13 Seminar Topics For Computer Science (2024)

If you’re a final-year engineering college student preparing for the CSE seminars, this list of handpicked technical seminar topics for computer science will help you.

Best part?

We have shared the possible topics you can include in each of the following seminar topics and also some project references to start your research work.

Computer science seminar topics at a glance

1. Comparative analysis of convolutional neural networks (CNN) architectures for image classification

Convolutional neural networks (CNNs) are a powerful type of neural network used extensively in computer vision and image classification tasks.

They can automatically learn relevant features from image data. Many different CNN architectures like VGG, ResNet, Inception etc. represent different design philosophies and tradeoffs. Comparing different CNN models in terms of accuracy, efficiency, and complexity can help understand their strengths and weaknesses for a given task.

Goal:

To provide engineering students with an overview and comparative analysis of major convolutional neural network (CNN) architectures for image classification.

What to cover in this seminar:

Brief technical introduction to CNNs – architecture, key layers, how they work for feature extraction and image classification.
Cover 5-6 major CNN architectures in depth: AlexNet, VGGNet, Inception, ResNet, MobileNets. Explain their novel contributions and key technical details on architecture and training.
Analyze the accuracy of the ImageNet dataset. Also compare model size, training times, and compute requirements.
Explain code and demonstrate live training of 2-3 models on GPU to showcase training times.
Use ablation studies and visualizations to illustrate the impact of key architectural innovations of each model.
Evaluate models for a sample application case study (e.g. limited data, constrained platform). Recommend the most suitable model based on findings.
Discuss the latest ideas like Automated Machine Learning, and Neural Architecture Search to auto-generate optimal CNN models.
Conclude with practical guidelines on selecting CNN architectures.

Seminar references:

2. Sentiment analysis using LSTM networks

Sentiment analysis is text classification to detect positive, and negative sentiments. Useful for analyzing customer reviews, survey responses etc. Recurrent neural networks like LSTMs are effective for text modelling due to their ability to capture context and long-range dependencies.

Understanding sentiment analysis architecture and training using LSTMs has many real-world applications in NLP such as:

Analyzing customer feedback and reviews for products/services
Social media monitoring – understanding public sentiment on topics, events
Analyzing survey responses to guide business decisions
Chatbots – detecting user sentiment from conversations
Political sentiment analysis from speeches, news
Healthcare – gauging patient satisfaction from feedback

Goal:

To understand the concept of sentiment analysis and its applications in areas like customer service, social media, healthcare etc.
To learn how LSTM networks work and why they are effective for text modelling and sequence tasks.

What to cover in this seminar:

Introduction with real-world examples of sentiment analysis applications to motivate students.
Explain LSTM architecture with interactive visualizations and examples to build intuition.
Lead students through hands-on coding walkthrough of data preprocessing steps like tokenization, and padding.
Guide students through model implementation in TensorFlow/Keras – adding layers, compiling, and fitting.
Do a live demo of model training and evaluate results on test data. Visualize loss curves.
Analyze misclassified examples to understand model limitations. Invite students’ ideas on improvements.
Perform ablation studies to showcase the impact of model hyperparameters and architecture choices.
Examine attention weights to visualize how LSTMs focus on relevant words.
Discuss enhancements like ensembles, and multitask learning to increase accuracy.

Seminar references:

3. Docker containerization for deployment of cloud-native applications

Docker is a popular container technology that allows packaging apps into portable containers, whereas containers provide efficient, lightweight virtualization to deploy apps in the cloud.

Goal:

To get a strong understanding of containers and how leveraging Docker can improve the development lifecycle of modern cloud applications.

What to cover in this seminar:

Docker and containerization basics
Docker architecture and components
Basic Docker commands – run, pull, ps, exec
Build Docker images with Dockerfile
Docker Compose for multi-container apps
Deploying microservices with Docker
CI/CD pipelines with Docker
Best practices for optimizing Docker images
Troubleshooting common Docker issues
Interactive demos for containerizing apps

Seminar references:

4. Simulation of wireless sensor networks for smart agriculture

This seminar topic focuses on the use of wireless sensor network (WSN) simulations to model and evaluate applications for smart agriculture. You can also discuss how sensors and connectivity can provide data-driven insights to optimize crop yields, water usage, and farm operations.

Goal:

To get an overview of applying wireless sensor networks and simulation to enable data-driven decision-making in agriculture. Through real-world examples and simulation demos, you will have an understanding of this emerging approach for sustainable farming powered by sensor data and analytical models.

What to cover in this seminar:

Introduction to smart agriculture and precision farming techniques
Overview of wireless sensor network topology, hardware, and communication protocols
Data collection, analysis and visualization for agriculture
Simulation fundamentals and agriculture modelling
Hands-on demonstration of an open-source simulator for wireless sensor networks
Testbed concepts and case studies of real-world deployments
Evaluation of feasibility, costs, and challenges of implementing smart agriculture

Seminar references:

5. Dynamic malware analysis using machine learning

Dynamic malware analysis refers to executing and monitoring malware programs in controlled sandbox environments to analyze their runtime behaviours and effects on the systems they infect.

This seminar will provide an overview of leveraging machine learning techniques to perform dynamic analysis of malware.

Goal:

To gain practical knowledge of behaviour-based analysis workflows, the type of features extracted, and how models can be trained on this data to improve detection accuracy while minimizing false positives.

What to cover in this seminar:

Limitations of signature-based and static malware analysis
Introduction to dynamic analysis and sandboxing
Behavioral features extraction using instrumentation
Supervised learning algorithms for malware classification
Simulation of evasive malware samples
Unsupervised learning for anomaly-based detection
Architectures for scalable analysis in the cloud

Seminar references:

https://ieeexplore.ieee.org/abstract/document/8389286/

6. Computer vision for automated visual inspection in manufacturing

This seminar will focus on applying computer vision and image processing techniques to automate visual inspection tasks in manufacturing environments. During this seminar, you will explore how cameras and sensors combined with AI algorithms can reliably detect product and part defects on production lines.

Goal:

To get an overview of the technologies, techniques, and applications of computer vision to radically transform visual inspection from time-consuming human monitoring to accurate and scalable automated quality assurance.

What to cover in this seminar:

Applications of automated visual inspection
Computer vision and deep learning fundamentals
Image processing for manufacturing
Data collection, labelling, and augmentation
Algorithm training for surface, structural and functional defects
Deployment considerations for product lines
Edge computing integrations
Combining CV analysis with process adjustments
Industry case studies and emerging innovations

Seminar references:

7. Supply chain optimization with simulation and AI

This seminar topic focuses on simulation and artificial intelligence techniques to model, analyze, and optimize complex global supply chains. It includes the usage of simulation models and artificial intelligence algorithms to analyze various areas of the supply chain, such as:

inventory management
production scheduling
logistics and distribution.

Goal:

Through real-world applications, you will learn how simulation and AI can provide data-driven insights to dramatically improve forecast accuracy, inventory management, distribution strategies and overall supply network health.

What to cover in this seminar:

Challenges with traditional supply chain management
Simulation modelling for what-if analysis
AI for demand forecasting, delivery optimization
Machine learning applications in planning and scheduling
Digital twin technology and simulation
Analytics for risk detection and mitigation
Industry use cases across manufacturing, transportation etc.
Future directions for intelligent, self-correcting supply networks

Seminar references:

8. Implementing handwriting recognition with LSTM neural networks

With this seminar, you will learn how LSTM models are uniquely equipped to learn both long-range and short-term contextual dependencies in handwriting samples.

Goal:

To learn how recurrent LSTM networks can enable high-accuracy offline handwriting text recognition.

What to cover in this seminar:

Challenges with handwriting recognition
Introduction to LSTM networks
Preprocessing handwriting data
Feature extraction techniques
LSTM model architectures
Training considerations for convergence
Deployment of LSTM neural networks to mobile and edge devices

Seminar references:

9. Grover’s algorithm for database search

In this seminar, discuss the quadratic speedup offered by this algorithm over classical methods, along with the concepts of amplitude amplification and oracle functions on quantum states.

Goal:

To understand how Grover’s algorithm leverages the phenomena of quantum superposition and entanglement to achieve faster search times.

What to cover in this seminar:

Classical vs quantum search
Overview of Grover’s algorithm
Mathematics of amplitude amplification
Constructing quantum oracles
Query and time complexity analysis
Speedup over classical algorithms
Potential real-world applications
Implementation challenges
Future outlook with larger qubit systems

Seminar references:

10. Scalable distributed storage for big data analytics

You can focus on scalable distributed storage and computing architectures enabling big data analytics on massive datasets including the design considerations for storage systems that manage structured, unstructured, and streaming data across clusters.

Goal:

To learn about the architectural view of modern distributed file systems for storing and analyzing big data. Students will understand system design choices, consistency tradeoffs, and integration of commodity hardware for economical and adaptable analytics at scale.

What to cover in this seminar:

Challenges for storage systems in the era of big data
Introduction to distributed file system architecture
Storage considerations for file types and access patterns
Replication strategies for scalability and fault tolerance
Consistency, availability and partition tolerance
Analytics with MapReduce and Spark over distributed storage
Case studies of Hadoop HDFS, Cassandra etc.

Seminar references:

11. Screenless Display technology

This seminar will explore the latest screenless display technologies that can render images and interfaces in midair without requiring a traditional display screen. Also, you can highlight approaches utilizing lasers, holograms, and focus beams of ultrasound waves to create crisp interactive displays on any surface.

Goal:

To understand the techniques and assess the feasibility of applications from augmented reality to interactive 3D data visualization.

What to cover in this seminar:

Limitations of current display technology
Approaches for screenless display
Laser-based aerial display systems
Volumetric and holographic display methods
Acoustic display through ultrasound
Midair haptics for interactive sensations

Seminar references:

Further seminar topic ideas:

12. Parallel computing

Parallel computing involves breaking down large problems into smaller, independent parts that can be processed simultaneously.

This seminar provides an overview of parallel computing concepts, architectures, and techniques to improve computational speed, throughput, and efficiency through concurrent processing.

Goal:

To understand the core principles and tradeoffs with common parallel processing systems. By the seminar’s end, you should be able to evaluate when and how to leverage parallelism across embedded devices, desktops, servers, and high-performance computing clusters to improve software performance.

What to cover in this seminar:

Need for parallel computing
Multicore processors and system architectures
Parallel algorithms, decomposition, and profiling
Programming frameworks (OpenMP, MPI)
Parallel patterns and libraries
GPU computing and CUDA
Case studies and applications
Debugging, testing, and portability
Trends towards exascale computing

Seminar references:

13. Video summarization with deep learning

This seminar focuses on applying deep learning techniques to automatically create concise summaries of long video content by extracting only the most informative parts. This involves the use of technologies like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Transfer Learning, Reinforcement Learning (RL), and more.

Goal:

To learn how advanced neural networks can mimic complex human understanding of video to automatically determine significance and synthesize shortened videos, saving crucial analyst time and resources.

What to cover in this seminar:

Basics of deep learning and its application in video analysis
Overview of existing techniques in video summarization
Deep learning architectures for video summarization
Challenges and limitations in video summarization with deep learning
Case studies or examples demonstrating the effectiveness of deep learning in video summarization

Seminar references:

Final words

That’s all. I hope you’ve now found the right seminar topic ideas to cover in your CSE college seminar. If you have any suggestions, feel free to ping me and get your idea featured on 10Pie.

You can find further resources on computer science and software engineering here:

Mrittika Sengupta

Mrittika Sengupta is a professional content writer with more than 2 years of experience in writing for some of the popular blogs on marketing and tech fields. She also mentors aspiring writers transitioning from academia to the commercial writing world.

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