Flagship Projects – IRIIC UIU

CMED

IRIIC — Sat, 15 Feb 2025 19:24:57 +0000

CMED

Cloud based Medical System for Rural Health Monitoring in Developing Countries

Delivering adequate healthcare in rural and remote areas of developing countries is a major challenge. CMED (Cloud-based MEDical system) addresses this by integrating cloud services with healthcare centers and portable services managed by Community Health Workers (CHWs). CMED categorizes patients into healthy, alarming, and emergency groups, ensuring timely and appropriate care. For complex cases, the system connects CHWs with specialist doctors. Using Identity-Based Encryption, CMED ensures the privacy and confidentiality of patient health information, providing a smart and secure solution to enhance healthcare accessibility for underprivileged populations.

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Specifications

Continuous monitoring of patients’ health status through portable and stationary healthcare systems
Regular check-ups and real-time health data collection by Community Health Workers (CHWs)
Evaluation of patients’ health risks based on collected data
Categorization of patients into healthy, alarming, and emergency groups
Provision of health education and awareness programs to patients and communities
Promoting preventive measures and healthy lifestyle choices to reduce the risk of Non-Communicable Diseases (NCDs)
Focused efforts on preventing NCDs like diabetes, heart disease, and stroke
Early detection and management of risk factors through regular health assessments
Cloud-based system ensures that health records are accessible anytime, anywhere
Secure storage and retrieval of patient health information
Efficient referral system connecting CHWs with specialist doctors for complex cases
Ensuring patients receive timely specialist care when needed
Use of AI to predict risks of stroke, heart attack, and diabetes
Advanced algorithms analyze health data to provide early warnings and preventive recommendations
Comprehensive health records maintained in a secure, cloud-based system
Ensuring healthcare providers have access to up-to-date patient information for informed decision-making

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Diagrams

Publications

Kashfia Sailunaz, Musaed Alhussein, Md. Shahiduzzaman, Farzana Anowar, Khondaker Abdullah Al Mamun, CMED: Cloud based medical system framework for rural health monitoring in developing countries,Computers & Electrical Engineering, Volume 53, 2016,Pages 469-481,ISSN 0045-7906, https://doi.org/10.1016/j.compeleceng.2016.02.005. Computers & Electrical Engineering Volume 53, July 2016, Pages 469-481

Abstract

Healthcare is one of the basic human rights, but providing proper healthcare in rural remote areas of developing countries has always been a challenging task. Telemonitoring has brought some relief with limitations due to accessing patient's health information by healthcare workers and specialists, but merging it with cloud services can be an optimal solution. This proposed framework, CMED (Cloud based MEDical system), is a setup consisting of both healthcare center and a portable healthcare service with a Community Health Worker(CHW), which will sort the patients into three different categories—healthy, alarming and emergency. Upon failing to diagnose a unique problem, it will suggest and contact a specialist doctor and provide suggestions. The privacy and confidentiality of patient's health information will be secured by Identity Based Encryption. In this paper, a framework has been presented for smart and secure healthcare monitoring system for rural under privileged people in developing countries.

Digital health inclusion towards achieving universal health coverage for Bangladesh utilizing general practitioner model Moinul H. Chowdhury a, Rony Chowdhury Ripan a, A.K.M. Nazmul Islam a, Rubaiyat Alim Hridhee a, Farhana Sarker a,b, Sheikh Mohammed Shariful Islam c, Khondaker A. Mamun a,d,*

Abstract:

Bangladesh’s health care system, particularly in rural areas, experiences enormous obstacles in
providing complete preventive and primary healthcare services due to the lack of adequate healthcare facilities,
resource constraints, and a non-functional referral system. To alleviate these problems, in this study, we introduce
the digital general practitioner (GP) model for rural Bangladesh, digital platforms and present a statistical
analysis of the data that was gathered from the pilot project.
Methods: A total of 12,746 people were provided regular health services during the pilot project, from all genders
and age groups, and provided their socio-demographic and healthcare-related data. We analyzed healthcarerelated
data by carrying out both descriptive and inferential statistics.
Results: By utilizing this digital GP model, rural residents can receive routine health screenings at their homes,
identify health risks early, receive consultation and health education, and be referred to GP and upper-level
health facilities as needed. We found that hypertension was more prevalent (4.84% of the served population),
and cancer was the least prevalent of all the NCDs in the studied population (0.05% of the served population).
The population for stroke, hypertension, diabetes increased until the 50–59 age range as age increased, following
which the population proportion declined as age increased. Additionally, 3.96% of young females were severely
malnourished, comparably higher proportion than young males (2.34%).
Conclusion: NCDs such as hypertension, diabetes was prevalent among rural people. Necessary steps should be
taken to raise preventive and primary healthcare awareness among rural people.
Public interest summary: The absence of proper healthcare facilities, resource constraints, and a non-functional
referral system hamper Bangladesh’s health care system’s ability to provide comprehensive preventive and
primary healthcare services in rural area. As a result, patients develop advanced ailments, including noncommunicable
diseases (NCDs), and must seek treatment at an expensive specialty hospital. To resolve this
issue, we introduce a digital GP model for rural Bangladesh, then show digital platforms that use the concept, and
lastly summarize significant findings from the piloted digital GP model. By utilizing this digital GP model, rural
residents can receive routine health screenings at their homes, identify health risks early, receive consultation
and health education, and be referred to GP and upper-level health facilities as need. From our data analysis, we
discovered high burden of NCDs such as hypertension and diabetes in the piloted area. Necessary steps should be
taken to raise preventive and primary healthcare awareness among rural people.

Awards

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AI Reception

IRIIC — Thu, 30 Jan 2025 05:34:59 +0000

AI Reception

Human-Robot Interaction in Bengali Language for Better Customer Satisfaction

Bangladesh is a developing country with a lot of potential. Even though Bangladesh has a lot of complications like overpopulation, the government is working hard to resolve these issues and continue the development process. By integrating technology into different sectors, the government has gained enormous success. To contribute in the digitizing movement of the Government of Bangladesh, an artificial intelligence based system is proposed. The overall objective of this system is to design and develop an Artificial Intelligence based system which can interact with the target audience in banks, universities and hospitals and answer their queries and questions where the main medium of communication is Bengali language. For effective human machine interaction, the system will be presented in a controlled environment with an interface that would ideally put the human subject at ease and facilitate better service.

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Specifications

Detect and recognize distinct faces, remembering past facial features for future references.
Interpret user commands or queries in Bengali using a speech recognition system.
Generate responses in human voice using Text-to-Speech (TTS) synthesis in Bengali.
Interpret unstructured natural language data, which is challenging for machines.
Operate in Bengali for both speech recognition and synthesis.

Diagrams

Publications

S. I. Pranto et al., "AIMS TALK: Intelligent Call Center Support in Bangla Language with Speaker Authentication," 2021 Emerging Technology in Computing, Communication and Electronics (ETCCE), Dhaka, Bangladesh, 2021, pp. 1-6, doi: 10.1109/ETCCE54784.2021.9689831 Published in: 2021 Emerging Technology in Computing, Communication and Electronics (ETCCE) Date of Conference: 21-23 December 2021 Date Added to IEEE Xplore: 26 January 2022 DOI: 10.1109/ETCCE54784.2021.9689831 Publisher: IEEE Conference Location: Dhaka, Bangladesh

Abstract

Call support centers operate over the telephone, connecting customers and receptionists to ensure customer satisfaction by solving their problems. Due to pandemics, customer call support centers have become a popular way of communication that has been used in different domains such as e-commerce, hospitals, banks, credit card support, government offices. Moreover, humans’ limitations to serve 24 hours a day and the fluctuation of waiting time makes it more challenging to satisfy all the customers over call centers. So, customer service needs to be automated to handle customers by providing a domain-based response in the native language, especially in a developing country like Bangladesh, where call support centers are increasing. Although most people use the Bangla language to communicate, little work has been done in customer care automation in the native language. Our developed architecture, “AIMS TALK” can respond to that customer’s need by recognizing users’ voices, specifying customers’ problems in the standardized Bangla language, collecting customers’ responses to the database to give feedback according to the queries. Besides, the system uses MFCC feature extraction for speaker recognition with an average accuracy of 94.38% on 42 people in real-time testing, an RNN-based model for Bangla Automatic Speech Recognition (ASR) with a word error rate (WER) of 42.15%, and sentence summarization we used Sentence similarity measurement technique having an average loss of 0.004. Lastly, we used gTTS that works as Text to Speech Synthesis for the Bangla language in WavNet architecture.

Rahad Arman Nabid, Shehan Irteza Pranto, Nabeel Mohammed, Farhana Sarker, Mohammad Nurul Huda, and Khondaker A. Mamun. “AI Reception: An Intelligent Bengali Receptionist System Integrating with Face, Speech, and Interaction Recognition” In 2021 Int’l Conference on Bangabandhu and Digital Bangladesh (ICBBDB), 03-04 December 2021. (Accepted)

Shehan Irteza Pranto, Rahad Arman Nabid, Ahnaf Mozib Samin, Nabeel Mohammed Farhana Sarker, Mohammad Nurul Huda, and Khondaker A. Mamun. “HumanRobot Interaction in Bengali language for Healthcare Automation integrated with Speaker Recognition and Artificial Conversational Agent” In 2021 3rd International Conference on Electrical and Electronic Engineering (ICEEE), IEEE, 22-24 December 2021.(Manuscript Submitted)

Shehan Irteza Pranto, Rahad Arman Nabid , Ahnaf Mozib Samin , Nabeel Mohammed, Farhana Sarker , Mohammad Nurul Huda, and Khondaker A. Mamun. “Intelligent Call Center Support integrating with Speaker and Interaction Recognition using NLP”, In 2nd International Conference on Emerging Technology in Computing, Communication and Electronics (ETCCE 2021), 21-23 December 2022 (Manuscript Submitted)

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MindWave Analytics

IRIIC — Thu, 30 Jan 2025 03:44:51 +0000

Mindwave Analytics

An intelligent neuromarketing solution for Bangladesh

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Specifications

Utilizes Brain-Computer Interface (BCI) technology to record brain signals.
Analyzes and predicts consumer responses to marketing stimuli.
Adapts advanced Industry 4.0 technologies for accurate consumer insights.
Builds the first local Neuromarketing platform in Bangladesh.
Provides cost-efficient and customized solutions for local businesses.
Positions Bangladesh on the global stage with innovative marketing research tools.

Diagrams

Publications

Mashrur, Fazla Rabbi & Mahmudur Rahman, Khandoker & Miya, Mohammad & Vaidyanathan, Ravi & Anwar, Syed & Sarker, Farhana & Mamun, Khondaker. (2023). An EEG-based Intelligent Neuromarketing System for Predicting Consumers’ Choice. 10.1007/978-981-19-8032-9_3.

Abstract:

Marketers use different marketing strategies to elicit the desired response from the target customers. To measure customer engagement, they usually conduct one-on-one interviews, surveys, broad polls, and focus group discussions. However, these are costly and sometimes unreliable. On the other hand, neuromarketing measures customer response to marketing stimuli by measuring the electrical activity of the brain and has the potential to address these drawbacks. which can be overcome by neuromarketing. In this work, we proposed a prediction algorithm that can identify consumer affective attitude (AA) and purchase intention (PI) from EEG signals from the brain. At first, the raw EEG signals are initially preprocessed to remove noise. After that, three features are extracted: time, frequency, and time frequency domain features. Wavelet packet transform is used to separate the EEG bands in time-domain feature extraction. Then, for feature selection, wrapper-based SVM-RFE is utilized. Finally, we use SVM to distinguish the classes in AA and PI. Results show that for SVM with radial basis function the kernel performs better to classify positive and negative AA with an accuracy of $90\pm 4.33$ and PI with an accuracy of $75\pm 2.5$. So, EEG-based neuromarketing solutions can assist companies and organizations in accurately predicting future consumer preferences. As a result, neuromarketing based-solutions have the potential to increase sales by overcoming the constraints of traditional marketing Keywords Consumer neuroscience Neuromarketing Electroencephalography Consumer behavior Pattern recognition

F. Ishtiaque et al., "BCI-based Consumers’ Preference Prediction using Single Channel Commercial EEG Device," 2022 25th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2022, pp. 43-48, doi: 10.1109/ICCIT57492.2022.10054806. Published in: 2022 25th International Conference on Computer and Information Technology (ICCIT) Date of Conference: 17-19 December 2022 Date Added to IEEE Xplore: 03 March 2023 DOI: 10.1109/ICCIT57492.2022.10054806 Publisher: IEEE Conference Location: Cox's Bazar, Bangladesh

Abstract:

Brain-Computer Interface (BCI) technology is used in neuromarketing to learn how consumers respond to marketing stimuli. This helps evaluate the marketing stimuli which is traditionally done using marketing research procedures. BCI-based neuromarketing promises to replace these traditional marketing research procedures which are time-consuming and costly. Although BCI-based neuromarketing has its difficulty as EEG devices are inconvenient for consumer-grade applications. This study is performed to predict consumers’ affective attitude (AA) and purchase intention (PI) toward a product using EEG signals. EEG signals are collected using a single channel consumer-grade EEG device from 4 healthy participants while they are subject to 3 different types of marketing stimuli; product, promotion, and endorsement. Multi-domain features are extracted from the EEG signals after pre-processing. 52 features are selected among those using SVM-based Recursive Feature Elimination. SMOTE algorithm is used to balance out the dataset. Support Vector Machine (SVM) is used to classify positive and negative affective attitude and purchase intention. The model manages to achieve an accuracy of 88.2% for affective attitude and 80.4% for purchase intention proving the viability of consumer-grade BCI devices in neuromarketing.

Mashrur, F. R., Rahman, K. M., Miya, M. T. I., Vaidyanathan, R., Anwar, S. F., Sarker, F., & Mamun, K. A. (2022). An Intelligent Neuromarketing System for Predicting Consumers’ Future Choice from Electroencephalography Signals. Physiology & Behavior, 113847. IF: 3.2 doi: https://doi.org/10.3389/fnhum.2022.861270

Abstract:

Neuromarketing utilizes Brain-Computer Interface (BCI) technologies to provide insight into consumers' responses on marketing stimuli. In order to achieve insight information, marketers spend about $400 billion annually on marketing, promotion, and advertisement using traditional marketing research tools. In addition, these tools like personal depth interviews, surveys, focus group discussions, etc. are expensive and frequently criticized for failing to extract actual consumer preferences. Neuromarketing, on the other hand, promises to overcome such constraints. In this work, an EEG-based neuromarketing framework is employed for predicting consumer future choice (affective attitude) while they view E-commerce products. After preprocessing, three types of features, namely, time, frequency, and time-frequency domain features are extracted. Then, wrapper-based Support Vector Machine-Recursive Feature Elimination (SVM-RFE) along with correlation bias reduction is used for feature selection. Lastly, we use SVM for categorizing positive affective attitude and negative affective attitude. Experiments show that the frontal cortex achieves the best accuracy of 98.67±2.98, 98±3.22, and 98.67±3.52 for 5-fold, 10-fold, and leave-one-subject-out (LOSO) respectively. In addition, among all the channels, Fz achieves best accuracy 90±7.81, 90.67±9.53, and 92.67±7.03 for 5-fold, 10-fold, and LOSO respectively. Subsequently, this work opens the door for implementing such a neuromarketing framework using consumer-grade devices in a real-life setting for marketers. As a result, it is evident that EEG-based neuromarketing technologies can assist brands and enterprises in forecasting future consumer preferences accurately. Hence, it will pave the way for the creation of an intelligent marketing assistive system for neuromarketing applications in future.

F. R. Mashrur, M. T. I. Miya, F. S. Rawnaque, K. M. Rahman, R. Vaidyanathan, S. F. Anwar, F. Sarker, and K. A. Mamun “MarketBrain: An EEG based intelligent consumer preference prediction system,” in 2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) doi: https://doi.org/10.1109/EMBC46164.2021.9629841 Published in: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Date of Conference: 01-05 November 2021 Date Added to IEEE Xplore: 09 December 2021 DOI: 10.1109/EMBC46164.2021.9629841 Publisher: IEEE Conference Location: Mexico

Abstract:

The traditional marketing research tools (Personal Depth Interview, Surveys, FGD, etc.) are cost-prohibitive and often criticized for not extracting true consumer preferences. Neuromarketing tools promise to overcome such limitations. In this study, we proposed a framework, MarketBrain, to predict consumer preferences. In our experiment, we administered marketing stimuli (five products with endorsements), collected EEG signals by EMOTIV EPOC+, and used signal processing and classification algorithms to develop the prediction system. Wavelet Packet Transform was used to extract frequency bands (δ, θ, α, β 1 , β 2 , γ) and then statistical features were extracted for classification. Among the classifiers, Support Vector Machine (SVM) achieved the best accuracy (96.01±0.71) using 5-fold cross-validation. Results also suggested that specific target consumers and endorser appearance affect the prediction of the preference. So, it is evident that EEG-based neuromarketing tools can help brands and businesses effectively predict future consumer preferences. Hence, it will lead to the development of an intelligent market driving system for neuromarketing applications.

F.S. Rawnaque, K.M. Rahman, S.F. Anwar, et al. Technological advancements and opportunities in Neuromarketing: a systematic review. Brain Inf. 7, 10 (2020). (IF: 6.245 as per Scimago, Scopus) doi: https://doi.org/10.1186/s40708-020-00109-x Brain Informatics volume 7, Article number: 10 (2020)

Abstract:

Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in the Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signals. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope this review will assist future researchers with vital information in the field of Neuromarketing for making novel contributions.

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SuSastho.AI

IRIIC — Sun, 26 Jan 2025 13:06:00 +0000

SuSastho.AI

An AI enabled solution for adolescents in Bangladesh

The innovation lies in integrating AI-powered chatbots with smartphone applications to provide accessible and self-assisted screening for SRMH. Existing data reveal that there are no AI solutions for SRMH in Bangladesh. So the Bangla Medical GPT is a pioneering healthcare technology utilizing advanced NLP algorithms to understand and generate Bangla text, addressing linguistic diversity gaps in AI-driven healthcare. This project’s focus on early detection through technology-driven solutions marks a novel step in improving healthcare accessibility and outcomes. The solution showcases Bangla Medical GPT innovatively enhances healthcare accessibility and inclusivity by enabling comprehensive communication in Bangla, contributing to the digital transformation of healthcare in Bangladesh.

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Specifications

Develops an AI tool for secure access to SRHR/MH knowledge.
Guides adolescents with appropriate pathways to care.
Integrates AI engine into SuSastho platform for seamless access.
Ensures validation for clinical, cultural, and inclusive considerations.
Develops Bangla medical chatbot for confidential support.
Offers education, screening, and referral features for holistic care.

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