Model Perform in Predicting Medicine Prices for Procurement System?

Pentrakan

Yang

Wong

2021

IJERPH

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“…The SVM maximization problem is as: where λ is the Lagrange multiplier, x is the input data and y represent the class label. In SMO, two Lagrange multipliers are optimized while all the other multipliers are kept constant using this equation [ 39 ]: …”

Section: Methodsmentioning

confidence: 99%

Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides

2021

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Background Cancer is one of the major causes of death worldwide. To treat cancer, the use of anticancer peptides (ACPs) has attracted increased attention in recent years. ACPs are a unique group of small molecules that can target and kill cancer cells fast and directly. However, identifying ACPs by wet-lab experiments is time-consuming and labor-intensive. Therefore, it is significant to develop computational tools for ACPs prediction. Though some ACP prediction tools have been developed recently, their performances are not well enough and most of them do not offer a function to distinguish ACPs from antimicrobial peptides (AMPs). Considering the fact that a growing number of studies have shown that some AMPs exhibit anticancer function, this work tries to build a model for distinguishing AMPs from ACPs in addition to a model that predicts ACPs from whole peptides. Results This study chooses amino acid composition, N5C5, k-space, position-specific scoring matrix (PSSM) as features, and analyzes them by machine learning methods, including support vector machine (SVM) and sequential minimal optimization (SMO) to build a model (model 2) for distinguishing ACPs from whole peptides. Another model (model 1) that distinguishes ACPs from AMPs is also developed. Comparing to previous models, models developed in this research show better performance (accuracy: 85.5% for model 1 and 95.2% for model 2). Conclusions This work utilizes a new feature, PSSM, which contributes to better performance than other features. In addition to SVM, SMO is used in this research for optimizing SVM and the SMO-optimized models show better performance than non-optimized models. Last but not least, this work provides two different functions, including distinguishing ACPs from AMPs and distinguishing ACPs from all peptides. The second SMO-optimized model, which utilizes PSSM as a feature, performs better than all other existing tools.

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“…Sequential Minimal Optimization (SMO) ( Platt, 1998 ) is one of the most popular algorithms for training Support Vector Machines (SVMs). It was not used in the aforementioned studies, but was chosen for its wide applicability in pattern recognition and classification problems ( Naveed et al, 2019 ). Others used by other researchers in similar studies, such as Naive Bayes ( Guo et al, 2016 ) and K-Nearest Neighbors ( Guo et al, 2016 ; Löchtefeld et al, 2015 ; Seipp & Devlin, 2015 ) were also included.…”

Section: Algorithm Evaluationmentioning

confidence: 99%

Implicit detection of user handedness in touchscreen devices through interaction analysis

Fernández

González-Rodríguez

Fernandez-Lanvin

et al. 2021

PeerJ Computer Science

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Mobile devices now rival desktop computers as the most popular devices for web surfing and E-commerce. As screen sizes of mobile devices continue to get larger, operating smartphones with a single-hand becomes increasingly difficult. Automatic operating hand detection would enable E-commerce applications to adapt their interfaces to better suit their user’s handedness interaction requirements. This paper addresses the problem of identifying the operative hand by avoiding the use of mobile sensors that may pose a problem in terms of battery consumption or distortion due to different calibrations, improving the accuracy of user categorization through an evaluation of different classification strategies. A supervised classifier based on machine learning was constructed to label the operating hand as left or right. The classifier uses features extracted from touch traces such as scrolls and button clicks on a data-set of 174 users. The approach proposed by this paper is not platform-specific and does not rely on access to gyroscopes or accelerometers, widening its applicability to any device with a touchscreen.

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Human activity recognition using mixture of heterogeneous features and sequential minimal optimization

Cited by 31 publications

References 32 publications

How Well Does a Sequential Minimal Optimization Model Perform in Predicting Medicine Prices for Procurement System?

How Well Does a Sequential Minimal Optimization Model Perform in Predicting Medicine Prices for Procurement System?

Incorporating support vector machine with sequential minimal optimization to identify anticancer peptides

Implicit detection of user handedness in touchscreen devices through interaction analysis

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