The Biotechnology PhD candidate, Niravkumar Kosamia will present his research: Multicriteria Feasibility Assessment of BioSuccinic Acid Production from Lignocellulosic Biomass

May 10, 2023
1:00 pm
FB 2023 and Zoom

Committee Members:
Drs. Sudip Rakshit and Arturo Sánchez Carmona (co-supervisors), Dr. Baoqiang Liao, Dr. Siamak Elyasi
and Dr. Vijai Kumar Gupta (external)

Everyone is welcome

For more information contact Brenda Magajna at phd.ses@lakeheadu.ca

Please join the Computer Science Department for the upcoming thesis defense:

Presenter: Jingtian Zhao

Thesis title: Adding Time-series Data to Enhance Performance of Natural Language Processing Tasks


Abstract: In the past few decades, with the explosion of information, a large number of computer scientists have devoted themselves to analyzing collected data and applying these findings to many disciplines. Natural language processing (NLP) has been one of the most popular areas for data analysis and pattern recognition. A significantly large amount of data is obtained in text format due to the ease of access nowadays. Most modern techniques focus on exploring large sets of textual data to build forecasting models; they tend to ignore the importance of temporal information which is often the main ingredient to determine the performance of analysis, especially in the public policy view. The contribution of this paper is three-fold. First, a dataset called COVID-News is collected from three news agencies, which consists of article segments related to wearing masks during the COVID-19 pandemic. Second, we propose a long-short term memory (LSTM)-based learning model to predict the attitude of the articles from the three news agencies towards wearing a mask with both temporal and textural information. Then we added the BERT model to further improve and enhance the performance of the proposed model. Experimental results on the COVID-News dataset show the effectiveness of the proposed LSTM-based algorithm.



Committee Members:
Dr. Yimin Yang (supervisor, committee chair), Dr. Ruizhong Wei (co-supervisor), Dr. Amin Safaei, Dr. Thangarajah Akilan (Software Engineering)

Please contact grad.compsci@lakeheadu.ca for the Zoom link.
Everyone is welcome.

Please join the Computer Science Department for the upcoming thesis defense:

Presenter: Weiting Liu

Thesis title: Vapnik-Chervonenkis Dimension in Neural Networks


Abstract: This academic article aims to explore the potential of statistical concepts, specifically the Vapnik-Chervonenkis Dimension (VCD), in optimizing neural networks. With the increasing use of neural networks and machine learning in replacing human labor, ensuring the safety and reliability of these systems is a critical concern.

The article delves into the question of how to test the safety of neural networks and optimize them through accessible statistical concepts. The article presents two case studies to demonstrate the effectiveness of using VCD in optimizing neural networks. The first case study focuses on optimizing the autoencoder, a neural network with both encoding and decoding functions, through the calculation of the VC dimension. The conclusion suggests that optimizing the activation function can improve the accuracy of the autoencoder at the mathematical level.

The second case study explores the optimization of the VGG16 neural network by comparing it to VGG19 in terms of their ability to process high-density data. By adding three hidden layers, VGG19 outperforms VGG16 in learning ability, suggesting that adjusting the number of neural network layers can be an effective way to optimize neural networks.

Overall, this article proposes that statistical concepts such as VCD can provide a promising avenue for optimizing neural networks, thus contributing to the development of more reliable and efficient machine learning systems. The final vision is to allocate the mathematical model reasonably to machine learning and establish an idealized neural network establishment, allowing for safe and effective use of neural networks in various industries.



Committee Members:
Dr. Yimin Yang (supervisor, committee chair), Dr. Amin Safaei, Dr. Fang (Fiona) Fang (Western University)


Please contact grad.compsci@lakeheadu.ca for the Zoom link.
Everyone is welcome.

Please join the Computer Science Department for the upcoming thesis defense:

Presenter: Weiting Liu

Thesis title: Vapnik-Chervonenkis Dimension in Neural Networks


Abstract: This academic article aims to explore the potential of statistical concepts, specifically the Vapnik-Chervonenkis Dimension (VCD), in optimizing neural networks. With the increasing use of neural networks and machine learning in replacing human labor, ensuring the safety and reliability of these systems is a critical concern.

The article delves into the question of how to test the safety of neural networks and optimize them through accessible statistical concepts. The article presents two case studies to demonstrate the effectiveness of using VCD in optimizing neural networks. The first case study focuses on optimizing the autoencoder, a neural network with both encoding and decoding functions, through the calculation of the VC dimension. The conclusion suggests that optimizing the activation function can improve the accuracy of the autoencoder at the mathematical level.

The second case study explores the optimization of the VGG16 neural network by comparing it to VGG19 in terms of their ability to process high-density data. By adding three hidden layers, VGG19 outperforms VGG16 in learning ability, suggesting that adjusting the number of neural network layers can be an effective way to optimize neural networks.

Overall, this article proposes that statistical concepts such as VCD can provide a promising avenue for optimizing neural networks, thus contributing to the development of more reliable and efficient machine learning systems. The final vision is to allocate the mathematical model reasonably to machine learning and establish an idealized neural network establishment, allowing for safe and effective use of neural networks in various industries.



Committee Members:
Dr. Yimin Yang (supervisor, committee chair), Dr. Amin Safaei, Dr. Fang (Fiona) Fang (Western University)


Please contact grad.compsci@lakeheadu.ca for the Zoom link.
Everyone is welcome.

The Biotechnology PhD candidate, Mahsa Janati will present her research: Experimental Investigation of Water Entry of a Solid Object and Sand Particles

Committee Members: Dr. Amir Azimi (supervisor), Dr. Eltayeb Mohamedelhassan, Dr. Baoqiang Liao, and Dr. Majid Mohammadian (external)

Everyone is welcome

For more information contact Brenda Magajna at phd.ses@lakeheadu.ca

Please join the Computer Science Department for the upcoming thesis defense:

Presenter: Ahmed Aboulfotouh

Thesis title: User-Centric Clustering and Pilot Assignment in Cell-Free Networks


Abstract: Current 5G networks are primarily built on the cellular massive MIMO physical layer technology which achieved significant improvement in spectral efficiency as compared to previous generations. Nevertheless, there is always an increasing demand for higher data rates, and more reliable and uniform service. After successful massive MIMO deployments, it has become a natural question, "what will the physical layer in beyond 5G and 6G networks be like?"

Cell-free massive MIMO has emerged as a physical layer technology that is one of the candidates to takeover future deployments in beyond 5G and 6G networks. The main concept is to go beyond the cellular paradigm by employing an ultra dense deployment of small-sized multi-antenna access points (APs) which cooperate to serve users in the coverage area, eliminating the notion of boundaries between cells. The cell-free architecture has shown the capability of providing uniform service within the coverage area, while cellular networks suffer from poor performance at cell edges. It also has better ability to manage interference due to cooperation between APs which is not the case in cellular networks with no cooperation.

The most practical form of this paradigm is user-centric cell-free massive MIMO. Instead of allowing all the APs to serve all the users in the network, each user is served by a subset of the APs which ensures that network operation is scalable as the number of users grows. The main objective of this thesis is to provide a structured approach to design the cluster of APs that serve each user which is known as the user-centric clustering problem. In the pursuit to solve the clustering problem, there is another problem which is tightly connected to it, the pilot assignment problem. Both problems have to be solved together to ensure satisfactory network-wide performance.

The thesis provides a mathematical formulation for each of the user-centric clustering and the pilot assignment problems as stochastic non-linear binary integer programs which are solved using sample average approximation and the genetic algorithm. The pilot assignment problem is formulated such that it takes into account the usercentric clusters while choosing the pilot assignments which makes the optimization more accurate and efficient. Numerical experiments show that the resulting solutions outperform heuristic baseline algorithms from the literature, leading to significant spectral efficiency gains. Furthermore, we propose an approximate approach to derive closed form expressions of the uplink and downlink SINR which eliminates the need for sampling.

In future work, more effort shall be directed towards finding more practical approaches to realize the optimized solutions of both the user-centric clustering and pilot assignment problems with reasonable time-complexity. For instance, one candidate approach is to use machine learning and AI methods to learn structured approaches to solve both problems, using the optimized solutions as a reference for learning.


Committee Members:
Dr. Zubair Fadlullah (supervisor, committee chair), Dr. Muhammad Asaduzzaman, Dr. Al-Sakib Khan Pathan (United International University)

Please contact grad.compsci@lakeheadu.ca for the Zoom link.
Everyone is welcome.

Title: "Biosynthesis of ω–Hydroxy Fatty Acid Polyesters in Nicotiana tabacum Stigmas"

Supervisory Committee:
Dr. Wensheng Qin (Supervisor)
Dr. Isabel Molina (Co-Supervisor)
Dr. Heidi Schraft
Dr. Doug Reid
Dr. Guanqun(Gavin) Chen (External)

All are welcome to attend. Please contact biology@lakeheadu.ca for the meeting ID and password.

Lakehead University International and the Office of Indigenous Initiatives invite all students, staff and faculty to join us for the KAIROS Blanket Exercise.

The KAIROS Blanket Exercise (KBE) is an interactive learning experience that teaches the Indigenous rights history. Developed in response to the 1996 Report of the Royal Commission on Aboriginal Peoples—which recommended education on Canadian-Indigenous history as one of the key steps to reconciliation, the Blanket Exercise covers over 500 years of history in a one-and-a-half-hour participatory workshop.

Blanket Exercise participants take on the roles of Indigenous peoples in Canada. Standing on blankets that represent the land, they walk through pre-contact, treaty-making, colonization and resistance. They are directed by facilitators representing a narrator (or narrators) and the European colonizers.

Participants are drawn into the experience by reading scrolls and carrying cards which ultimately determine their outcomes. By engaging on an emotional and intellectual level, the Blanket Exercise effectively educates and increases empathy. The exercise will be followed by a debriefing session in which participants have the opportunity to discuss the experience as a group. This often takes the form of a talking circle.

Please register in advance at https://forms.gle/THKHU4XaZKVX7N4S9.

Please join the Computer Science Department for the upcoming thesis defense:

Presenter: Khaled Bedda

Thesis title: New Paradigms of Distributed AI for Improving 5G Network Performance


Abstract: With the advent of 5G technology, there is an increasing need for efficient and effective machine learning techniques to support a wide range of applications, from smart cities to autonomous vehicles. The research question is whether distributed machine learning can provide a solution to the challenges of large-scale data processing, resource allocation, and privacy concerns in 5G networks. The thesis examines two main approaches to distributed machine learning: split learning and federated learning. Split learning enables the separation of model training and data storage between multiple devices, while federated learning allows for the training of a global model using decentralized data sources. The thesis investigates the performance of these approaches in terms of accuracy, communication overhead, and privacy preservation. The findings suggest that distributed machine learning can provide a viable solution to the challenges of 5G networks, with split learning and federated learning techniques showing promising results for spectral efficiency, resource allocation, and privacy preservation. The thesis concludes with a discussion of future research directions and potential applications of distributed machine learning in 5G networks.

In this thesis, we investigate the application of distributed machine learning in 5G networks, with a focus on split learning and federated learning techniques for improving spectral efficiency and resource allocation. The research question of whether distributed machine learning can provide comparable performance to centralized methodologies is explored using a novel method of split learning called peer-coordinated sequential split learning and asynchronous federated learning. The results show that the peer-coordinated sequential split learning method provides comparable performance to the vanilla method, while the asynchronous federated learning technique performs well in terms of low latency and network overhead. These findings suggest that distributed machine learning can be effectively applied in 5G applications, improving network performance in terms of spectral efficiency and resource allocation.

We propose two methodologies; PC-SSL peer-coordinated sequential slit learning and AFD Asynchronous federated learning. The proposed PC-SSL minimizes the data transmitted between the client BSs and a server by processing data locally on the clients. This results in low latency and computation overhead in making handoff decisions and other network operations. While the Federated learning model permits a reasonably accurate decision for the resource allocation for different 5G users without violating their privacy or introducing additional load to the network. Experimental results demonstrate the efficiency of the asynchronously weight updating federated learning in contrast with the conventional FedAvg (Federated averaging) strategy and the traditional centralized learning model. In particular, our proposed technique achieves network overhead reduction with a consistent and significantly high prediction accuracy, that validates its low-latency and efficiency advantages



Committee Members:
Dr. Zubair Fadlullah (supervisor, committee chair), Dr. Muhammad Asaduzzaman, Dr. Al-Sakib Khan Pathan (United International University)

Please contact grad.compsci@lakeheadu.ca for the Zoom link.
Everyone is welcome.

Please join the Computer Science Department for the upcoming thesis defense:

Presenter: Anwar As'ad

Thesis title: Moreau Envelopes-based Personalized Asynchronous Federated Learning: Improving Practicality in Distributed Machine Learning


Abstract: Federated learning is a promising approach for training models on distributed data, driven by increasing demand in various industries. However, it faces several challenges, including communication bottlenecks and client data heterogeneity. Personalized asynchronous federated learning addresses these challenges by customizing the model for individual users based on their local data while trading model updates asynchronously. In this paper, we propose Personalized Moreau Envelopes-based Asynchronous Federated Learning (APFedMe) that combines personalized learning with asynchronous communication and Moreau Envelopes as clients’ regularized loss functions. Our approach uses the Moreau Envelopes to handle non-convex optimization problems and employs asynchronous updates to improve communication efficiency while mitigating heterogeneity data challenges through a personalized learning environment. We evaluate our approach on several datasets and compare it with PFedMe, FedAvg, and PFedAvg federated learning methods. Our experiments show that APFedMe outperforms other methods in terms of convergence speed and communication efficiency. Then, we mention some well-performing implementations to handle missing data in distributed learning. Overall, our work contributes to the development of more effective and efficient federated learning methods that can be applied in various real-world scenarios.



Committee Members:
Dr. Zubair Fadlullah (supervisor, committee chair), Dr. Muhammad Asaduzzaman, Dr. Al-Sakib Khan Pathan (United International University)

Please contact grad.compsci@lakeheadu.ca for the Zoom link.
Everyone is welcome.