All articles
36 articles
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Traversal Speed Comparison of BFS and DFS in Balanced and Skewed Binary Trees
Abstract: In this paper, we provide an analysis of how well both breadth-first search (BFS) and depth-first search (DFS) algorithms perform while wandering through two kinds of binary trees: balanced and skewed. The research was motivated by the practical application of storing files and directories in a certain type of parent-child relationship through the use of hierarchical file systems (e.g., windows explorer). The result of measuring how fast and versatilely these …
Published in International Journal of Data Structure Studies · Vol. 4, Issue 2, 2026 Read article
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Improving Dataset Integrity Through Automated Data Cleaning Techniques
Abstract: High-quality data is a fundamental requirement in data science for producing trustworthy analytical insights and effective machine learning models. Problems, including incomplete records, inconsistent entries, duplicate observations, and anomalous values, can severely reduce the accuracy and robustness of predictive systems. As modern datasets continue to expand in both volume and structural complexity, relying on manual data cleaning methods become time-consuming and error-prone, highlighting the growing importance of automated data preprocessing …
Published in International Journal of Data Structure Studies · Vol. 4, Issue 1, 2026 · pp. 40–45 Read article
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Learning Data Structures: Key to Good Programming
Abstract: Data structures are the most crucial feature of good programming and are needed to solve hard computational problems. This model makes use of two different recurrent neural network architectures, specifically long short-term memory (LSTM), and gated recurrent unit (GRU) networks. It explains how selecting and using the correct data structures may speed up computations, optimize memory, and scale code. How data structures and algorithms relate and how to think about …
Published in International Journal of Data Structure Studies · Vol. 4, Issue 1, 2026 · pp. 29–39 Read article
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An Auxiliary Array Indexing Approach for Efficient Binary Search in Linked Lists
Abstract: The paper covers an algorithm for searching a linked list structure using binary search. Binary search is a classic example of an algorithm that follows the divide-and-conquer approach. Binary search may be used to find elements in an array. Trying to apply the conventional binary search to a linked list simply does not work out very well; it still has an O(n) time complexity, the same as linear search. This …
Published in International Journal of Data Structure Studies · Vol. 4, Issue 1, 2026 · pp. 21–28 Read article
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Data Structure Driven Probabilistic Deadlock Resolution in Multiprocessor Systems
Abstract: Deadlock resolution in multiprocessor systems is fundamentally a graph-theoretic and probabilistic decision problem. Existing victim selection heuristics, such as youngest, oldest, and lowest priority, apply static rules that overlook the dynamic runtime state of processes, leading to unnecessary computational loss. This paper reframes the inference-guided preemption (IGP) algorithm as a data-structure-centric solution, highlighting how resource allocation graphs, wait-for graphs, adjacency lists, min-heaps, and hash-based evidence stores interact to enable efficient …
Published in International Journal of Data Structure Studies · Vol. 4, Issue 1, 2026 · pp. 11–20 Read article
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Machine Learning Based Sentiment Analysis of Student Feedback in Higher Education
Abstract: Educational institutions routinely collect feedback from students to understand their perceptions of academic programs, infrastructure, and campus facilities, to improve the overall quality of the college environment. In current practice, feedback is often gathered using numerical or grade-based rating systems, which tend to oversimplify student opinions and may overlook important details related to their level of satisfaction. In contrast, open-ended textual feedback allows students to clearly express their views, concerns, …
Published in International Journal of Data Structure Studies · Vol. 4, Issue 1, 2026 · pp. 01–10 Read article
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Application of B-trees for Design of Optimal Page Replacement Technique in Modern Operating Systems
Abstract: Algorithms related to replacing the memory pages in operating systems are critical components of modern operating systems that manage virtual memory efficiently. Current algorithms such as LRU (Least Recently Used), Clock algorithms as well as FIFO (First-In-First-Out), often struggle with the increasing demands of contemporary applications and larger memory hierarchies. This research work proposes a novel approach utilizing B-tree data structures to design an optimal page replacement technique. The proposed …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 2, 2025 · pp. 23–30 Read article
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Securing the Internet of Things: A Survey on Lightweight Blockchain Framework for Enhancing Security and Privacy
Abstract: Modern technologies, supported by the Internet of Things (IoT), have imparted great speed in data sharing between connected devices across several domains of the economy. The Internet of things transformed data collection activities with its creation of smart homes and cities, healthcare, transportation environments, and industrial automation. The fact that there are multiple resource-limited devices, and continuous data transfer of sensitive information has turned the IoT devices less secure and …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 2, 2025 · pp. 6–14 Read article
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Early Flood Detection and Avoidance Using IoT
Abstract: This project presents an advanced flood alert system powered by IoT technology, designed to enhance public safety and minimize flood-related damage in high-risk areas. The system uses various sensors to keep track of environmental conditions, especially changes in water levels. These sensors are strategically placed in critical zones to detect early indicators of flooding, such as sudden increases in water level, surface runoff, and river overflow. The gathered information is …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 2, 2025 · pp. 1–5 Read article
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Semantics Analysis of Expected Goals in Soccer Data Using Machine Learning
Abstract: In recent years, the increasing availability of soccer data has greatly enhanced the accuracy and depth of player performance evaluation. Soccer, being one of the most popular sports worldwide, attracts millions of fans due to its simple rules, minimal equipment requirements, and high entertainment value. However, analyzing an entire match manually can be time-consuming, leading to a growing demand for automated methods that can summarize and interpret game data efficiently. …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 2, 2025 · pp. 31–47 Read article
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Implementing Machine Learning in Data Classification
Abstract: Data classification forms an essential aspect of artificial intelligence (AI) and soft computing, helping a great deal in the transformation of raw data into knowledge that forms the basis of numerous applications, such as fraud detection, medical diagnostics, and natural language processing. This study discusses the challenges and the state of the art in data classification, as far as scalability, noise handling, and feature selection optimization are concerned. It gives …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 2, 2025 · pp. 15–22 Read article
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A Review of Machine Learning Applications in Web Data Mining
Abstract: The rapid development of Internet technology has resulted in a rapidly changing and intricate digital environment that requires new methods for organizing and evaluating online data. This study examines the use of machine learning (ML) in web data mining, focusing on its ability to extract relevant insights from huge amounts of online data. Web data mining, which is divided into three categories: content mining, structure mining, and use mining, uses …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 1, 2025 · pp. 39–47 Read article
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TensorFlow-Based Big Data Analytics for IoT Networks: A Study
Abstract: Internet of Things (IoT) has exploded in recent years, connecting billions of devices generating massive amounts of data. This deluge presents both a significant opportunity and a considerable challenge. While the potential insights hidden within this data are transformative, customary data processing techniques frequently fall short when handled with the velocity, volume, and variety of IoT-generated data. This is where Big Data technologies step in, offering the tools and infrastructure …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 1, 2025 · pp. 31–38 Read article
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Python's Applications in the Profession of Data Science
Abstract: Because of its ease of use, adaptability, and huge ecosystem of libraries, Python has become one of the most influential programming languages in the field of data science. Python is highly valued for its straightforward and versatile nature. This study delves into its various uses in data science, including tasks like data preprocessing, exploratory data analysis (EDA), statistical modeling, machine learning, and creating visualizations. Libraries like Pandas and NumPy make …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 1, 2025 · pp. 23–30 Read article
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Optimizing Data Processing Efficiency in Big Data: Advanced MapReduce Algorithm Innovations
Abstract: The exponential growth of big data in recent years has created an urgent need for innovative and efficient processing frameworks capable of managing and analyzing massive and complex datasets. Among these, MapReduce has gained prominence as a powerful tool for distributed data processing due to its simplicity and scalability. However, traditional MapReduce frameworks often encounter significant limitations in terms of efficiency, scalability, and resource optimization, particularly when handling large-scale and …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 1, 2025 · pp. 1–7 Read article
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Ensuring Data Traceability Across Multiple Cloud Environments
Abstract: This study investigates the challenges and solutions for ensuring data traceability across multiple cloud environments. With organizations' increasing reliance on cloud infrastructure, maintaining data traceability is crucial for compliance, data integrity, and secure data management. The diversity of cloud systems, spanning public, private, and hybrid models, introduces complexities in tracking data lineage, access, and movement. This study delves into multi-cloud strategies' technical and operational hurdles, such as varying data formats, …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 1, 2025 · pp. 08–22 Read article
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Dynamic Skip-Layer Trees: A Novel Data Structure for Efficient Multi-level IoT Data Processing in Smart Cities
Abstract: This paper introduces dynamic skip-layer trees (DSLTs), a novel hierarchical data structure specifically designed for processing and managing multi-layered Internet of Things (IoT) sensor data in smart city environments. DSLTs extend the traditional skip list concept by incorporating dynamic layer adjustment and spatial awareness, enabling efficient querying and updates across various geographical and temporal dimensions. Our experimental results demonstrate that DSLTs achieve up to 40% faster query processing and a …
Published in International Journal of Data Structure Studies · Vol. 2, Issue 2, 2024 · pp. 11–21 Read article
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Algorithmic Strategies for Complex Data Handling: Optimizing Data Structures for Enhanced Computational Performance
Abstract: We live in an age of big data and processing very large often complicated datasets can be crucial to efficient algorithmic performance. This paper discusses different algorithmic techniques when working with difficult data and how to arrange your information structures correctly for better functionality in large-scale methods. It checks the impact of different algorithms like sorting, searching, and hashing in boosting its processing speed as well as memory use. This …
Published in International Journal of Data Structure Studies · Vol. 2, Issue 2, 2024 · pp. 1–10 Read article
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Data Representation and Abstraction in Computer Systems: An Interactive Study
Abstract: Data abstraction simplifies data, while data representation stores or encodes it. Data abstraction in programming involves constructing a data type that hides data representation. This lets consumers concentrate on the data’s primary features rather than its implementation. Data abstraction is common in object-oriented programming and database administration. Computers store data in binary format. The smallest binary unit is a bit, or “binary digit”. Bytes typically have eight bits. Data abstraction …
Published in International Journal of Data Structure Studies · Vol. 2, Issue 2, 2024 · pp. 22–31 Read article
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Efficient Malware Detection in Cybersecurity: Leveraging Advanced Data Structures for Enhanced Threat Identification
Abstract: The cybersecurity landscape is constantly changing with more advanced malware creating major challenges for detection systems. To address these challenges effectively, advanced data structures have become essential in optimizing how data is managed, processed, and analyzed for malware detection. This review paper delves into the role of several cutting-edge data structures—bloom filters, tries, hash tables, graphs, decision trees, and suffix trees—in enhancing the efficiency and accuracy of malware detection mechanisms. …
Published in International Journal of Data Structure Studies · Vol. 2, Issue 2, 2024 · pp. 32–40 Read article