Browsing by Author "Rahman, Sam Matiur"

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Development of fuzzy inference system for automatic tea making
(IEEE Xplore, 2017-03-27) Ahamed, Nizam Uddin; Taha, Zahari Bin; Khairuddin, Ismail B. Mohd; Rabbi, Mohammad Fazle; Sikandar, Tasriva; Palaniappan, Rajkumar; Ali, Md. Asraf; Rahman, Sam Matiur; Sundaraj, K.
In this paper, a fuzzy inference system has been developed for automatic tea making process. The system takes five inputs and gives two output which determines the grade of black tea and milk tea. Specifically, the proposed system considers five important characteristics of hot tea beverage such as water temperature, sugar, milk, brewing time and tea leaves quantity for grading the standard of the drink according to the consumer's requirement. Both black tea and milk tea can be rated with a grade based on the human expert judgment which is according to the taste and aroma of the tea. This automatic tea making system can let the users choose their preferred type of tea without figuring out the complicated process to making a cup of hot tea beverage. Full Text Link: http://doi.org/10.1109/I2CACIS.2016.7885314
EMG-based Classification of Forearm Muscles in Prehension Movements
(Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, Springer, 2020-07-30) Rahman, Sam Matiur; Altwijri, Omar; Ali, Md. Asraf; Alqahtani, Mahdi
This paper aimed to classify two forearm muscles known as Flexor Carpi Ulnaris (FCU) and Extensor Carpi Radialis Longus (ECRL) using surface Electromyography (sEMG) signal during different hand prehension tasks, such as cylindrical, tip, spherical, palmar, lateral and hook while grasping any object. Thirteen Machine Learning (ML) algorithms were analyzed to compare their performance using a single EMG time domain feature called integrated EMG (IEMG). The tree-based methods have the top performance to classify the forearm muscles than other ML methods among all those 13 ML algorithms. Results showed that 4 out of 5 tree-based classifiers achieved more than 75% accuracies, where the random forest method showed maximum classification accuracy (85.07%). Additionally, these tree-based ML methods computed the variable importance in classification margin. The results showed that the lateral grasping was the most important moving variable for all those algorithms except AdaBoost where tipping was the most significant movement variable for this method. We hope, this ML- and EMG-based classification results presented in the paper may alleviate some of the problems in implementing advanced forearm prosthetics, rehabilitation devices and assistive biomedical robots.
Ensemble-based Machine Learning Algorithms for Classifying Breast Tissue Based on Electrical Impedance Spectroscopy
(Advances in Intelligent Systems and Computing, Springer, 2019-06-19) Rahman, Sam Matiur; Ali, Md Asraf; Altwijri, Omar; Alqahtani, Mahdi; Ahmed, Nasim; Ahamed, Nizam U.
The initial identification of breast cancer and the prediction of its category have become a requirement in cancer research because they can simplify the subsequent clinical management of patients. The application of artificial intelligence techniques (e.g., machine learning and deep learning) in medical science is becoming increasingly important for intelligently transforming all available information into valuable knowledge. Therefore, we aimed to classify six classes of freshly excised tissues from a set of electrical impedance measurement variables using five ensemble-based machine learning (ML) algorithms, namely, the random forest (RF), extremely randomized trees (ERT), decision tree (DT), gradient boosting tree (GBT) and AdaBoost (Adaptive Boosting) (ADB) algorithms, which can be subcategorized as bagging and boosting methods. In addition, the ranked order of the variables based on their importance differed across the ML algorithms. The results demonstrated that the three bagging ensemble ML algorithms, namely, RF ERT and DT, yielded better classification accuracies (78–86%) compared with the two boosting algorithms, GBT and ADB (60–75%). We hope that these our results would help improve the classification of breast tissue to allow the early prediction of cancer susceptibility.
Fuzzy logic controller design for intelligent drilling system
(IEEE Xplore, 2017-03-27) Ahamed, Nizam Uddin; Yusof, Zulkifli; Hamedon, Zamzury; Rabbi, Mohammad Fazle; Sikandar, Tasriva; Palaniappan, Rajkumar; Ali, Md. Asraf; Rahman, Sam Matiur; Sundaraj, K.
An intelligent drilling system can be commercially very profitable in terms of reduction in crude material and labor involvement. The use of fuzzy logic based controller in the intelligent cutting and drilling operations has become a popular practice in the ever growing manufacturing industry. In this paper, a fuzzy logic controller has been designed to select the cutting parameter more precisely for the drilling operation. Specifically, different input criterion of machining parameters are considered such as the tool and material hardness, the diameter of drilling hole and the flow rate of cutting fluid. Unlike the existing fuzzy logic based methods, which use only two input parameters, the proposed system utilizes more input parameters to provide spindle speed and feed rate information more precisely for the intelligent drilling operation. Full Text Link: http://doi.org/10.1109/I2CACIS.2016.7885316
Significance of Electromyography in the Assessment of Diabetic Neuropathy
(Journal of Mechanics in Medicine and Biology, World Scientific, 2019) Rabbi, Mohammad Fazle; Ghazali, Kamarul Hawari; Altwijri, Omar; Alqahtani, Mahdi; Rahman, Sam Matiur; Ali, Md. Asraf; Sundaraj, Kenneth; Taha, Zahari; Ahamed, Nizam Uddin
Diabetic neuropathy is one of the physical complications of diabetes mellitus (DM) patients with a long history of diabetes. An electromyography (EMG)-based assessment may be very useful for the management of diabetic neuropathy. In the present study, we aimed to summarize all of the findings and recommendations obtained from previous studies that investigated the application of EMG to the assessment of diabetic neuropathy. An extensive search of the prominent electronic databases PubMed, Google Scholar and Scopus was performed to evaluate the following areas: (i) what are the muscles to be evaluated by EMG for neuropathy assessment, (ii) what type of EMG methodologies have been used and (iii) what recommendation can be made for neuropathy detection. The major findings are summarized as follows: (i) very few studies have analyzed the correlation of the EMG signals acquired from peripheral muscles affected in neuropathy with those obtained with non-neuropathic complications, such as ankle sprain; (ii) EMG has been applied for the detection of diabetic neuropathy more than diabetes treatment; and (iii) neuropathy detection using an EMG-based assessment were mainly performed for type 2 DM patients aged at least 50 years.