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Currently focusing on application of Artificial Intelligence & Blockchain in the different sectors.
In the digital era, almost every system is connected to a digital platform to enhance efficiency. Although life is thus improved, security issues remain important, especially in the healthcare sector. The privacy and security of healthcare records is paramount; data leakage is socially unacceptable. Therefore, technology that protects data but does not compromise efficiency is essential. Blockchain technology has gained increasing attention as it ensures transparency, trust, privacy, and security. However, the critical factors affecting efficiency require further study. Here, we define the critical factors that affect blockchain implementation in the healthcare industry. We extracted such factors from the literature and from experts, then used interpretive structural modeling to define the interrelationships among these factors and classify them according to driving and dependence forces. This identified key drivers of the desired objectives. Regulatory clarity and governance (F2), immature technology (F3), high investment cost (F6), blockchain developers (F9), and trust among stakeholders (F12) are key factors to consider when seeking to implement blockchain technology in healthcare. Our analysis will allow managers to understand the requirements for successful implementation.
Satyabrata Aich; Sushanta Tripathy; Moon-Il Joo; Hee-Cheol Kim. Critical Dimensions of Blockchain Technology Implementation in the Healthcare Industry: An Integrated Systems Management Approach. Sustainability 2021, 13, 5269 .
AMA StyleSatyabrata Aich, Sushanta Tripathy, Moon-Il Joo, Hee-Cheol Kim. Critical Dimensions of Blockchain Technology Implementation in the Healthcare Industry: An Integrated Systems Management Approach. Sustainability. 2021; 13 (9):5269.
Chicago/Turabian StyleSatyabrata Aich; Sushanta Tripathy; Moon-Il Joo; Hee-Cheol Kim. 2021. "Critical Dimensions of Blockchain Technology Implementation in the Healthcare Industry: An Integrated Systems Management Approach." Sustainability 13, no. 9: 5269.
Preventing exacerbation and seeking to determine the severity of the disease during the hospitalization of chronic obstructive pulmonary disease (COPD) patients is a crucial global initiative for chronic obstructive lung disease (GOLD); this option is available only for stable-phase patients. Recently, the assessment and prediction techniques that are used have been determined to be inadequate for acute exacerbation of chronic obstructive pulmonary disease patients. To magnify the monitoring and treatment of acute exacerbation COPD patients, we need to rely on the AI system, because traditional methods take a long time for the prognosis of the disease. Machine-learning techniques have shown the capacity to be effectively used in crucial healthcare applications. In this paper, we propose a voting ensemble classifier with 24 features to identify the severity of chronic obstructive pulmonary disease patients. In our study, we applied five machine-learning classifiers, namely random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), XGboost (XGB), and K-nearest neighbor (KNN). These classifiers were trained with a set of 24 features. After that, we combined their results with a soft voting ensemble (SVE) method. Consequently, we found performance measures with an accuracy of 91.0849%, a precision of 90.7725%, a recall of 91.3607%, an F-measure of 91.0656%, and an AUC score of 96.8656%, respectively. Our result shows that the SVE classifier with the proposed twenty-four features outperformed regular machine-learning-based methods for chronic obstructive pulmonary disease (COPD) patients. The SVE classifier helps respiratory physicians to estimate the severity of COPD patients in the early stage, consequently guiding the cure strategy and helps the prognosis of COPD patients.
Ali Hussain; Hee-Eun Choi; Hyo-Jung Kim; Satyabrata Aich; Muhammad Saqlain; Hee-Cheol Kim. Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques. Diagnostics 2021, 11, 829 .
AMA StyleAli Hussain, Hee-Eun Choi, Hyo-Jung Kim, Satyabrata Aich, Muhammad Saqlain, Hee-Cheol Kim. Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques. Diagnostics. 2021; 11 (5):829.
Chicago/Turabian StyleAli Hussain; Hee-Eun Choi; Hyo-Jung Kim; Satyabrata Aich; Muhammad Saqlain; Hee-Cheol Kim. 2021. "Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques." Diagnostics 11, no. 5: 829.
Parkinson’s Disease is a neurodegenerative disease that affects the aging population and is caused by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). With the onset of the disease, the patients suffer from mobility disorders such as tremors, bradykinesia, impairment of posture and balance, etc., and it progressively worsens in the due course of time. Additionally, as there is an exponential growth of the aging population in the world the number of people suffering from Parkinson’s Disease is increasing and it levies a huge economic burden on governments. However, until now no therapeutic method has been discovered for completely eradicating the disease from a person’s body after it’s onset. Therefore, the early detection of Parkinson’s Disease is of paramount importance to tackle the progressive loss of dopaminergic neurons in patients to serve them with a better life. In this study, 3T T1-weighted MRI scans were acquired from the Parkinson’s Progression Markers Initiative (PPMI) database of 406 subjects from baseline visit, where 203 were healthy and 203 were suffering from Parkinson’s Disease. Following data pre-processing, a 3D convolutional neural network (CNN) architecture was developed for learning the intricate patterns in the Magnetic Resonance Imaging (MRI) scans for the detection of Parkinson’s Disease. In the end, it was observed that the developed 3D CNN model performed superiorly by completely aligning with the hypothesis of the study and plotted an overall accuracy of 95.29%, average recall of 0.943, average precision of 0.927, average specificity of 0.9430, f1-score of 0.936, and Receiver Operating Characteristic—Area Under Curve (ROC-AUC) score of 0.98 for both the classes respectively.
Sabyasachi Chakraborty; Satyabrata Aich; Hee-Cheol Kim. Detection of Parkinson’s Disease from 3T T1 Weighted MRI Scans Using 3D Convolutional Neural Network. Diagnostics 2020, 10, 402 .
AMA StyleSabyasachi Chakraborty, Satyabrata Aich, Hee-Cheol Kim. Detection of Parkinson’s Disease from 3T T1 Weighted MRI Scans Using 3D Convolutional Neural Network. Diagnostics. 2020; 10 (6):402.
Chicago/Turabian StyleSabyasachi Chakraborty; Satyabrata Aich; Hee-Cheol Kim. 2020. "Detection of Parkinson’s Disease from 3T T1 Weighted MRI Scans Using 3D Convolutional Neural Network." Diagnostics 10, no. 6: 402.
In the last few years, the importance of measuring gait characteristics has increased tenfold due to their direct relationship with various neurological diseases. As patients suffering from Parkinson's disease (PD) are more prone to a movement disorder, the quantification of gait characteristics helps in personalizing the treatment. The wearable sensors make the measurement process more convenient as well as feasible in a practical environment. However, the question remains to be answered about the validation of the wearable sensor-based measurement system in a real-world scenario. This paper proposes a study that includes an algorithmic approach based on collected data from the wearable accelerometers for the estimation of the gait characteristics and its validation using the Tinetti mobility test and 3D motion capture system. It also proposes a machine learning-based approach to classify the PD patients from the healthy older group (HOG) based on the estimated gait characteristics. The results show a good correlation between the proposed approach, the Tinetti mobility test, and the 3D motion capture system. It was found that decision tree classifiers outperformed other classifiers with a classification accuracy of 88.46%. The obtained results showed enough evidence about the proposed approach that could be suitable for assessing PD in a home-based free-living real-time environment.
Satyabrata Aich; Pyari Mohan Pradhan; Sabyasachi Chakraborty; Hee-Cheol Kim; Hee-Tae Kim; Hae-Gu Lee; Il Hwan Kim; Moon-Il Joo; Sim Jong Seong; Jinse Park. Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson's Patients. Journal of Healthcare Engineering 2020, 2020, 1823268 -11.
AMA StyleSatyabrata Aich, Pyari Mohan Pradhan, Sabyasachi Chakraborty, Hee-Cheol Kim, Hee-Tae Kim, Hae-Gu Lee, Il Hwan Kim, Moon-Il Joo, Sim Jong Seong, Jinse Park. Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson's Patients. Journal of Healthcare Engineering. 2020; 2020 ():1823268-11.
Chicago/Turabian StyleSatyabrata Aich; Pyari Mohan Pradhan; Sabyasachi Chakraborty; Hee-Cheol Kim; Hee-Tae Kim; Hae-Gu Lee; Il Hwan Kim; Moon-Il Joo; Sim Jong Seong; Jinse Park. 2020. "Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson's Patients." Journal of Healthcare Engineering 2020, no. : 1823268-11.
Parkinson’s disease is caused due to the progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). Presently, with the exponential growth of the aging population across the world the number of people being affected by the disease is also increasing and it imposes a huge economic burden on the governments. However, to date, no therapy or treatment has been found that can completely eradicate the disease. Therefore, early detection of Parkinson’s disease is very important so that the progressive loss of dopaminergic neurons can be controlled to provide the patients with a better life. In this study, 3T T1-MRI scans were collected from 906 subjects, out of which, 203 are control subjects, 66 are prodromal subjects and 637 are Parkinson’s disease patients. To analyze the MRI scans for the detection of neurodegeneration and Parkinson’s disease, eight subcortical structures were segmented from the acquired MRI scans using atlas based segmentation. Further, on the extracted eight subcortical structures, feature extraction was performed to extract textural, morphological and statistical features, respectively. After the feature extraction process, an exhaustive set of 107 features were generated for each MRI scan. Therefore, a two-level feature extraction process was implemented for finding the best possible feature set for the detection of Parkinson’s disease. The two-level feature extraction procedure leveraged correlation analysis and recursive feature elimination, which at the end provided us with 20 best performing features out of the extracted 107 features. Further, all the features were trained using machine learning algorithms and a comparative analysis was performed between four different machine learning algorithms based on the selected performance metrics. And at the end, it was observed that artificial neural network (multi-layer perceptron) performed the best by providing an overall accuracy of 95.3%, overall recall of 95.41%, overall precision of 97.28% and f1-score of 94%, respectively.
Sabyasachi Chakraborty; Satyabrata Aich; Hee-Cheol Kim. 3D Textural, Morphological and Statistical Analysis of Voxel of Interests in 3T MRI Scans for the Detection of Parkinson’s Disease Using Artificial Neural Networks. Healthcare 2020, 8, 34 .
AMA StyleSabyasachi Chakraborty, Satyabrata Aich, Hee-Cheol Kim. 3D Textural, Morphological and Statistical Analysis of Voxel of Interests in 3T MRI Scans for the Detection of Parkinson’s Disease Using Artificial Neural Networks. Healthcare. 2020; 8 (1):34.
Chicago/Turabian StyleSabyasachi Chakraborty; Satyabrata Aich; Hee-Cheol Kim. 2020. "3D Textural, Morphological and Statistical Analysis of Voxel of Interests in 3T MRI Scans for the Detection of Parkinson’s Disease Using Artificial Neural Networks." Healthcare 8, no. 1: 34.
Anchored in the resource‐based view theory, the objective of this research is to empirically analyse the behavioural factors affecting the green supply chain management (GCSM) performance in a fast‐growing emerging economy by taking an empirical data set of 101 responses from personnel in the mining sector. Behavioural factors in green supply chains are still a critical challenge—not yet a well‐explored academic subject—when the focus is on the mining industry of emerging economies like India; the lack of studies in this field could be a factor preventing the Indian mining industry becoming more green. In terms of methodology, original survey data were processed through AMOS 4.0, adopted for assessing the causal connection among the six constructs, that is, top management support, teamwork, workplace culture, resistance to change, green innovation, and green motivation. We further explore the input from the human side of GCSM by highlighting that top management support and green motivation are the most crucial behavioural factors that influence GCSM in the Indian mining sector. The study will be helpful for mining companies because it will enable them to identify the areas that require their attention for enhancing GCSM performance related to behavioural aspects.
Kamala Kanta Muduli; Sunil Luthra; Sachin Kumar Mangla; Charbel Jose Chiappetta Jabbour; Satyabrata Aich; Julio Cesar Ferro De Guimarães. Environmental management and the “soft side” of organisations: Discovering the most relevant behavioural factors in green supply chains. Business Strategy and the Environment 2020, 29, 1647 -1665.
AMA StyleKamala Kanta Muduli, Sunil Luthra, Sachin Kumar Mangla, Charbel Jose Chiappetta Jabbour, Satyabrata Aich, Julio Cesar Ferro De Guimarães. Environmental management and the “soft side” of organisations: Discovering the most relevant behavioural factors in green supply chains. Business Strategy and the Environment. 2020; 29 (4):1647-1665.
Chicago/Turabian StyleKamala Kanta Muduli; Sunil Luthra; Sachin Kumar Mangla; Charbel Jose Chiappetta Jabbour; Satyabrata Aich; Julio Cesar Ferro De Guimarães. 2020. "Environmental management and the “soft side” of organisations: Discovering the most relevant behavioural factors in green supply chains." Business Strategy and the Environment 29, no. 4: 1647-1665.
The safety and welfare of companion animals such as dogs has become a large challenge in the last few years. To assess the well-being of a dog, it is very important for human beings to understand the activity pattern of the dog, and its emotional behavior. A wearable, sensor-based system is suitable for such ends, as it will be able to monitor the dogs in real-time. However, the question remains unanswered as to what kind of data should be used to detect the activity patterns and emotional patterns, as does another: what should be the location of the sensors for the collection of data and how should we automate the system? Yet these questions remain unanswered, because to date, there is no such system that can address the above-mentioned concerns. The main purpose of this study was (1) to develop a system that can detect the activities and emotions based on the accelerometer and gyroscope signals and (2) to automate the system with robust machine learning techniques for implementing it for real-time situations. Therefore, we propose a system which is based on the data collected from 10 dogs, including nine breeds of various sizes and ages, and both genders. We used machine learning classification techniques for automating the detection and evaluation process. The ground truth fetched for the evaluation process was carried out by taking video recording data in frame per second and the wearable sensors data were collected in parallel with the video recordings. Evaluation of the system was performed using an ANN (artificial neural network), random forest, SVM (support vector machine), KNN (k nearest neighbors), and a naïve Bayes classifier. The robustness of our system was evaluated by taking independent training and validation sets. We achieved an accuracy of 96.58% while detecting the activity and 92.87% while detecting emotional behavior, respectively. This system will help the owners of dogs to track their behavior and emotions in real-life situations for various breeds in different scenarios.
Satyabrata Aich; Sabyasachi Chakraborty; Jong-Seong Sim; Dong-Jin Jang; Hee-Cheol Kim. The Design of an Automated System for the Analysis of the Activity and Emotional Patterns of Dogs with Wearable Sensors Using Machine Learning. Applied Sciences 2019, 9, 4938 .
AMA StyleSatyabrata Aich, Sabyasachi Chakraborty, Jong-Seong Sim, Dong-Jin Jang, Hee-Cheol Kim. The Design of an Automated System for the Analysis of the Activity and Emotional Patterns of Dogs with Wearable Sensors Using Machine Learning. Applied Sciences. 2019; 9 (22):4938.
Chicago/Turabian StyleSatyabrata Aich; Sabyasachi Chakraborty; Jong-Seong Sim; Dong-Jin Jang; Hee-Cheol Kim. 2019. "The Design of an Automated System for the Analysis of the Activity and Emotional Patterns of Dogs with Wearable Sensors Using Machine Learning." Applied Sciences 9, no. 22: 4938.
Detection of the state of mind has increasingly grown into a much favored study in recent years. After the advent of smart wearables in the market, each individual now expects to be delivered with state-of-the-art reports about his body. The most dominant wearables in the market often focus on general metrics such as the number of steps, distance walked, heart rate, oximetry, sleep quality, and sleep stage. But, for accurately identifying the well-being of an individual, another important metric needs to be analyzed, which is the state of mind. The state of mind is a metric of an individual that boils down to the activity of all other related metrics. But, the detection of the state of mind has formed a huge challenge for the researchers as a single biosignal cannot propose a particular decision threshold for detection. Therefore, in this work, multiple biosignals from different parts of the body are used to determine the state of mind of an individual. The biosignals, blood volume pulse (BVP), and accelerometer are intercepted from a wrist-worn wearable, and electrocardiography (ECG), electromyography (EMG), and respiration are intercepted from a chest-worn pod. For the classification of the biosignals to the multiple state-of-mind categories, a multichannel convolutional neural network architecture was developed. The overall model performed pretty well and pursued some encouraging results by demonstrating an average recall and precision of 97.238% and 97.652% across all the classes, respectively.
Sabyasachi Chakraborty; Satyabrata Aich; Moon-Il Joo; Mangal Sain; Hee-Cheol Kim. A Multichannel Convolutional Neural Network Architecture for the Detection of the State of Mind Using Physiological Signals from Wearable Devices. Journal of Healthcare Engineering 2019, 2019, 1 -17.
AMA StyleSabyasachi Chakraborty, Satyabrata Aich, Moon-Il Joo, Mangal Sain, Hee-Cheol Kim. A Multichannel Convolutional Neural Network Architecture for the Detection of the State of Mind Using Physiological Signals from Wearable Devices. Journal of Healthcare Engineering. 2019; 2019 ():1-17.
Chicago/Turabian StyleSabyasachi Chakraborty; Satyabrata Aich; Moon-Il Joo; Mangal Sain; Hee-Cheol Kim. 2019. "A Multichannel Convolutional Neural Network Architecture for the Detection of the State of Mind Using Physiological Signals from Wearable Devices." Journal of Healthcare Engineering 2019, no. : 1-17.
One of the most common symptoms observed among most of the Parkinson’s disease patients that affects movement pattern and is also related to the risk of fall, is usually termed as “freezing of gait (FoG)”. To allow systematic assessment of FoG, objective quantification of gait parameters and automatic detection of FoG are needed. This will help in personalizing the treatment. In this paper, the objectives of the study are (1) quantification of gait parameters in an objective manner by using the data collected from wearable accelerometers; (2) comparison of five estimated gait parameters from the proposed algorithm with their counterparts obtained from the 3D motion capture system in terms of mean error rate and Pearson’s correlation coefficient (PCC); (3) automatic discrimination of FoG patients from no FoG patients using machine learning techniques. It was found that the five gait parameters have a high level of agreement with PCC ranging from 0.961 to 0.984. The mean error rate between the estimated gait parameters from accelerometer-based approach and 3D motion capture system was found to be less than 10%. The performances of the classifiers are compared on the basis of accuracy. The best result was accomplished with the SVM classifier with an accuracy of approximately 88%. The proposed approach shows enough evidence that makes it applicable in a real-life scenario where the wearable accelerometer-based system would be recommended to assess and monitor the FoG.
Satyabrata Aich; Pyari Mohan Pradhan; Jinse Park; Nitin Sethi; Vemula Sai Sri Vathsa; Hee-Cheol Kim. A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer. Sensors 2018, 18, 3287 .
AMA StyleSatyabrata Aich, Pyari Mohan Pradhan, Jinse Park, Nitin Sethi, Vemula Sai Sri Vathsa, Hee-Cheol Kim. A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer. Sensors. 2018; 18 (10):3287.
Chicago/Turabian StyleSatyabrata Aich; Pyari Mohan Pradhan; Jinse Park; Nitin Sethi; Vemula Sai Sri Vathsa; Hee-Cheol Kim. 2018. "A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer." Sensors 18, no. 10: 3287.
In the past few years, machine-learning techniques have garnered much attention across disciplines. Most of these techniques are capable of producing highly accurate results that compel a majority of scientists to implement the approach in cases of predictive analytics. Few works related to wine data have been undertaken using different classifiers, and thus far, no studies have compared the performance metrics of the different classifiers with different feature sets for the prediction of quality among types of wine. In this chapter, an intelligent approach is proposed by considering a recursive feature elimination (RFE) algorithm for feature selection, as well as nonlinear and probabilistic classifiers. Performance metrics including accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) are compared by implementing different classifiers with original feature sets (OFS) as well as reduced feature sets (RFS). The results show accuracy ranging from 97.61 to 99.69% among the different feature sets. This analysis will aid wine experts in differentiating various wines according to their features.
Satyabrata Aich; Mangal Sain; Jin-Han Yoon. Prediction of Different Types of Wine Using Nonlinear and Probabilistic Classifiers. Econometrics for Financial Applications 2018, 11 -19.
AMA StyleSatyabrata Aich, Mangal Sain, Jin-Han Yoon. Prediction of Different Types of Wine Using Nonlinear and Probabilistic Classifiers. Econometrics for Financial Applications. 2018; ():11-19.
Chicago/Turabian StyleSatyabrata Aich; Mangal Sain; Jin-Han Yoon. 2018. "Prediction of Different Types of Wine Using Nonlinear and Probabilistic Classifiers." Econometrics for Financial Applications , no. : 11-19.
In recent years, most small and medium scale enterprises (SMEs) worldwide looking for improvement in their business practices in order to gain competitive advantage and total quality management(TQM) as a means by which SMEs could achieve the desired result. The objective of this study is to discover the critical success factors that are affecting the quality management practices in SMEs. In this work eight factors were identified through the literature review and experts from academic as well as industries. The factors are commitment to quality, employee involvement, customer focus, information technology, improved production planning and control, recognition system, supplier quality management, and management vision and mission. Interpretive structural modeling(ISM) is used to understand the complex relationships among the factors and classify the factors into various categories as per the driving and the dependence capacity. The result shows that information technology (IT) is a key success factor for implementing TQM in SMEs. It is observed that SMEs have to increase the use of IT to improve the quality of the product and productivity.
Satyabrata Aich; KamalaKanta Muduli; Mehedi Hassan Onik; Hee Cheol Kim. A novel approach to identify the best practices of quality management in SMES based on critical success factors using interpretive structural modeling (ISM). International Journal of Engineering & Technology 2018, 7, 130 -133.
AMA StyleSatyabrata Aich, KamalaKanta Muduli, Mehedi Hassan Onik, Hee Cheol Kim. A novel approach to identify the best practices of quality management in SMES based on critical success factors using interpretive structural modeling (ISM). International Journal of Engineering & Technology. 2018; 7 (3.29):130-133.
Chicago/Turabian StyleSatyabrata Aich; KamalaKanta Muduli; Mehedi Hassan Onik; Hee Cheol Kim. 2018. "A novel approach to identify the best practices of quality management in SMES based on critical success factors using interpretive structural modeling (ISM)." International Journal of Engineering & Technology 7, no. 3.29: 130-133.
In recent year’s neurodegenerative diseases such as Amyotrophic Lateral Sclerosis (ALS), Huntington’s disease (HD), and Parkinson’s disease (PD) has gain lot of attention because the money spend on the healthcare services generated by these diseases has created a huge burden in the economy of the developed countries. These diseases are progressive in nature and it produces some dead cells in the brain and spinal cords that directly affect the gait, speech and memory loss. As the old age population is increasing at a higher rate, it is necessary to develop some intelligent technique to detect these diseases at the early stage to reduce the economic burden. With the advent of machine learning techniques, classification based on gait signals has become popular these days. Few past research have been made to classify the neurodegenerative diseases by using binary classification approach with linear feature selection algorithm and linear classifiers. In this paper quad classification approach was used by considering all groups (ALS, HD, PD and Control) with Recursive Feature Elimination (RFE) algorithm for feature selection and using supervised machine learning techniques such as linear, nonlinear classifiers with decision tree and probabilistic classifiers. Finally the performance measures of each classifiers has been studied with 5 selected features obtained from RFE method and was found that the nonlinear classifier such as Random Forest and Bagging CART have given best performance with an accuracy of 96.93% and 97.43% respectively. This analysis helps the clinicians to distinguish neurodegenerative disease from the healthy group by using gait signals.
Satyabrata Aich; Ki-Won Choi; Pyari Mohan Pradhan; Jinse Park; Hee-Cheol Kim. Prediction of Neurodegenerative Diseases Based on Gait Signals Using Supervised Machine Learning Techniques. Advanced Science Letters 2018, 24, 1974 -1978.
AMA StyleSatyabrata Aich, Ki-Won Choi, Pyari Mohan Pradhan, Jinse Park, Hee-Cheol Kim. Prediction of Neurodegenerative Diseases Based on Gait Signals Using Supervised Machine Learning Techniques. Advanced Science Letters. 2018; 24 (3):1974-1978.
Chicago/Turabian StyleSatyabrata Aich; Ki-Won Choi; Pyari Mohan Pradhan; Jinse Park; Hee-Cheol Kim. 2018. "Prediction of Neurodegenerative Diseases Based on Gait Signals Using Supervised Machine Learning Techniques." Advanced Science Letters 24, no. 3: 1974-1978.
The pastresearch and the ongoing research on stock market suggested that the prediction of future stock return is the one of the most critical topic in finance and economics. The unpredictable nature of stock price makes the related research more interesting. In the recent years with the increasing rate of fluctuation of the stock market prices, lot of researches are focusing on the forecasting of future stock prices before doing any investment or trading in the market. According to the well-known economist it has been mentioned that the economy of a country directly or indirectly depend on the stock prices. Although there are different statistical model available for predicting the time series data, the Autoregressive integrated moving average (ARIMA) model is widely used because it is an efficient and robust model for forecasting the time series data. The conventional time series analysis has lot of drawbacks and it takes long time. However with the advent of artificial intelligent technique it is easier to work with the different model with huge amount of data in short time. Past research has been done on forecasting of future returns with ARIMA model by using historical data, but so far no work has been done on comparing the accuracies of the ARIMA model while predicting the future stock prices by using the historical stock data. In this paper a study has been done by using long term historical data and comparing the accuracies of ARIMA model with 2, 4, 6, 8 and 10 year historical data to give a clear idea about the analyst that how long past data is required for better prediction of forecasting the stock returns.
Satyabrata Aich; Hak-Chun Lee; Hee-Cheol Kim. Forecasting the Future Stock Returns Using Data Mining Approach Based on the Historical Data. Advanced Science Letters 2018, 24, 2046 -2049.
AMA StyleSatyabrata Aich, Hak-Chun Lee, Hee-Cheol Kim. Forecasting the Future Stock Returns Using Data Mining Approach Based on the Historical Data. Advanced Science Letters. 2018; 24 (3):2046-2049.
Chicago/Turabian StyleSatyabrata Aich; Hak-Chun Lee; Hee-Cheol Kim. 2018. "Forecasting the Future Stock Returns Using Data Mining Approach Based on the Historical Data." Advanced Science Letters 24, no. 3: 2046-2049.
In the recent years the most common neurological disorder that affect the people over 65 years are Parkinson’s disease (PD) and Alzheimer disease (AD). As the diseases progresses it produces different abnormalities in the spinal cords and brain cells that direct affect the gait. Although PD and AD are clinically different in many respect but the abnormalities in gait is the most common symptom in these kind of diseases. In recent years the gait analysis caught much attention while assessing the progression of PD and AD. With the invention of wearable device, the quantification of gait parameters become lot easier and also with the availability of powerful machine learning technique inventing pattern using gait parameter is getting popular. In this paper an attempt has been made to discriminate PD group from the Alzheimer group by using different classifier as well as different feature sets. Finally a performance comparison using different performance measure metrics has been made between the original set of features and reduced features. Our results found significance difference in the performance metrics of the two feature sets by achieving accuracy of 92.59% with the original sets compared with the reduced feature sets, which could achieve accuracy of 81.48%. This analysis helps the medical practitioner to distinguish PD from AD by using spatiotemporal gait signal.
Satyabrata Aich; Ki-Won Choi; Pyari Mohan Pradhan; Jinse Park; Hee-Cheol Kim. A Performance Comparison Based on Machine Learning Approaches to Distinguish Parkinson’s Disease from Alzheimer Disease Using Spatiotemporal Gait signals. Advanced Science Letters 2018, 24, 2058 -2062.
AMA StyleSatyabrata Aich, Ki-Won Choi, Pyari Mohan Pradhan, Jinse Park, Hee-Cheol Kim. A Performance Comparison Based on Machine Learning Approaches to Distinguish Parkinson’s Disease from Alzheimer Disease Using Spatiotemporal Gait signals. Advanced Science Letters. 2018; 24 (3):2058-2062.
Chicago/Turabian StyleSatyabrata Aich; Ki-Won Choi; Pyari Mohan Pradhan; Jinse Park; Hee-Cheol Kim. 2018. "A Performance Comparison Based on Machine Learning Approaches to Distinguish Parkinson’s Disease from Alzheimer Disease Using Spatiotemporal Gait signals." Advanced Science Letters 24, no. 3: 2058-2062.
Numerous texts in the Social Networking Service (SNS) contain the author’s feelings and can be analyzed through the SNS crawling and opinion mining. We can analyze individual’s emotions through personal text analysis, collect personal texts by region, and analyze regional emotions through regional text analysis. The text collected by the region can be analyzed for the emotions of the region that could not be obtained from the personal text, and for the reason why the region has such an emotions. In this paper, we analyze emotions contained in texts and designed a regional-based emotion analysis system which provides related information based on analysis. The system collects text data by region through Twitter crawling and stores and manages data using HBase in Hadoop. From the collected data, the stem is extracted through Hangul Stemming Algorithm, and the emotional dictionary for the emotional analysis is constructed by using the elastic net regression analysis. Based on the constructed emotion dictionary, emotions of texts in each region are analyzed, and the analyzed results are provided to users through web services. Through this research, it is expected that the emotion of the individual will be classified automatically, and it will be the basis for the creation of various emotion-based services that can generate and provide new information through the emotional analysis.
Kiwon Choi; Moon-Il Joo; Satyabrata Aich; Hee-Cheol Kim. Design of Regional-Based Emotion Analysis System Using Twitter Feeds. Advanced Science Letters 2018, 24, 2054 -2057.
AMA StyleKiwon Choi, Moon-Il Joo, Satyabrata Aich, Hee-Cheol Kim. Design of Regional-Based Emotion Analysis System Using Twitter Feeds. Advanced Science Letters. 2018; 24 (3):2054-2057.
Chicago/Turabian StyleKiwon Choi; Moon-Il Joo; Satyabrata Aich; Hee-Cheol Kim. 2018. "Design of Regional-Based Emotion Analysis System Using Twitter Feeds." Advanced Science Letters 24, no. 3: 2054-2057.
Satyabrata Aich; Ahmed Abdulhakim Al-Absi; Kueh Lee Hui; John Tark Lee; Mangal Sain. A classification approach with different feature sets to predict the quality of different types of wine using machine learning techniques. 2018 20th International Conference on Advanced Communication Technology (ICACT) 2018, 1 .
AMA StyleSatyabrata Aich, Ahmed Abdulhakim Al-Absi, Kueh Lee Hui, John Tark Lee, Mangal Sain. A classification approach with different feature sets to predict the quality of different types of wine using machine learning techniques. 2018 20th International Conference on Advanced Communication Technology (ICACT). 2018; ():1.
Chicago/Turabian StyleSatyabrata Aich; Ahmed Abdulhakim Al-Absi; Kueh Lee Hui; John Tark Lee; Mangal Sain. 2018. "A classification approach with different feature sets to predict the quality of different types of wine using machine learning techniques." 2018 20th International Conference on Advanced Communication Technology (ICACT) , no. : 1.
Satyabrata Aich; Kim Younga; Kueh Lee Hui; Ahmed Abdulhakim Al-Absi; Mangal Sain. A nonlinear decision tree based classification approach to predict the Parkinson's disease using different feature sets of voice data. 2018 20th International Conference on Advanced Communication Technology (ICACT) 2018, 1 .
AMA StyleSatyabrata Aich, Kim Younga, Kueh Lee Hui, Ahmed Abdulhakim Al-Absi, Mangal Sain. A nonlinear decision tree based classification approach to predict the Parkinson's disease using different feature sets of voice data. 2018 20th International Conference on Advanced Communication Technology (ICACT). 2018; ():1.
Chicago/Turabian StyleSatyabrata Aich; Kim Younga; Kueh Lee Hui; Ahmed Abdulhakim Al-Absi; Mangal Sain. 2018. "A nonlinear decision tree based classification approach to predict the Parkinson's disease using different feature sets of voice data." 2018 20th International Conference on Advanced Communication Technology (ICACT) , no. : 1.
Satyabrata Aich; Kim Younga; Kueh Lee Hui; Ahmed Abdulhakim Al-Absi; Mangal Sain. A nonlinear decision tree based classification approach to predict the Parkinson's disease using different feature sets of voice data. 2018 20th International Conference on Advanced Communication Technology (ICACT) 2018, 1 .
AMA StyleSatyabrata Aich, Kim Younga, Kueh Lee Hui, Ahmed Abdulhakim Al-Absi, Mangal Sain. A nonlinear decision tree based classification approach to predict the Parkinson's disease using different feature sets of voice data. 2018 20th International Conference on Advanced Communication Technology (ICACT). 2018; ():1.
Chicago/Turabian StyleSatyabrata Aich; Kim Younga; Kueh Lee Hui; Ahmed Abdulhakim Al-Absi; Mangal Sain. 2018. "A nonlinear decision tree based classification approach to predict the Parkinson's disease using different feature sets of voice data." 2018 20th International Conference on Advanced Communication Technology (ICACT) , no. : 1.
Satyabrata Aich; Ahmed Abdulhakim Al-Absi; Kueh Lee Hui; John Tark Lee; Mangal Sain. A classification approach with different feature sets to predict the quality of different types of wine using machine learning techniques. 2018 20th International Conference on Advanced Communication Technology (ICACT) 2018, 1 .
AMA StyleSatyabrata Aich, Ahmed Abdulhakim Al-Absi, Kueh Lee Hui, John Tark Lee, Mangal Sain. A classification approach with different feature sets to predict the quality of different types of wine using machine learning techniques. 2018 20th International Conference on Advanced Communication Technology (ICACT). 2018; ():1.
Chicago/Turabian StyleSatyabrata Aich; Ahmed Abdulhakim Al-Absi; Kueh Lee Hui; John Tark Lee; Mangal Sain. 2018. "A classification approach with different feature sets to predict the quality of different types of wine using machine learning techniques." 2018 20th International Conference on Advanced Communication Technology (ICACT) , no. : 1.
The power of the public opinions especially in the social media such as facebook, or twitter has risen to such an extent to give a significant impact on the economy of a country. For the last few years the sentiment analysis became a powerful tool for analyzing the public opinions especially the tweets because it is based on the real time. Since the democracy and public opinions has a big role to play in the growth of a country, the sentiments of public towards the political risks as well as political change become more important for the economical growth of a country. In this paper we have done an analysis based on the twitter sentiments of the people towards the recent political change happened in South Korea. We have collected several thousands of tweets and done the analysis based on the sentiment score of tweets. We have found a significant correlation between the sentiment score and financial index, which clearly interpret the impact on the economy of the country.
Satyabrata Aich; Ki-Won Choi; Hee-Cheol Kim. An Approach to Investigate the Impact of Political Change on the Economy of South Korea Using Twitter Sentiment Analysis. Advanced Science Letters 2017, 23, 10172 -10176.
AMA StyleSatyabrata Aich, Ki-Won Choi, Hee-Cheol Kim. An Approach to Investigate the Impact of Political Change on the Economy of South Korea Using Twitter Sentiment Analysis. Advanced Science Letters. 2017; 23 (10):10172-10176.
Chicago/Turabian StyleSatyabrata Aich; Ki-Won Choi; Hee-Cheol Kim. 2017. "An Approach to Investigate the Impact of Political Change on the Economy of South Korea Using Twitter Sentiment Analysis." Advanced Science Letters 23, no. 10: 10172-10176.