Computer Science and Engineering - Tutoring, Assignments, Projects and Research Support Services
Topics Covered
Computer Science and Engineering
Computer Science and Engineering is one of the most rapidly evolving fields in recent years with more advancements and innovations in artificial intelligence, data science, machine learning, deep learning etc. This is one the most attractive courses of study for the students worldwide because of its interdisciplinary nature and vast applications. Despite their interest, students face challenges to achieve higher grades, that lead them to seek professional assistance for their assignments and projects. Dr. NV offers you an ideal help with assignments, projects, research paper writing, thesis writing, dissertation writing in the following topics.
Image Processing
Students often face difficulties in solving image processing assignments, projects and research problems because of the lack of practical understanding of the subject, time constraints and the complexity of the tasks. To address these challenges, Dr. N. Vaijayanthi (Dr. NV) provides personalized guidance to students worldwide, helping them solve the assignments, projects and research problems using tools such as MATLAB, R and Python. She also provides one-on-one subject tutoring to help students gain better understanding of this subject.
With extensive academic experience and expertise in image processing, Dr. NV ensures that students gain a deeper understanding of the subject while achieving their academic goals. She has successfully completed numerous research projects in the field, showcasing her proficiency. She has helped students from universities in Australia, the UK, the US, France, Germany, Malaysia, the UAE and India for Image processing assignments and projects. Some of Dr. NV's notable research works, projects and assignments completed under Dr. NV’s assistance are listed below:
Description
A most serious type of skin cancer is melanoma, also known as malignant melanoma. This type of skin cancer develops in the cells that produce melanin. Early detection is the best way to reduce mortality due to melanoma, but it remains a challenging task for dermatologists to distinguish melanoma from other benign moles.
In this research work, a Computer Aided Diagnosis (CAD) system based on Deep Convolutional Neural Networks (DCNN) has been developed to detect and classify melanoma and non-melanoma dermoscopic images.
Software Tool
The experiments for this work were conducted using MATLAB R2021 (64-bit) environment on a computer with an Intel Core i5 processor, 8 GB RAM, and NVIDIA GeForce MX250 graphics card.
Request for Code
You can get more information about this work or request the code by sending an email or calling Dr. NV.
Research Assistance: Sample Works : Robustness of the Radiomic Features Extracted from CT Images of Lung Patients
Description
This project aims at evaluating the stability of different radiomic features extracted from CT images of lung cancer patients. The robustness of these radiomic features is tested by performing feature classification on the radiomic data extracted from CT images of Non-Small Lung Cancer (NSLC) patients. (Learn More)
Software Tool
The experiments for this work were conducted using MATLAB R2021 (64-bit) environment on a computer with an Intel Core i5 processor, 8 GB RAM, and NVIDIA GeForce MX250 graphics card.
Request for Code
You can get more information about this work or request the code by sending an email or calling Dr. NV.
Dr. NV has provided guidance to university students globally, by offering tailored solutions including code development, project report writing and presentation preparation. She has also helped the students in transforming their project concepts into research articles and writing these articles in LaTeX. Some of the sample project works completed under Dr. NV’s guidance are given below:
Project Assistance: Sample Projects : Forecasting of Beer Production in Australia
Description
This project aims at evaluating the stability of different radiomic features extracted from CT images of lung cancer patients. The robustness of these radiomic features is tested by performing feature classification on the radiomic data extracted from CT images of Non-Small Lung Cancer (NSLC) patients. (Learn More)
Software Tool
The experiments for this work were conducted using MATLAB R2021 (64-bit) environment on a computer with an Intel Core i5 processor, 8 GB RAM, and NVIDIA GeForce MX250 graphics card.
Request for Code
You can get more information about this work or request the code by sending an email or calling Dr. NV.
Project Assistance: Sample Projects : Computer Aided Diagnosis of Breast Cancer using Machine Learning Techniques
Description
A computer-aided diagnostic algorithm based on machine learning techniques for the classification of breast cancer into benign and malignant categories is proposed. This work also investigates the effect of feature selection on classification accuracy by ranking features using the Minimum Redundancy Maximum Relevance (MRMR) algorithm and selecting the most significant features. (Learn More)
Software Tool
The experiments for this work were conducted using MATLAB programming on a computer equipped with an Intel Core i5 processor and 8 GB RAM.
Request for Code
You can get more information about this work or request the code by sending an email or calling Dr. NV.
Dr. NV has provided solutions to assignments for university students globally. Her support includes code development, detailed reports, and thorough explanations of the assignment problems. In addition to delivering high-quality solutions, Dr. NV ensures that students understand the underlying concepts by offering clear justifications and step-by-step reasoning for each solution.
Some sample assignments solved by Dr. NV are given below:
Assignment Assistance: Sample Assignments : Semantic image segmentation using deep learning
Description
The main goal of this work is to build and evaluate semantic segmentation using deep learning architectures. This project also explores the workflow of semantic segmentation using the CamVid dataset. Various data augmentation methods and deep learning networks are employed to evaluate accuracy in segmentation. (Learn More)
Software Tool
The experiments for this work were conducted using MATLAB programming on a computer equipped with an Intel Core i5 processor and 8 GB RAM.
Request for Code
You can get more information about this work or request the code by sending an email or calling Dr. NV.
Assignment Assistance: Sample Assignments : Image Compression Assignment Questions
Description
a) Implement JPEG compression with different degrees of compression by multiplying the quantization table with a constant k ≥ 1. Make k a parameter for both the compression and decompression functions.
b) Present 3 compressed images to demonstrate how varying the value of k affects image quality. Calculate the PSNR (Peak Signal-to-Noise Ratio) for each image. Do you observe any artifacts introduced by JPEG compression as the value of k increases or decreases? Explain why these artifacts occur and identify where they appear, if possible. (Learn More)
Software Tool
The experiments for this assignment were conducted using Python in Jupyter Notebook on a computer with an Intel Core i5 processor and 8 GB RAM.
Request for Code
You can get more information about this work or request the code by sending an email or calling Dr. NV.
Image and Video Analysis
Students often face numerous challenges in solving Image and Video Analysis assignments, projects and research problems due to the technical complexity and interdisciplinary nature of the field. A major difficulty is the lack of practical understanding, as many students struggle to apply theoretical concepts like filtering, segmentation, or motion tracking to real-world data. To overcome these challenges, Dr. NV offers personalized guidance to students worldwide, helping them navigate the complexities of image and video analysis through one-on-one tutoring. With her extensive academic experience and research background in this field, Dr. NV assists students in solving assignments, developing projects and provides research assistance in this subject. Some of the assignments and projects on Image and Video Analysis completed are listed below:
Sample Assignments and Projects:
Description: A GUI tool in MATLAB is developed to perform animation, image analysis, and video analysis. The application is designed to extract and display the features available in both images and videos efficiently.
Request for Code: You can get more information about this project or request the source code by sending an email or calling Dr. N.V..
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Image:
coins.jpgin the Images folder. Your objective is to create an image processing pipeline to automatically count the total value of the 5p and 10p coins (i.e. the silver colour coins) in the image. Analyse the image (visually and/or otherwise) and list four observations that are relevant to achieving your objective. Describe each observation using between 50 to 75 words. - Suggest an approach to label the pixels corresponding to each coin in the image using your knowledge of colour and grey-scale images, thresholding, morphological operations and connected components. Your suggested pipeline should be described as a series of steps, explaining each step and the choice of any parameters used. It is expected that there will be between four to six main steps (excluding image reading and display) to achieve the objective.
Request for Code: You can get solution for this assignment by sending email or calling Dr. N.V..
Computer Vision
Computer Vision assignments are a little more complex than image processing, analysis because the algorithm should be developed in such a way to mimic human vision to detect, classify and make decisions. Students face challenges in doing computer vision assignments because of the complexity in algorithms, handling sensitive real time data, time constraints and lack of proficiency in programming tools such as MATLAB, R and Python.
To overcome these challenges, Dr. NV offers personalized tutoring to students worldwide, helps in solving assignments, developing projects, and provides research support related to Computer Vision, Image Processing, and Image Analysis. With extensive academic experience and a strong research background in these fields, Dr. NV helps students bridge the gap between theoretical knowledge and practical implementation. She provides step-by-step support in code development, algorithm design, and project execution using tools like MATLAB, R, and Python. The following are sample assignments and projects completed on Computer Vision :
Sample Assignments and Projects:
Description: The algorithm based on K-means clustering is developed to detect anomalous network activity in the NSL-KDD dataset, which is a benchmark network intrusion detection system. This algorithm aims to partition the data into k clusters, where each data point is assigned to the cluster with the nearest center.
Software Tool: The experiments for this work were conducted using Python in Jupyter Notebook on a computer with an Intel Core i5 processor and 8 GB RAM.
Request for code: You can get more information about this work or request the code by sending an email or calling Dr. N.V.
Nanofiber Morphology Analysis
Description: The morphology of tissue engineered scaffolds, specifically Polyacrylonitrile (PAN) and Polyvinylidene (PVD) nanofibers, are analyzed. The nanofibers are scanned using an ECHO Revolution microscope and magnified at x4 and x20 levels. The images are analyzed using ImageJ (Fiji) software and MATLAB. Morphological features such as fiber diameter, porosity, pore area, pore perimeter, and fiber orientation are evaluated.
Software Tool: The experiments for this work were conducted using ImageJ and MATLAB on a computer with an Intel Core i5 processor and 8 GB RAM.
Request for code: You can get more information about this work or request the code by sending an email or calling Dr. N.V.
Neural Networks and Deep Learning
Neural networks consist of algorithms developed based on human brain functions and structure for data processing, patter recognition and predictions. To perform these functions, neural networks consist of layers of interconnected nodes, which are trained with the required data to obtain the desired objective. Deep learning, on the other hand, is a neural network with multiple hidden layers, which learn complex features from large datasets.
Dr. NV provides one-on-one tutoring in this subject, helps students in solving assignments, developing projects, and offers research support. Here are some sample assignments solved:
Description: In this project, object recognition of the CIFAR-100 dataset using a deep convolutional neural network (DCNN) is proposed. The proposed DCNN architecture consists of the following steps: (i) Preprocessing, (ii) Feature Extraction with convolutional layers, and (iii) Classification with fully connected layers. In the feature extraction phase, three modules of convolutional layers are used, while the classification phase consists of two fully connected layers and an output layer.
Software Tool: The experiments for this work were conducted using MATLAB on a computer with an Intel Core i5 processor and 8 GB RAM.
Request for code: You can get more information about this work or request the code by sending an email or calling Dr. N.V.
Description: This thesis is aimed at developing an algorithm for image compression of HEVC images using a convolutional autoencoder. The proposed methodology consists of the following steps:
(i) Data Collection and Preprocessing,
(ii) Data Augmentation,
(iii) Deep Convolutional Autoencoder Architecture,
(iv) Performance Evaluation.
Software Tool: The experiments for this work were conducted using MATLAB on a computer with an Intel Core i5 processor and 8 GB RAM.
Request for code: You can get more information about this work or request the code by sending an email or calling Dr. NV.
Cryptography and Network Security
Cryptography deals with securing sensitive information by converting into unreadable format which is called encryption. The encryption is done using different symmetric or asymmetric cryptographic algorithms. At the receiver side, the encrypted message is converted into readable form which is called decryption. Only those with the correct cryptographic key can decrypt the message to get the original information. Cyber security on the other hand deals with protecting computers, data and networks from malware, phishing and data breaches.
Dr. NV provides personalized tutoring to students globally, helping them understand the mathematical concepts related to cryptosystems, crypto algorithms and security. She also assists in solving assignments, developing projects, and provides research assistance in this subject. Some of the sample assignments, projects and research works done under the guidance of Dr. NV are listed below:
Description: In this paper, a comparative study of biomedical and audio signals encryption using Elgamal, Advanced Encryption Standard (AES), Data Encryption Standard (DES), and Rivest-Shamir-Adleman (RSA) encryption algorithms is performed. The computational cost of the encryption algorithm is evaluated using the metrics such as encryption time, decryption time, execution time, memory usage, and throughput.
Software Tool: The experiments for this work were conducted using MATLAB on a computer with an Intel Core i5 processor and 8 GB RAM.
Request for code: You can get more information about this work or request the code by sending an email or calling Dr. NV.
Description: In this thesis, an intrusion detection system with machine learning (ML) techniques has been developed to detect different types of attacks in the IoT network traffic. In this work, as a first step, the data collection is done, and data analysis is performed. After performing analysis, the data is pre-processed to perform data cleaning. The pre-processed data consists of redundant features which would increase the computational complexity of the algorithm. Hence, redundant features are dropped using correlation-based method of feature selection. (Learn More)
Request for code: You can get more information about this work or request the code by sending email or calling Dr. NV.
Software Tool: The experiments for this work were conducted using Python in Jupyter Notebook on a computer with an Intel Core i5 processor and 8 GB RAM.