Filter by keyword, subject, or both. Updates live as new model answers are added to our portal.
Critical Analysis of Computational Algorithms: Research Paper Evaluation and Complexity Analysis
This postgraduate Computer Science coursework requires students to undertake a critical technical analysis of a computational algorithm presented in a prescribed academic research paper. Students select one paper from the available options and demonstrate that they understand both the research problem addressed by the authors and the algorithmic solution proposed. The assessment contributes 30% of the overall module grade and is completed individually. assignment The available research papers cover several algorithmic topics, including an improved Dijkstra shortest-path algorithm for sparse networks, a modified merge-sort approach for large-scale datasets, parallel merge sort with load balancing, and a Prim-based algorithm for hierarchical clustering. Students must extract the principal algorithm from their selected paper and explain its purpose, inputs, outputs and operating procedure. A major component involves identifying the research question and computational problem addressed by the selected study. Students then reproduce or extract the proposed algorithm in pseudocode form and clearly identify the information supplied to the algorithm and the outputs it generates. The algorithm must also be explained step by step using straightforward language so that its operation can be understood without relying exclusively on formal notation. The coursework further requires a detailed time-complexity analysis, demonstrating understanding of how computational requirements grow with input size and how the proposed technique compares with alternative or conventional approaches. Students must critically evaluate the algorithm’s strengths, weaknesses, performance characteristics and limitations, and suggest potential improvements where appropriate. The marking rubric gives substantial emphasis to five areas: identifying the computational problem and research questions, extracting the proposed algorithm, identifying inputs and outputs, explaining the algorithm clearly, analysing its time complexity, and critically evaluating its strengths and weaknesses. assignment The written submission must be 800–1,000 words, although the inputs/outputs, pseudocode and time-complexity sections are excluded from that limit. Figures and images are not permitted, and the work must be submitted using the prescribed coursework template in DOC/DOCX format. Overview word count: approximately 340 words. AI-use note: the guideline permits generative AI only for proofreading. AI tools are explicitly not permitted to create the assessed work itself. assignment
Read Model Answer →
Computer Science / Algorithms / Computational Complexity
Modified Merge Sort for Large-Scale Data: Algorithm Analysis, Complexity and Evaluation
This Computational Algorithms and Paradigms assignment critically examines a modified merge sort algorithm designed for large-scale datasets. The work focuses on the computational problem of sorting very large collections of data efficiently while preserving the stability and predictable complexity associated with classical merge sort. The analysed approach replaces recursive processing with an iterative successive-merging strategy intended to reduce stack overhead and improve practical performance on large datasets. Dubba ramesh(up) The first section identifies the underlying computational problem and frames the main research questions. These include how standard merge sort can be modified to improve large-scale performance, whether recursion can be replaced with a non-recursive iterative process, whether the proposed double-merge technique reduces resource consumption, and how its computational performance compares with classical merge sort. Dubba ramesh(up) A technical section then reconstructs the algorithm in pseudocode. The modified process begins with subsequences of size one and repeatedly merges adjacent sorted subsequences, doubling the merge size after each iteration until the entire dataset is sorted. This bottom-up approach removes the recursive decomposition used in conventional merge sort. Dubba ramesh(up) The assignment also identifies the algorithm's principal inputs and outputs. Inputs include the dataset, number of elements, subsequence boundaries and temporary storage required during merging. The resulting output is a fully sorted and stable sequence. Dubba ramesh(up) Complexity analysis shows that the modified algorithm processes approximately n elements across log₂(n) merging levels, resulting in O(n log n) time complexity in both best and worst cases. Because an auxiliary array is used during merging, the reported space complexity is O(n). Dubba ramesh(up) The final critical evaluation highlights the main benefits of the modified approach, including removal of recursive-call overhead, greater stability when processing very large datasets, predictable performance and preservation of merge-sort stability. Its main limitation is the continued requirement for auxiliary memory during the merge operation. The work also notes that the performance advantages are most relevant for large-scale datasets and may be less significant for smaller inputs. Dubba ramesh(up) Overall, the assignment integrates algorithm interpretation, pseudocode extraction, input-output analysis, complexity analysis and critical evaluation within the context of large-scale sorting. Note: this upload appears to be the completed student response rather than the original assessment brief, so the referencing style and exact formal overall word limit are not stated. I would leave the reference-style field as Not specified unless you also upload the official 7COM1078 guideline.
Read Model Answer →
Big Data Analytics / Machine Learning
3,000 words
Machine Learning on Big Data Using PySpark: Large-Scale Data Analysis and Predictive Modelling
This group-based Machine Learning on Big Data project requires students to apply machine learning techniques to a large real-world dataset using PySpark DataFrames and Spark machine-learning libraries. Students select a substantial dataset, ideally between approximately 300 MB and 1 GB, from sources such as Kaggle, workplace data or other valid repositories, and develop an end-to-end big-data analytics workflow. CN7030 CRWK 26T1 The project begins with data loading and preprocessing using PySpark. Students are expected to handle missing values, perform data normalisation and feature engineering, identify class imbalance and propose appropriate mitigation strategies. Where text datasets are selected, additional preprocessing may include stemming, lemmatization and TF-IDF representation. CN7030 CRWK 26T1 The modelling stage requires implementation of an appropriate machine-learning approach using PySpark MLlib or Spark ML. The brief expects a multiclass rather than binary classification problem and allows techniques including multiclass classification, ensemble learning, clustering and text mining. Students must justify their model choice and consider model robustness, bias and variance when attempting to improve predictive performance. CN7030 CRWK 26T1 Students then perform hyperparameter tuning using techniques such as grid search or random search and evaluate the resulting model with appropriate measures. Relevant evaluation outputs may include accuracy, F1-score, precision, recall and a confusion matrix. Results should also be visualised or clearly presented and interpreted to identify meaningful patterns and performance characteristics. CN7030 CRWK 26T1 The project additionally requires consideration of Legal, Social, Ethical and Professional (LSEP) issues. Students discuss potential ethical concerns associated with their dataset, including bias and privacy risks, and propose suitable mitigation strategies. The final work is consolidated into a single user-friendly HTML analytics report that clearly presents the group's preprocessing, modelling, optimisation, evaluation and interpretation. CN7030 CRWK 26T1 CN7030 CRWK 26T1 Overview word count: approximately 335 words. If you are also uploading the presentation separately to the Reference Library, that should be a second entry under “Presentations and Academic Posters”, because the presentation forms a distinct 40% component and assesses understanding of Spark, preprocessing, modelling, optimisation, evaluation and responses to examiner questions. CN7030 CRWK 26T1
Read Model Answer →