CSC 490/CSE466 Human Computer Interaction

Course Description

Human-computer interaction and the importance of good interface design. Interface quality and methods of evaluation. Prototyping and implementation techniques. Task analysis and iterative design cycle. Dialog techniques, basic computer graphics, use of colour and sound. I/O device. Menus and their use. Command languages. Screen formatting. Natural language facilities. Case studies and project.

Course Information

Project TitleAbstract
Design of a mHealth App for Gestational Diabetes Mellitus Management and Education in Bangladeshi WomenGestational Diabetes Mellitus (GDM) is a type of diabetes that a woman may experience during their pregnancy period. According to the World Health Organization (WHO), one out of ten pregnant women around the world tend to have GDM and may have a risk to both mother and child of having type two diabetes. Studies reported that around 35% of Bangladeshi women have GDM. For most of the women in Bangladesh GDM is unknown and its education is essential for the women who are pregnant or planning for pregnancy. Although there are some apps available online, due to language barriers it is not in the limelight, so we have designed an app which not only eliminates the language barrier but also manages and educates the women for pre and post-pregnancy GDM-related problems. Our app has features of keeping track and control of food intake, water intake, physical activity, reminder, GDM education, chat forum in social community, game. The features in the tool are HCI based which are known by usability testing, it will help pregnant women with GDM to keep a track of their health and blood glucose level and improve their lifestyle in a better way.
 Stress Management mHealth Application for Healthcare Professionals in Bangladesh Stress Management mHealth Application for Healthcare Professionals in BangladeshThis study explores the development of a usercentered mobile health (mHealth) application aimed at managing stress among healthcare professionals in Bangladesh. The objective of this study was to identify key stressors, coping strategies, challenges during managing stress, the role of technology, and users’ requirements through the survey. Then, based on the findings of the survey we conceptualized an mHealth app that addresses these challenges and help professionals to manage their stress at work. The method includes designing and disseminating a questionnaire, analyzing the data to inform the application’s features, and creating a high-fidelity prototype. The results show that high workload, patient and family pressure, and staffing shortages are major stressors while social support, taking short breaks, and time management are some of the effective strategies for coping with stress. On the other hand, participants demanded for features like guided sessions for meditation, personalized stress management plans, real-time health tracking and expressed willingness to use a smartwatch-based mHealth real-time stress monitoring app. Evaluation of the prototype demonstrated its potential effectiveness in stress management. The study concludes that such an mHealth application could significantly aid healthcare professionals in stress mitigation. We plan to improve the app further based on user feedback.
Smart Emergency Healthcare Service System Based on IOT and AI – A Model based FrameworkThis work presents StaySafeAI, an advanced Android application designed to enhance emergency healthcare through the integration of the Internet of Things (IoT) and Artificial Intelligence (AI). Addressing the increasing demand for home-based healthcare and the challenges faced by an aging population, StaySafeAI offers a novel approach to patient monitoring. The app leverages IoT devices like smartbands to monitor vital health parameters such as heart rate, blood pressure, and SpO2 in real time, enabling swift detection and response to health emergencies. The development of StaySafeAI involved an extensive literature review, analyzing existing IoT applications in healthcare. A detailed survey with 107 participants was conducted to understand user adaptation to smartbands, common diseases among the elderly, and challenges in emergency healthcare. The application was developed using the Rapid Iterative Testing and Evaluation (RITE) method, employing Flutter for cross-platform functionality. Google Fit and AI tools were integrated for efficient data collection and analysis. The app’s usability was tested extensively among diverse user groups to ensure reliability and ease of use. StaySafeAI demonstrated significant potential as an emergency healthcare tool. It successfully detected abnormal health patterns, automatically shared the patient’s location, and alerted emergency contacts, ensuring a rapid medical response. The usability tests yielded positive results, confirming the app’s effectiveness, ease of use, and user satisfaction. Despite limitations in expert feedback due to external factors, StaySafeAI’s positive reception by users underscores its potential impact in transforming emergency healthcare through smart technology integration.
 A Comprehensive Analysis on Nutritional Awareness in Bangladesh and a Proposal of a Mobile Application for Better Health ChoicesEating healthy is important, but it is not enough. It is also important to consume the necessary nutrients that our body needs. Nutrients are essential for growth, repair, and the proper functioning of all bodily systems. In this study we conduct a survey to understand the views and practices among Bangladeshis. Understanding the user needs and mobile app benefits we are proposing a simple solution for the people of Bangladesh. Just simply providing physiological information allows users to know their nutritional needs and make grocery choices accordingly. Among 72 participants 75% wanted this kind of solution and among 10 testers all of them liked using our system.
ShoroSonket: A Vision Transformer based Bengali Sign Language InterpreterEffective communication is a fundamental human right, yet hearing-impaired individuals often face significant challenges in their interactions with the non-sign language using population. In this context, we present a novel web app that leverages state-of-the-art machine learning techniques to interpret Bengali sign language gestures into text, bridging the communication gap between sign language users and non-sign language users. Our approach employs a Vision Transformer (ViT) model, trained on a comprehensive dataset, to accurately capture the spatial and temporal intricacies of sign language gestures. The model achieves an impressive accuracy of 97.41%, with high precision, recall, and F1-Score, making it proficient in real-time sign language interpretation. The web app’s usability was validated through extensive testing, resulting in an average System Usability Scale (SUS) score of 82.5, signifying strong user satisfaction and ease of use. Participants praised its user friendliness, ease of learning, and integration of functions. This initiative not only improves communication for the hearing impaired but also promotes accessibility and inclusivity in society. In future work, we aim to expand our model’s capabilities to encompass various sign language dialects. Our project exemplifies the potential of technology to empower and enrich the lives of individuals with hearing impairments by providing them with a powerful tool for effective communication. 
 ShashthyoShongi: Healthcare Assistance with Emergency Services for Bangladehsi CitizensThis study explores the entire process of creating, assessing, and analyzing the “Shashthyoshongi” healthcare app, including user experience, usability, and correlation analyses. The study employs usability testing scales like SUS, MAUQ and UEQ to give a better understanding of user experience. The methods measure user happiness, app reliability, and preferences using established scales, statistical methods, and online questionnaires. Features that are focused on the user, such as tracking health information, emergency services, and appointment scheduling, are demonstrated in the prototype. Usability evaluations highlight areas of strength and improvement. The result of the study gives positive feedback on user experience. The study ends with flexible suggestions for improving the app and considering possible effects in medical emergencies. Index Terms—Healthcare, Emergency, Services,
 Design Validation of a Complete Dengue Assistance Web App in BanglaIn recent times, dengue fever has become a significant public health concern in Bangladesh and is causing a substantial burden on healthcare systems and society. Our project aims to create a user-centered virtual conversational Agent-Based dengue dashboard, helping users in Bangladesh access accurate information, symptom tracking, and guidance during dengue outbreaks. Our main objective is to create a user-friendly virtual platform with an AI-driven chatbot to help educate and guide dengue patients and caregivers in Bangladesh. We also need to design the platform with a focus on the user’s needs to make it effective and easy to use, and we also need to continuously gather user feedback and make improvements to the system through iterative evaluations. The user-centered approach being used in this project is intended to solve the dengue problem in Dhaka. User personas facilitate the development of chatbot interfaces and virtual dashboards by integrating a range of viewpoints obtained from focus groups, interviews, and surveys. An effective and culturally appropriate set of tools is the goal of the iterative design process, which is guided by user input and health data. Encouraging preventive and health monitoring, the chatbot uses strong natural language processing (NLP) technology to deliver individualized information and help. Tests conducted in the actual world will be used to gauge the project’s efficacy and cultural fit. The project’s objective is to empower people in Bangladesh to prevent dengue by working together with stakeholders and adhering to ethical principles such as user data privacy and accessibility
Maternity and Infant Care Application with Language Flexibility for Bangladeshi MothersWith the advancement of technology, there is a growing need for accessible and user-friendly applications that can provide antenatal and postpartum care guidance. However, many existing solutions are costly and lack the feasibility of receiving care when needed. To address these challenges, we have developed “GorbhoKotha” a mobile application prototype designed to provide accurate and reliable information during pregnancy and infant care in Bangladesh. The application features include a chatbot capable of responding to users’ inquiries, along with carefully selected articles on maintaining good health during pregnancy and caring for newborns. The application also includes functionalities such as searching for nearby pharmacies and hospitals, managing symptoms and diseases, and emergency management for children. To guarantee the usability and inclusivity of our application, we have included concepts of Human-Computer Interaction (HCI) in our design process. Our application takes a human-centered approach, prioritizing the needs and preferences of our users to create a solution that is both effective and accessible.

CSE 425/525: Artificial Intelligence

Course Description

An introduction to the basic principles, techniques, and applications of Artificial Intelligence. Coverage includes perception and learning, searching and logical inference and knowledge base. Methods used in this course will have wide applications in different artificial intelligent systems such as expert system, robotics, computer vision, and natural language processing. Students will have practical experience in designing and implementing components of an intelligent system.

Course Information

Syllabus

TopicsDescriptions# of lectures
IntroductionIntelligent agents: a discussion on what Artificial Intelligence is about and different types of AI agents2
SearchOptimization on a Discrete state-space – Uninformed and informed search methods – BFS, DFS, IDS, A*, and IDA* search methods3
Constraint Satisfaction SearchConstraint Satisfaction Problems (CSP), Arc consistency algorithm3
Local searchHill Climbing, Simulated Annealing, Genetic algorithms, Swarm intelligence – Particle Swarms, Ant Colony Optimization3
Logical ReasoningPropositional logic, Reasoning – Forward and Backward Chaining, *First order Logic and Reasoning2
OptimizationReview of Linear Algebra and Calculus for Multivalued Functions. Optimization of multivariable functions, Directional Derivatives, Gradient, Hessian, Gradient-based Optimization, Numerical Differentiation3
Machine Learning ISupervised learning, Regression, Classification methods – formulation of Linear Regression, Logistic regression, Linear classifiers3
Machine Learning IINeural Networks, Backpropagation, Regression and Multiclass Classification, Training of Neural Networks3

CSE424: Neural Network

Syllabus

WeeksTopicsLecturesPresentation Topics
Week-1Neural Network Basics, Multilayer Perceptron, Linear Classifiers, Loss calculation, Log likelihood loss, Cross Entropy Loss, Softmax Classifier, Different Activation Functions and their Derivatives2
Week-2Gradient Descent, Chain Rule for Derivatives, Back Propagation, Update Rule, Implementation of Multilayer Perceptron from Scratch that uses back propagation2
Week-3Convolutional Neural Network, Filters, Kernels, Convolutional Layer, Max Pool Layer, Activation Function ReLU, Batch Normalization, Implementation of CNN from Scratch2
Week-4Capacity, Overfitting, Under fitting, Regularization, Weight Decay, Dropout, Batch Normalization, Convolutional AutoEncoder, Semantic Segmentation, Different up-sampling method (Deconvolution, Reverse Maxpool)2Presentation: Semantic Segmentation Presentation
1. Segnet
2. FCN-8
Week-5Attention, Where CNN pays attention for classification Concept:
Class Activation Map (CAM)
21. GradCAM
Learn to Pay Attention
Week-6Object Detection, Object localization , Region Proposal, Regional Convolutional Neural Network (R-CNN) , Mask R-CNN21. YOLO
2.Fast R-CNN
3.Faster R-CNN
Week-7Word Embedding, Word2vec, Negative Sampling, Character Level Embedding, Sentence Level Embedding21. Attention all you need
2. BERT
Week-8LSTM/GRU for language model, Neural Machine Translation, LST/GRU + Attention, Image Captioning21. Show, Attend, and Tell
Week-9Self-Attention, Transformer for Neural Machine Translation21. Transformer-XL
Week-10Introduction to Graph Embedding, Node2vec, Graph Convolution Network21. Representation Learning on Graphs: Method and Application
Week-11Graph Neural Network (GNN) style Embedding, Graph Attention Network (GAT) style embedding21. GraphSage
Week-12Advanced Topics Variational Auto Encoder, Generative Adversarial Network, Few/Zero Shot Learning2

CEN/CSE 421: Machine Learning

Course Objective

We have entered the era of big data. This deluge of data calls for automated methods of data analysis, which is what machine learning or pattern recognition provides. Machine learning as a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty (such as planning how to collect more data!). This introductory pattern recognition/machine learning course will give an overview of many popular models and algorithms used in modern machine learning – both statistical and non-statistical. The course will give the student the basic ideas and intuition behind these methods, as well as a more formal understanding of how and why they work. Students will have an opportunity to experiment with machine learning techniques and apply them on real life problems.

Course Information

Syllabus

# of LecturesTopics
1Machine Learning/Pattern Recognition basics:
Supervised and Unsupervised Learning – Classification, Clustering and Regression, Parametric and Non-parametric Models, Curse of Dimensionality, Over-fitting, and Model Selection, Performance Measures
1Data:  Attribute types, Basic Statistical description of Data, Review of probability theory
2Bayesian Decision Theory: Likelihood Ratio Test, Bayes Risk; Bayes, ML and MAP Criteria, Naive Bayes classifier
1Normal Variables and its Discriminant Analysis
1Parametric Density Estimation: MLE, Bayesian Density Estimation
1Nonparametric Density Estimation: Kernel Density Estimators and Nearest Neighbor Method
1Regression: Linear Regression Analysis and Bayesian Linear Regression
2Decision Trees and Random Forests, Ensemble Methods: Bagging and Boosting
4Feature Selection and Extraction, Dimensionality Reduction : PCA and SVD
3Linear Models for Classification: Fisher’s Linear Discriminant, Support Vector Machines
2Introduction to Graphical Models: Bayesian Networks, Exact and Approximate Inference Methods
2*Introduction to Reinforcement Learning: Policy gradient, Q-learning

CSE417: Data mining and Data warehouse

Course Description

We will learn theory, concepts, and applications on how to extract useful information from huge amounts of data.

Course Information

Syllabus

WeekSessionTopicsResourcesAssignments
Week-1Session-1Data Matrix, Attributes, Vector Recap, Basic Statistics, Distributions, PDF, CDFhttps://dataminingbook.info/resources/
Book: DMML, Chapter 1 and Chapter 2
Assignment-1
Week-2Session-2Multivariate Gaussian, Covariance Matrix, Geometry of the multivariate normal, Diagonalization of Covariance Matrixhttps://dataminingbook.info/resources/
Book: DMML, Chapter 1 and Chapter 2
Week-3Session-3Frequent Itemset Mining, The Market-Basket Model, Mining Association Rules, Finding Frequent Pairs, A-Priori Algorithm, FP Growth, *Eclat algorithmhttp://www.mmds.org/#book
Book: MMDS, Chapter 6
Assignment-2
Week-4Session-4Mining Data Streams, General Stream Processing Model, Sampling from a Data Stream, *Queries over a (long) Sliding Windowhttp://www.mmds.org/#book
Book: MMDS, Chapter 4
Week-5Session-5Analysis of Large Graphs: Link Analysis, PageRank, Topic Specific Page rank, *Sim Rankhttp://www.mmds.org/#book
Book: MMDS, Chapter 5
Assignment-3
Week-6Session-6Recommender Systems, Content-based Systems, Collaborative Filteringhttp://www.mmds.org/#book/>Book: MMDS, Chapter 9
Week-7Session-7Recommender Systems, Latent Factor Models, SVDhttp://www.mmds.org/#book
Book: MMDS, Chapter 9, 11
Assignment-4
Week-8Session-8Application of SVD in recommender system, *SVD for dimension reductionhttp://www.mmds.org/#book
Book: MMDS, Chapter 9, 11
Week-9Session-9Analysis of Large Graphs: Community Detection, Betweenness, Modularity, Graph Partitioning, *Graph Cut, Spectral Partitioninghttp://www.mmds.org/#book
Book: MMDS, Chapter 10
Assignment-5
Week-10Session-10Map-Reduce and the New Software Stackhttp://www.mmds.org/#book
Book: MMDS, Chapter 10
Assignment-6
Week-11Session-11*Finding Similar Items: Locality Sensitive Hashing, *Distance Measure, *MinHashinghttp://www.mmds.org/#book
Book: MMDS, Chapter 3
Week-12Session-12TBATBA

CSE317: Numerical Methods

Course Objective

The objective of this course is to introduce the student computational methods required by engineers, mathematicians, physicists and economists to explore complex systems. Mathematical models developed to explore complex systems can be rarely “solvable” algebraically and hence computational methods have been developed. This course introduces such methods that range from techniques for system of linear equations, nonlinear equations, approximation of functions, interpolation, clustering, least square data fitting and classification, differentiation and integration. More emphasis will be put on applied linear algebra topics which are prerequisite for Artificial Intelligence, Machine Learning, and other advanced courses. We will make use of Matlab programming to implement and analyze the methods.

Course Information

Syllabus

TopicsReadings# of lectures
Approximation errors and approximating single variable functions
Floating point number system and error in number representation, review of derivatives, Taylor Series, finding optima of single variable functionsCh 3, 4 NME1
Finding roots of single variable functions – Bisection, Secant and Newton-Raphson MethodCh 5, 6 NME1
Vectors and Matrices
Vectors – review of vector notation, vector operations, linear and affine multivariable functions, complex vectors, complexity of vector computations,
applications: vector representation of data (e.g., images, documents, timeseries, features), vector representation of linear and affine functions (e.g., regression, Linear (Taylor) approximation of multivariable function functions)
Ch 1,2 VMLS2
Norms and distances – Euclidean norm and distances, properties (Cauchy-Schwarz and triangle inequalities, Pythagorean theorem), statistical measurements of data: average, rms, standard deviation, and angle between vectors and correlation, covariance; representation of hyperplanes,
application: single variable linear regression, k-means clustering
Ch 3,4 VMLS2
Direct Methods for Solving System of Linear Equations
Solving system of linear equations using LU decomposition,  application: Polynomial interpolation and Vandermonde matrix, applications of solving system of linear equationsCh 8 VMLS2
Matrix Inverses: Left and right inverses, solving system of linear equations using matrix inverses, Gram matrix and Pseudo-inverseCh 5, 11 VMLS2
Orthogonality and Least Square Methods
Basis, orthogonality and inner products: basis and change of basis, Orthogonal basis, Gram-Schmidt, modified-Gram Schmidt algorithms, QR decomposition of matrices, *Householder reflections,
application: solving system of linear equations using QR factorization, *lower dimensional data representation
Ch 5, 10, 11 VMLS2
Linear least-Squares: solution to over-determined systems, normal equation and pseudo inverse of a matrix, computing pseudo inverse using QR and Cholesky factorization, solving least squares using matrix-vector derivates,
application: data fitting and least-square regression, feature engineering, Least-square classification, regularized least square data fitting, *least square function approximation
Ch 12-14 VMLS; Ch 17 NME3
*Interpolation
Interpolation using monomial and Lagrange bases will be discussed in Linear equation lecture. *Interpolation using other basis functions: Newton, Legendre, Chebyshev bases, Hermite interpolation, cubic spline interpolationCh 18 NME2
Numerical Differentiation and Integration
Finite divided difference approximation of derivatives, Trapezoidal rule, Simpson’s ruleCh 22, 23 NME1
Problem Condition, Algorithm StabilityCh 6, 7 (notes)2