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1011 articles for “structured data”
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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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Impact of Time Complexity Using Array and Linked List in Data Structure
Abstract: Data structures are techniques for maintaining, manipulating, and storing data on a computer, enabling efficient access and modification. They support various operations, such as insertion, deletion, updating, and sorting. Examples of data structure include arrays, linked lists, graphs, heaps, stacks, and queues. Each data structure is designed to meet specific needs and solve particular problems. Typically, we identify the problem, devise a solution as an algorithm, and then write an …
Published in International Journal of Data Structure Studies · Vol. 2, Issue 2, 2024 · pp. 41–48 Read article
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Array-Linked Data Structure: Introducing a Hybrid Model of Memory Management and Faster and Easier Insertion and Reallocation Procedures
Abstract: Here, I have introduced a new data structure titled “Array-Linked Data Structure”. It incorporates a hybrid model of memory allocation, introducing a new insertion procedure in an existing data structure which is faster than that for arrays. It also offers O(c) access time where c is a constant. The access time is worse than O(1) for an array but still better than that for a linked list since the index …
Published in International Journal of Data Structure Studies · Vol. 1, Issue 1, 2023 · pp. 1–11 Read article
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Emerging Trends in Data Structures for Modern Machine Learning Applications
Abstract: In the realm of machine learning, data structures play a pivotal role in facilitating efficient data manipulation, storage, and retrieval, thereby significantly impacting the performance and scalability of machine learning algorithms. In recent years, the field of machine learning has witnessed the emergence of novel data structures tailored to address scalability and efficiency challenges inherent in handling large-scale and high-dimensional data. This study provides a look at the data preprocessing, …
Published in International Journal of Data Structure Studies · Vol. 2, Issue 1, 2024 · pp. 1–7 Read article
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An Investigative Study on Cache-Oblivious Data Structures
Abstract: Cache-oblivious data structures and data management systems have emerged as critical components in modern computing environments, aiming to optimize memory access patterns across different levels of the memory hierarchy without explicit knowledge of cache sizes or configurations. This study presents an overview of cache-oblivious techniques, including adaptive data structures, compression, parallel processing, and security considerations. The workexplores future directions in cache-oblivious systems, such as non-volatile memory support, graph processing, edge …
Published in International Journal of Data Structure Studies · Vol. 1, Issue 2, 2023 · pp. 33–37 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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Advancements in Data Structures: Bridging the Gap Between Theory and Real-world Applications
Abstract: In the rapidly advancing landscape of computer science, this study unfolds a comprehensive exploration of Data Structures, spanning from foundational principles to cutting-edge innovations. Data structures form the backbone of computational processes, and this study aims to dissect and illuminate their pivotal role. Beginning with fundamental concepts such as Arrays, Linked Lists, Stacks, and Queues, the narrative progresses to intricate structures like Trees, Graphs, and Hash Tables. Practical applications in …
Published in International Journal of Data Structure Studies · Vol. 2, Issue 1, 2024 · pp. 14–20 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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Concept of Trees: Using Data Structures
Abstract: AbstractA tree is an important concept used in data structures. All important trees are mentioned here with their detailed explanation and diagrams representing their implementation. Properties of some are also given. Basic terminology is used to define the appropriate and exact meanings of the words taken in explaining trees concept. The overall concept and mainly the types of binary tree are explained.Keywords: Terminology, binary treeCite this ArticleLeetasha Maheshwari. Concept of …
Published in Journal of Computer Technology & Applications · Vol. 6, Issue 3, 2015 · pp. 86–92 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
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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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A Study on Data Science Analytics: Addressing Challenges, Exploring Open Research Issues: A Literature Centric Approach
Abstract: The rapid growth of data science and analytics has revolutionized industries across the globe. This paper presents a comprehensive examination of the challenges encountered in the field of data science analytics and investigates unresolved research issues through a literature-centric approach. By analyzing recent research papers, articles, and industry reports, this study offers insights into the evolving landscape of data science and the critical challenges that data scientists face. Additionally, it …
Published in Journal of Computer Technology & Applications · Vol. 14, Issue 3, 2023 · pp. 70–82 Read article
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Combining Unstructured and Structured Clinical Data in a Hybrid Transformer Model to Enhance Cardiovascular Analytics and Clinical Decision- Making
Abstract: Since cardiovascular disease (CVD) continues to be a major global cause of morbidity and mortality, early and accurate risk prediction is essential for prompt intervention and individualized treatment. This study introduces a new hybrid transformer-based model that combines unstructured clinical narratives, structured data, and customized lifestyle characteristics. A comprehensive understanding of disease progression is made possible by the model's ability to capture contextual, temporal, and patient- specific insights through the …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 1, 2026 · pp. 30–37 Read article
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ASSESSING OF FOREST STRUCTURE USING EARTH OBSERVATION DATA: ACASE STUDY IN MUNESSA FOREST, OROMIA REGION, ETHIOPIA
Abstract: Forest structure is essential for estimating forest-related carbon emissions, analyzing forest degradation, and quantifying the effectiveness of forest restoration initiatives. However, forest structure quantification is only limited to the specific area of interest without considering the whole forest coverage. Remote sensing data can easily deliver a large area to assess forest structure. Therefore, this study aims to assess forest structure of Munessa Natural Forest by integrating satellite based light detection …
Published in International Journal of Land Read article
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Review on Strengthening & Retrofitting of RCC & Masonry Bridge Structure with Analytical Modelling of Sensor Data for the Durability of Bridge Structure
Abstract: In later a long time, monstrous advancement in Structural Health Monitoring (SHM) of bridges makes a difference address the life span and unwavering quality of bridge structure at differentiating stages of their benefit life. This article gives a point-by-point understanding of bridge observing, and it centers on sensors utilized and all sorts of harm location (strain, Displacement, acceleration, and temperature) concurring to bridge nature (scour, suspender failure, disconnection of bolt …
Published in Trends in Transport Engineering and Applications · Vol. 9, Issue 3, 2022 · pp. 16–23 Read article
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Assessing of Forest Structure Using Earth Observation Data: A Case Study in Munessa Forest, Oromia Region, Ethiopia
Abstract: Understanding forest structure is crucial for estimating carbon emissions associated with forests, assessing forest degradation, and evaluating the success of forest restoration efforts. However, forest structure quantification is limited to the area of interest without considering the whole forest coverage. Forest structure may be easily assessed over a wide area using data from remote sensing. Thus, by combining ground observation with satellite-based light detection and ranging (LiDAR) and Sentinel 2 …
Published in International Journal of Land · Vol. 2, Issue 1, 2025 · pp. 28–37 Read article
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Integrating Sensor Technologies and Machine Learning for Detection and Mitigation of Structural Deformity and Slope Failure in Opencast Mines
Abstract: With furtherance in the mining industry, accidents due to slope failure are frequent in mining sites. Slope instability, a complex process, seriously threatens the miner’s life and properties. The damage inflicted by slope failures in the recent past has pulled the attention of authorities toward implementing disaster risk reduction measures. This research aims to develop an innovative approach that combines sensor technologies and machine learning techniques to detect and mitigate …
Published in Journal of Communication Engineering & Systems · Vol. 13, Issue 3, 2023 · pp. 38–45 Read article
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Recent Developments in Structural Genomics: Uncovering Cellular Functions
Abstract: Structural genomics has become a groundbreaking field for understanding cellular functions by revealing the three-dimensional structures of proteins and other biomolecules. This field combines advanced methods like X-ray crystallography, nuclear magnetic resonance spectroscopy, cryo-electron microscopy, and computational modeling to explore the molecular structure and behavior of cellular components. Recent advances have significantly accelerated the pace of structure determination, bolstered by high-throughput methods and artificial intelligence tools like AlphaFold. These developments …
Published in International Journal of Cell Biology and Cellular Functions · Vol. 2, Issue 2, 2024 · pp. 30–35 Read article
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3D Printing for Polymer Science Visualization
Abstract: The burgeoning field of 3D printing offers exciting possibilities for various scientific disciplines. This paper explores the potential integration of 3D printing technology within the realm of polymer analysis. While the core focus of memory forensics investigations lies in digital forensics, the concept of 3D printing complex data structures presents intriguing possibilities for the visualization and communication of findings in polymer science. Here, we propose a future research avenue where …
Published in Journal of Polymer & Composites · Vol. 12, Issue 4, 2024 · pp. 96–101 Read article
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Predicting Dielectric Constants of Polymers Using Molecular Structural Descriptors and Explainable Machine Learning: A Data-Driven Approach
Abstract: Accurate prediction of dielectric constants in polymeric materials is fundamental to the rational design of advanced electronic components, energy storage capacitors, flexible substrates, and high-frequency communication circuits. Conventional approaches to identifying suitable polymer dielectrics rely on extensive experimental synthesis and characterisation, which are both time-consuming and resource-intensive. In this work, an explainable machine learning framework is developed to predict the dielectric constant of polymers directly from molecular structural descriptors derived …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 297–304 Read article