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6. Low-Cost Internet of Things Platform for Epilepsy Monitoring Using Real-Time Electroencephalogram

Author

Manoj Kumar Sharma, M. Shamim Kaiser, and Kanad Ray

Subjects
Software
Abstract

This work is focusing to develop a portable, low-cost remote diagnostic system for developing countries where the current state of health is not in the advanced stage. People with diseases like epilepsy, Alzheimer’s, an extreme turmeric state, or a disorder that makes it difficult to move have been observed. The authors propose a cost-effective remote neurology assessment health care system. To predict epilepsy form electroencephalogram (EEG) signals in real-time. The authors implemented the machine learning model that has been deployed in the raspberry pi micro-controller. The feature extraction stage was carried out in Matlab. The extracted features from the EEG signals were transferred wirelessly to the model deployed in pi raspberry to clearly predict epilepsy and normality cases. The results of the real-time prediction of the trained and deployed model were provided for the remote diagnosis system. The data visualizations can be done on Android/IOS and Matlab desktop.

Published
2022

7. A machine learning pipeline to classify foetal heart rate deceleration with optimal feature set

Author

Sahana Das, Sk Md Obaidullah, Mufti Mahmud, M. Shamim Kaiser, Kaushik Roy, Chanchal Kumar Saha, and Kaushik Goswami

Subjects
Multidisciplinary
Abstract

Deceleration is considered a commonly practised means to assess Foetal Heart Rate (FHR) through visual inspection and interpretation of patterns in Cardiotocography (CTG). The precision of deceleration classification relies on the accurate estimation of corresponding event points (EP) from the FHR and the Uterine Contraction Pressure (UCP). This work proposes a deceleration classification pipeline by comparing four machine learning (ML) models, namely, Multilayer Perceptron (MLP), Random Forest (RF), Naïve Bayes (NB), and Simple Logistics Regression. Towards an automated classification of deceleration from EP using the pipeline, it systematically compares three approaches to create feature sets from the detected EP: (1) a novel fuzzy logic (FL)-based approach, (2) expert annotation by clinicians, and (3) calculated using National Institute of Child Health and Human Development guidelines. The classification results were validated using different popular statistical metrics, including receiver operating characteristic curve, intra-class correlation coefficient, Deming regression, and Bland-Altman Plot. The highest classification accuracy (97.94%) was obtained with MLP when the EP was annotated with the proposed FL approach compared to RF, which obtained 63.92% with the clinician-annotated EP. The results indicate that the FL annotated feature set is the optimal one for classifying deceleration from FHR.

Published
2023

8. A Review on VANET Research: Perspective of Recent Emerging Technologies

Author

Md. Julkar Nayeen Mahi, Sudipto Chaki, Shamim Ahmed, Milon Biswas, M. Shamim Kaiser, Mohammad Shahidul Islam, Mehdi Sookhak, Alistair Barros, and Md Whaiduzzaman

Subjects
General Computer Science, General Engineering, General Materials Science, Electrical and Electronic Engineering
Published
2022

19. Intelligent Ubiquitous Computing and Advanced Learning Systems for Biomedical Engineering

Author

Chinmay Chakraborty, Mohammad Khosravi (GE), Lalit Garg, M Shamim Kaiser, Xingwang Li (GE), and Houbing Song

Abstract

The health monitoring for disease diagnosis and prognosis in a desired smart medical structure is realized by interpreting the health data. The advances in sensor technologies and biomedical data acquisition tools have led to the new era of big data, where different sensors collect massive medical data every day. This special issue explores the latest development in emerging technologies of biomedical engineering, including big medical data, artificial intelligence, cloud/fog computing, federated learning, ubiquitous computing and communication, internet of things, wireless technologies, and, security and privacy. The biological wearable sensors can enhance the decision-making and early disease diagnosis processes by intelligently investigating and collecting large amounts of biomedical data (i.e., big health data). Hence, there is a need for scalable advanced learning, and intelligent algorithms that lead to reliable and interoperable solutions to make effective decisions in emergency medicine technologies. The optimization algorithms can be used in order to acquire the sensor data from multiple sources for fast and accurate health monitoring.

Published
2022

22. Towards Machine Learning Based Intrusion Detection in IoT Networks

Author

Nahida Islam, Fahiba Farhin, Ishrat Sultana, M. Shamim Kaiser, Md. Sazzadur Rahman, Mufti Mahmud, A. S. M. Sanwar Hosen, and Gi Hwan Cho

Subjects
Biomaterials, Mechanics of Materials, Modeling and Simulation, Electrical and Electronic Engineering, Computer Science Applications
Published
2021

23. Pathological test type and chemical detection using deep neural networks: a case study using ELISA and LFA assays

Author

M. Shamim Kaiser, Kamal Abuhassan, Mohammed Alamgir Hossain, and Marzia Hoque Tania

Subjects
Computer science, General Decision Sciences, 02 engineering and technology, 01 natural sciences, Management of Technology and Innovation, Diagnosis, Machine learning, artificial_intelligence_robotics, Pathological, Decision Sciences(all), business.industry, 010401 analytical chemistry, Colourimetric test, Pattern recognition, 021001 nanoscience & nanotechnology, 0104 chemical sciences, Test (assessment), Point-of-care system, Pre-trained model, Deep neural networks, Computer vision, Artificial intelligence, 0210 nano-technology, business, Information Systems
Abstract

PurposeThe gradual increase in geriatric issues and global imbalance of the ratio between patients and healthcare professionals have created a demand for intelligent systems with the least error-prone diagnosis results to be used by less medically trained persons and save clinical time. This paper aims at investigating the development of image-based colourimetric analysis. The purpose of recognising such tests is to support wider users to begin a colourimetric test to be used at homecare settings, telepathology and so on.Design/methodology/approachThe concept of an automatic colourimetric assay detection is delivered by utilising two cases. Training deep learning (DL) models on thousands of images of these tests using transfer learning, this paper (1) classifies the type of the assay and (2) classifies the colourimetric results.FindingsThis paper demonstrated that the assay type can be recognised using DL techniques with 100% accuracy within a fraction of a second. Some of the advantages of the pre-trained model over the calibration-based approach are robustness, readiness and suitability to deploy for similar applications within a shorter period of time.Originality/valueTo the best of the authors’ knowledge, this is the first attempt to provide colourimetric assay type classification (CATC) using DL. Humans are capable to learn thousands of visual classifications in their life. Object recognition may be a trivial task for humans, due to photometric and geometric variabilities along with the high degree of intra-class variabilities, it can be a challenging task for machines. However, transforming visual knowledge into machines, as proposed, can support non-experts to better manage their health and reduce some of the burdens on experts.

Published
2020

24. Rhythmic Pattern of EEG for Identifying Schizophrenia

Author

Sh Hussain Hussain, Fuad M. Noman, Hadri Hussain, Chee-Ming Ting, Syed Rasul Bin G. Syed Hamid, Siti Hadrina Bt Sheikh Hussain, M. A. Jalil, A. L. Ahmad Zubaidi, Syed Zuhaib, Kuryati bt Kipli, Kavikumar Jacob, Kanad Ray, M. Shamim Kaiser, Mufti Mahmud, and Jalil Ali

Published
2022

37. Drop-Shaped Fractal Patch Antenna for THz Applications

Author

Anita Garhwal, Muhammad Arif Jalil, Mufti Mahmud, M. Shamim Kaiser, Kanad Ray, Preecha Yupapin, P. Prabpal, Syed Zuhaib Haider Rizvi, Kavikumar Jacob, Anirban Bandyopadhyay, and Jalil Ali

Published
2022

44. A Brief Review of Computation Techniques for ECG Signal Analysis

Author

Salleh Sh Hussain, Fuad Noman, Hadri Hussain, Chee-Ming Ting, Syed Rasul G. Syed bin Hamid, Hadrina Sh-Hussain, M. A. Jalil, Ahmad Zubaidi A.L., Syed Zuhaib Haider Rizvi, Kuryati Kipli, Kavikumar Jacob, Kanad Ray, M. Shamim Kaiser, Mufti Mahmud, and Jalil Ali

Published
2022

49. Contributors

Author

Shiladitya Acharya, Sabbir Ahmed, Bittab Biswas, Sukanta Bose, Susanta Chakraborty, Partha Chaudhuri, Amit Kumar Das, Gourab Das, Poonam B. Dhabai, Pijush Dutta, Gaurav Dwivedi, Adarsh Gaurav, Heba Ahmed Hassan, M. Shahidul Islam, Md. Mehedi Islam, Bhola Jha, M. Shamim Kaiser, Ramani Kannan, Nishant Raj Kapoor, Sujeet Kesharvani, Mohammad Khoobani, Aman Kumar, Anuj Kumar, Krishna Kumar, Narendra Kumar, Yatindra Kumar, Jia Liu, Siva Ramakrishna Madeti, Santanu Maity, Madhurima Majumder, Nabin Chandra Mandal, Anupam Nandi, Morteza Azimi Nasab, Kamaraj Nithyanandhan, Biplab Pal, Manoj Kumar Panda, Tamalika Panda, Md. Sazzadur Rahman, Sourav Sadhukhan, Hiranmay Saha, Gaurav Saini, R.P. Saini, Tina Samavat, Padmanabhan Sanjeevikumar, Sakshi Sarathe, Samir Settoul, Rachna Shah, Neeraj Tiwari, Mohammad Zand, Mohamed Zellagui, and Yuekuan Zhou

Published
2022

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