Retrieval Mechanism Research Articles

Construction of English Translation Model Based on Neural Network Fuzzy Semantic Optimal Control.

This work addresses four aspects of the English translation model: consistency, model structure, semantic understanding, and knowledge fusion. To solve the problem of lack of personality consistency in the responses generated by neural networks in English translation models, an English translation model with fuzzy semantic optimal control of neural networks is proposed in this study. The model uses a fuzzy semantic optimal control retrieval mechanism to obtain appropriate information from an externally set English information table; to further improve the effectiveness of the model in retrieving correct information, this work adopts a two-stage training method, using ordinary English translation data for model pretraining and then fine-tuning the model using English translation data with optimal control containing fuzzy semantic information. The model consists of two parts, a sequence generation network that can output the probability distribution of words and an evaluation network that can predict future whole-sentence returns. In particular, the evaluation network can evaluate the impact of currently generated words on whole sentences using deep inheritance features so that the model can consider not only the optimal solution for the current words, as in other generative models, but also the optimal solution for future generated whole sentences. The experimental results show that the English translation model with fuzzy semantic optimal control of the neural network proposed in this study can obtain better semantic feature representation by using a novel bidirectional neural network and a masked language model to train sentence vectors; the combination of semantic features and fuzzy semantic similarity features can obtain higher scoring accuracy and better model generalization. In English translation applications, there are large improvements in scoring accuracy and generality.

Implementing a Hybrid Recommendation System to Personalize Customer Experience in E-Commerce Domain

With the rapid use of the internet, a huge amount of data is generated over the network with every passing second. On the other hand, the user demands information that is relevant to his personal search. The processing of such huge data is a challenging task. To serve the user with his specific required information, there is a need for an information retrieval mechanism that can process this large data. A recommendation system is such required technology that retrieves information in order to improve users’ access and thereby recommending items that are relevant to his explicitly mentioned behavior and preferences. The recommendation algorithm analyzes the huge dataset and focuses to recommend accurate content to the user. There are several recommendation systems in use today, some popular among them are Netflix, YouTube, Tinder, and Amazon. In this article, discussion is done on various types of recommendation systems, issues of recommendation systems, and use cases of widely used recommendation engines and their potential benefits. The work introduced in this paper is an integration approach of domain-specific and item-based recommendation system. The performance of the proposed algorithm is measured on evaluation metrics precision and recall. Experimental results prove that the approach introduced in this paper performed well as compared to existing methods.

Ca2+-permeable AMPA receptors set the threshold for retrieval of drug memories.

Frequent relapse prevents the successful treatment of substance use disorders and is triggered in part by retrieval of drug-associated memories. Drug-conditioned behaviours in rodents are reinstated upon drug memory retrieval following re-exposure to cues previously associated with the drug, or the drug itself. Therapies based on mechanistic insights from rodent studies have focused on amnesic procedures of cue-drug associations but with so far limited success. Conversely, more recent studies propose that inhibiting drug memory retrieval offers improved anti-relapse efficacy. However, mechanisms of memory retrieval are poorly understood. Here, we used a conditioned place preference (CPP) procedure in mice to investigate the cellular and molecular underpinnings of drug-induced memory retrieval. After extinction training of CPP, Ca2+-permeable AMPA receptors (CP-AMPARs) accumulated at drug-generated silent synapses of nucleus accumbens (NAc) medium spiny neurons. The NAc CP-AMPARs regulated the retrieval mechanism of drug memories after extinction. Specifically, we used different priming doses of cocaine, fentanyl, or a cue associated with drug exposure to reinstate CPP, providing different memory retrieval conditions. Although both high and low doses of these two drugs induced CPP reinstatement, compromising CP-AMPAR accumulation impaired CPP reinstatement, induced by low doses of each drug or the cue. This threshold effect was mediated by NAc CP-AMPARs as region specific knock-down of PSD-95 prevented low-dose cocaine-induced retrieval selectively. These results demonstrate the NAc as a brain region and CP-AMPARs as key synaptic substrates that govern the threshold for drug-induced retrieval and behavioural expression of drug memories.

Sequential consumer choice as multi-cued retrieval.

Whether adding songs to a playlist or groceries during an online shop, how do we decide what to choose next? We develop a model that predicts such open-ended, sequential choices using a process of cued retrieval from long-term memory. Using the past choice to cue subsequent retrievals, this model predicts the sequential purchases and response times of nearly 5 million grocery purchases made by more than 100,000 online shoppers. Products can be associated in different ways, such as by their episodic association or semantic overlap, and we find that consumers query multiple forms of associative knowledge when retrieving options. Attending to certain knowledge sources, as estimated by our model, predicts important retrieval errors, such as the propensity to forget or add unwanted products. Our results demonstrate how basic memory retrieval mechanisms shape choices in real-world, goal-directed tasks.

Intercomparison of planetary boundary-layer height in Mexico City as retrieved by microwave radiometer, micro-pulse lidar and radiosondes

The planetary boundary layer (PBL) height plays a major role in air quality and weather forecast studies. However, it cannot be directly measured as it can only be determined from the measured profiles of atmospheric parameters such as., temperature, moisture, and aerosol backscatter based on different retrieval mechanisms. This paper presents the PBL features such as., stable boundary layer (SBL), convective boundary layer (CBL), and residual layer (RL) detected from the microwave radiometer (MWR), mini Micro Pulse Lidar (MPL), and radiosondes (RS) and an intercomparison was done between the instruments during the period of 16 February – 31 May 2019 in Mexico City. RS were launched thrice a day during this period at 06 LST, 12 LST, and 18 LST respectively. The PBL heights comparison was classified into 3 categories: MWR-RS, mini MPL-RS, and mini MPL-MWR. The daytime CBL heights for the MWR-RS comparison correlate well (r = 0.96 at 12 LST, r = 0.88 at 18 LST) as well as RL height (r = 0.94 at 06 LST). The CBL and RL heights for the mini MPL-RS and mini MPL-MWR comparison also agree very well (0.89 < r < 0.99). Overall, the corresponding SBL height comparisons at 06 LST yielded lower agreements (r < 0.66) with the lowest correlation values for the mini MPL-MWR comparison (r = 0.39), which is likely due to the inability of the mini MPL to detect aerosol backscatter below ~120 m above ground level (agl) as well as the stability of the atmosphere, which prevents uniform mixing of the aerosol particles.

Web-Based File Clustering and Indexing for Mindoro State University

Purpose – The Web-Based File Clustering and Indexing for Mindoro State University aim to organize data circulated over the Web into groups/collections to facilitate data availability and access, and at the same time meet user preferences. The main benefits include: increasing Web information accessibility, understanding users’ navigation behavior, improving information retrieval and content delivery on the Web. Web-based file clustering could help in reaching the required documents that the user is searching for. Method – In this paper a novel approach has been introduced for search results clustering that is based on the semantics of the retrieved documents rather than the syntax of the terms in those documents. Data clustering was used to improve the information retrieval from the collection of documents. Data were processed and analyzed using SPSS (version 18) where the instrument was evaluated to test the reliability and validity of the measures used. Evaluation was based on a Likert scale of Excellent, Good, Fair, and Poor as described for the selected quality characteristics. Results – A total of 200 questionnaires were distributed with a return rate of 100%. The questionnaire was tested 0.735 using Cronbach’s Alpha Coefficient and considered a reliable instrument. Four quality characteristics were evaluated in this study; Usability, Performance Efficiency, Reliability, and Functionality Suitability. Conclusion - The Web-based file clustering could help in reaching the required documents that the user is searching for. The need for an information retrieval mechanism can only be supported if the document collection is organized into a meaningful structure, which allows part or all the document collection to be browsed at each stage of a search. Recommendations – It is recommended that upon uploading of file it will show the use of the file and where it is originated (department). It is also recommended to create an index to cluster not only the file type but also the content and use of a file. Explore the clustering to a wider scope. Practical Implications – Document clustering provides a structure for organizing large bodies of text for efficient browsing and searching and helps a lot for the Mindoro State University for records/ document processing. Indexing is the best tool to maintain uniqueness of records in a database. Whenever new files or records are created, it can be easily added to the index. This makes it easy to keep documents up-to-date at all times. Grouping documents into two or more categories improves search time and makes life easier for everyone.

Razvoj i implementacija Registra zaposlenih u sistemu zdravstvene zaštite Republike Srbije

One of the main problems of human resource planning and development in the healthcare system lies in the lack of a comprehensive information system for their monitoring. Improvement of the data collection system on healthcare providers, their organisational structure and employees is necessary to ensure supervision over the development of human resources, healthcare planning and financing, monitoring the provision of services by healthcare professionals and other goals in line with the legislation in force. The register of employees in the Serbian healthcare system has been designed as a Web service with forms for data entry and mechanisms to store that data in a database, to search and export it. HTML, CSS and JavaScript were used to develop the Register of Employees, to design data entry forms as webpages, while PHP was used to link the forms with a database. The register of employees comprises 100 variables divided into the following sections: institution, organisational unit, job position, employee, specialisation, subspecialisation and work engagement of the employee. The Register of Employees was implemented by creating end-user accounts (for HR associates, analysts) and by training end-users to work with the Register of Employees (data entry and updating, data search, report generation). To evaluate the work of the Register of Employees, we used methods of validating the entered data and descriptive statistical methods to present the first results of its use. The Register of Employees was implemented, as of 1 August 2021, in 339 public healthcare institutions and 78 privately-owned healthcare institutions and practices in the Republic of Serbia. Data validation showed that mechanisms of data entry, storage and retrieval were working correctly. The first results of the use of the Registry of Employees demonstrate the value of its use in everyday practice. Use of the Registry of Employees can contribute to more efficient human resource management in the healthcare system and to the improvement of healthcare in the Republic of Serbia.

Lexicon on Board: A MEG Study Based on Expressive Picture-Naming

The cognitive processes associated with word retrieval and the investigation of word-picture differences are complex and not fully understood. Uttering a word entails orchestrating several steps as visual object recognition, accessing a lexical concept, lemma selection, lemma retrieval, accessing the morpheme(s) and generating the phonological word, and finally retrieving syllabic gestural scores and articulation. To study the mechanisms of word retrieval in lemma selection phase, we compared the three groups of monolingual, semi-bilingual and bilingual learners with respect to their topography and strength of Functional Connectivity (FC)values of the most highlighted pair of activated nodes in the time range of 0-150 ms in different frequency bands (delta, theta, alpha, beta, and gamma) upon application of the stimuli. We have seen no significant difference between difference frequency bands (p &gt; 0.05) at the most highlighted FC pairs. However, we observed higher gamma values signifying the semantic activation of the word. We could not find any significant difference between the groups in terms of FC values at designated pairs of nodes signifying that different amount of exposure could not affect electrophysiological patterns in the preliminary step of word production. Acknowledgements This study was supported by the Portuguese Foundation for Science and Technology and the Portuguese Ministry of Science, Technology and Higher Education (UID/PSI/01662/2019), through the national funds (PIDDAC).

Overnight changes to dual-memory processes reflected in speech-perceptual performance.

Adults' ability to attain and retain nonnative speech sound categories vary substantially among individuals. While we know that speech-perceptual skills play a role, we know less about how consolidation-related changes in acoustic-phonetic memory contribute to perceptual tasks. The goal of this investigation was to examine contributions of memory and perceptual skills to the perceptual performance on a trained nonnative speech contrast over two days. Twenty-one adult participants (ages 18-24) completed four different experiments. Three of these assessed learning and memory: visual statistical learning (implicit), visual object recognition (explicit), and nonnative (Hindi dental-retroflex) speech-sound training. Participants completed the learning tasks around 8 p.m., and performance was measured shortly after learning and again 12 hours later. On a separate day, participants completed a categorical perception task on a native (/a/-/e/) vowel continuum. Nonnative speech perception was associated with implicit learning performance when both were assessed shortly after learning, and associated with the retention of explicit memory when both were assessed after an overnight delay. Native speech-sounds were at least marginally associated with nonnative speech perception performance on both days, but with a stronger association observed with performance assessed on Day 2. These findings provide preliminary support for the interpretation that speech-sounds are encoded by at least two memory systems in parallel, but that perceptual performance may reflect acoustic-phonetic knowledge learned by different memory systems over time since exposure. Moreover, performance on speech perception tasks in both native and nonnative speech-sounds may rely on similar retrieval mechanisms for long-term storage of speech-sound information.

FA-RDN: A Hybrid Neural Network on GNSS-R Sea Surface Wind Speed Retrieval

Based on deep learning, this paper proposes a new hybrid neural network model, a recurrent deep neural network using a feature attention mechanism (FA-RDN) for GNSS-R global sea surface wind speed retrieval. FA-RDN can process data from the Cyclone Global Navigation Satellite System (CYGNSS) satellite mission, including characteristics of the signal, spatio-temporal, geometry, and instrument. FA-RDN can receive data extended in temporal dimension and mine the temporal correlation information of features through the long-short term memory (LSTM) neural network layer. A feature attention mechanism is also added to improve the model’s computational efficiency. To evaluate the model performance, we designed comparison and validation experiments for the retrieval accuracy, enhancement effect, and stability of FA-RDN by comparing the evaluation criteria results. The results show that the wind speed retrieval root mean square error (RMSE) of the FA-RDN model can reach 1.45 m/s, 10.38%, 6.58%, 13.28%, 17.89%, 20.26%, and 23.14% higher than that of Backpropagation Neural Network (BPNN), Recurrent Neural Network (RNN), Artificial Neural Network (ANN), Random Forests (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR), respectively, confirming the feasibility and effectiveness of the designed method. At the same time, the designed model has better stability and applicability, serving as a new research idea of data mining and feature selection, as well as a reference model for GNSS-R-based sea surface wind speed retrieval.

A Platform for Keyword Search and its Application for COVID-19 Pandemic Data

Keyword search is typically associated with information retrieval systems. However, recently, keyword search has been expanded to relational databases and RDF datasets, as an attractive alternative to traditional database access. This paper introduces DANKE, a platform for keyword search over databases, and discusses how third-party applications can be equipped with DANKE to take advantage of a data retrieval mechanism that does not require users to have specific technical skills for searching, retrieving and exploring data. The paper ends with the description of an application, called CovidKeyS, which uses DANKE to implement keyword search over three COVID-19 data scenarios.

Using Wikipedia's Big Data for creation of Knowledge Bases

Wikipedia is among the most prominent and comprehensive sources of information available on the WWW. However, its unstructured form impedes direct interpretation by machines. Knowledge Base (KB) creation is a line of research that enables interpretation of Wikipedia's concealed knowledge by machines. In light of the efficacy of KBs for the storage and efficient retrieval of semantic information required for powering several IT applications such Question-Answering System, many large-scale knowledge bases have been developed. These KBs have employed different approaches for data curation and storage. The retrieval mechanism facilitated by these KBs is also different. Further, they differ in their depth and breadth of knowledge. This paper endeavours to explicate the process of KB creation using Wikipedia and compare the prominent KBs developed using the big data of Wikipedia.

G4LDB 2.2: a database for discovering and studying G-quadruplex and i-Motif ligands.

Noncanonical nucleic acid structures, such as G-quadruplex (G4) and i-Motif (iM), have attracted increasing research interests because of their unique structural and binding properties, as well as their important biological activities. To date, thousands of small molecules that bind to varying G4/iM structures have been designed, synthesized and tested for diverse chemical and biological uses. Because of the huge potential and increasing research interests on G4-targeting ligands, we launched the first G4 ligand database G4LDB in 2013. Here, we report a new version, termed G4LDB 2.2 (http://www.g4ldb.com), with upgrades in both content and function. Currently, G4LDB2.2 contains >3200 G4/iM ligands, ∼28 500 activity entries and 79 G4–ligand docking models. In addition to G4 ligand library, we have also added a brand new iM ligand library to G4LDB 2.2, providing a comprehensive view of quadruplex nucleic acids. To further enhance user experience, we have also redesigned the user interface and optimized the database structure and retrieval mechanism. With these improvements, we anticipate that G4LDB 2.2 will serve as a comprehensive resource and useful research toolkit for researchers across wide scientific communities and accelerate discovering and validating better binders and drug candidates.

QoS Improvement Using In-Network Caching Based on Clustering and Popularity Heuristics in CCN.

Content-Centric Networking (CCN) has emerged as a potential Internet architecture that supports name-based content retrieval mechanism in contrast to the current host location-oriented IP architecture. The in-network caching capability of CCN ensures higher content availability, lesser network delay, and leads to server load reduction. It was observed that caching the contents on each intermediate node does not use the network resources efficiently. Hence, efficient content caching decisions are crucial to improve the Quality-of-Service (QoS) for the end-user devices and improved network performance. Towards this, a novel content caching scheme is proposed in this paper. The proposed scheme first clusters the network nodes based on the hop count and bandwidth parameters to reduce content redundancy and caching operations. Then, the scheme takes content placement decisions using the cluster information, content popularity, and the hop count parameters, where the caching probability improves as the content traversed toward the requester. Hence, using the proposed heuristics, the popular contents are placed near the edges of the network to achieve a high cache hit ratio. Once the cache becomes full, the scheme implements Least-Frequently-Used (LFU) replacement scheme to substitute the least accessed content in the network routers. Extensive simulations are conducted and the performance of the proposed scheme is investigated under different network parameters that demonstrate the superiority of the proposed strategy w.r.t the peer competing strategies.

Tubular microdomains of Rab7-positive endosomes retrieve TrkA, a mechanism disrupted in Charcot-Marie-Tooth disease 2B.

Axonal survival and growth requires signalling from tropomyosin receptor kinases (Trks). To transmit their signals, receptor-ligand complexes are endocytosed and undergo retrograde trafficking to the soma, where downstream signalling occurs. Vesicles transporting neurotrophic receptors to the soma are reported to be Rab7-positive late endosomes and/or multivesicular bodies (MVBs), where receptors localize within so-called intraluminal vesicles (herein Rab7 corresponds to Rab7A unless specified otherwise). Therefore, one challenging question is how downstream signalling is possible given the insulating properties of intraluminal vesicles. In this study, we report that Rab7-positive endosomes and MVBs retrieve TrkA (also known as NTRK1) through tubular microdomains. Interestingly, this phenotype is absent for the EGF receptor. Furthermore, we found that endophilinA1, endophilinA2 and endophilinA3, together with WASH1 (also known as WASHC1), are involved in the tubulation process. In Charcot-Marie-Tooth disease 2B (CMT2B), a neuropathy of the peripheral nervous system, this tubulating mechanism is disrupted. In addition, the ability to tubulate correlates with the phosphorylation levels of TrkA as well as with neurite length in neuronal cultures from dorsal root ganglia. In all, we report a new retrieval mechanism of late Rab7-positive endosomes, which enables TrkA signalling and sheds new light onto how neurotrophic signalling is disrupted in CMT2B. This article has an associated First Person interview with the first author of the paper.

From synapse to network: models of information storage and retrieval in neural circuits.

The mechanisms of information storage and retrieval in brain circuits are still the subject of debate. It is widely believed that information is stored at least in part through changes in synaptic connectivity in networks that encode this informationand that these changes lead in turn to modifications of network dynamics, such that the stored information can be retrieved at a later time. Here, we review recent progress in deriving synaptic plasticity rules from experimental dataand in understanding how plasticity rules affect the dynamics of recurrent networks. We show that the dynamics generated by such networks exhibit a large degree of diversity, depending on parameters, similar to experimental observations invivo during delayed response tasks.

Preliminary Application of Space-based Remote Sensing and Geospatial Technology for Investigation on the Geo-environmental Consequences of Cyclone Aila 2009 in the Bangladesh

Climate change phenomena have become a great concern in the context of global change and increased frequency and magnitude of natural disasters throughout the world in the recent time. Natural disasters like floods, cyclones, storm surges etc. often cause significant losses of life, large-scale economic and social impacts, and considerable environmental damages. This paper dealt with study on the overall methodological development to investigate the consequences of climate change extremities particularly, the after effects of cyclone using Remote Sensing (RS) and Geographic Information System (GIS) technology. In this connection, individual functional components have been investigated, tested and verified. Time series multi-sensor satellite data particularly of Landsat, World view (from Google origin) have been utilized to infer information on the consequences of Cyclone Aila 2009 hitting part of southwestern Bangladesh as a test case under the present study. Information retrieval mechanism utilized is based on specially designed methodological framework using satellite-based RS technology along with GIS. Developed technical approach consists of a three-fold components – (i) Establishment of input foundation layer with high spatial details to support characterization, recognition and identification of important surface features using high resolution satellite data; (ii) Effective operational procedure to process, analyze, interpret and finally to archive the retrieved information; (iii) Functional computations were made using spatial modeler language (SML) programming environment under ERDAS Imagine image processing software. Satellite image processing, analysis operation together with image-based spectral characterization of surface features under different stressing conditions etc. have been exercised to derive useful surface information. Specially designed geospatial database has been established in GIS using ArcInfo professional software. Varieties of geospatial data from diverse sources have been incorporated categorically as column-based attributes in GIS. Dual spatial data layers have been generated in GIS for two different dates representing the pre-cyclone and post-cyclone time sequences utilizing appropriate high spatial resolution satellite images. Finally, a geospatial image-based combinational technique has been utilized employing high temporal and moderate spatial resolution time series satellite data with low temporal and high spatial resolution satellite data. Such an operation results in an improved spatial and temporal resolution of output products enabling capture of dynamics of surface features in the spatiotemporal domain providing more precision and details in the output. This study has been supplemented with necessary Ground Position System (GPS)-based ground truthing, selected field data collection and field-based group discussions outcomes.

Geo-AI to aid disaster response by memory-augmented deep reservoir computing

It is a fact that natural disasters often cause severe damage both to ecosystems and humans. Moreover, man-made disasters can have enormous moral and economic consequences for people. A typical example is the large deadly and catastrophic explosion in Beirut on 4 August 2020, which destroyed a very large area of the city. This research paper introduces a Geo-AI disaster response computer vision system, capable to map an area using material from Synthetic Aperture Radar (SAR). SAR is a unique form of radar that can penetrate the clouds and collect data day and night under any weather conditions. Specifically, the Memory-Augmented Deep Convolutional Echo State Network (MA/DCESN) is introduced for the first time in the literature, as an advanced Machine Vision (MAV) architecture. It uses a meta-learning technique, which is based on a memory-augmented approach. The target is the employment of Deep Reservoir Computing (DRC) for domain adaptation. The developed Deep Convolutional Echo State Network (DCESN) combines a classic Convolutional Neural Network (CNN), with a Deep Echo State Network (DESN), and analog neurons with sparse random connections. Its training is performed following the Recursive Least Square (RLS) method. In addition, the integration of external memory allows the storage of useful data from past processes, while facilitating the rapid integration of new information, without the need for retraining. The proposed DCESN implements a set of original modifications regarding training setting, memory retrieval mechanisms, addressing techniques, and ways of assigning attention weights to memory vectors. As it is experimentally shown, the whole approach produces remarkable stability, high generalization efficiency and significant classification accuracy, significantly extending the state-of-the-art Machine Vision methods.

A method for land surface temperature retrieval based on model-data-knowledge-driven and deep learning

Most algorithms for land surface temperature (LST) retrieval depend on acquiring prior knowledge. To overcome this drawback, we propose a novel LST retrieval method based on model-data-knowledge-driven and deep learning, called the MDK-DL method. Based on the expert knowledge and radiation transfer model, we deduce LST retrieval mechanism and determine the best combination of the thermal infrared (TIR) bands of the sensor. Then, we use the radiation transfer model simulation and reliable satellite-ground data to establish a training and test database, and finally use the deep learning neural network for optimal computation. Three typical high-, medium- and low-spatial-resolution TIR remote sensing datasets (from Gaofen, the Moderate Resolution Imaging Spectroradiometer (MODIS), and Fengyun) are used for theoretical simulation and application analysis. The simulation shows that the minimum mean absolute error (MAE) is less than 0.1 K (standard deviation: 0.04 K; correlation coefficient: 1.000) at a small viewing direction (<7.5°) and less than 0.8 K at a large viewing direction (<65°). The in situ validation shows that the minimum MAE obtained by the optimal band combination is approximately 1 K (root mean square error (RMSE) = 1.12 K; coefficient of determination (R2) = 0.902). The retrieval accuracy is improved by increasing the number of TIR bands in the atmospheric window, and adding accurate atmospheric water vapor information produces better results. In general, four TIR bands in the atmospheric window bands are sufficient to retrieve the LST with high accuracy. Likewise, three TIR bands plus atmospheric water vapor information are sufficient for the retrieval requirements. All analyses indicate that our method is feasible and reliably accurate and can also be used to help design the instrument band to retrieve the LST with high precision.

Retrieval of Land-Use/Land Cover Change (LUCC) Maps and Urban Expansion Dynamics of Hyderabad, Pakistan via Landsat Datasets and Support Vector Machine Framework

Land-use/land cover change (LUCC) is an important problem in developing and under-developing countries with regard to global climatic changes and urban morphological distribution. Since the 1900s, urbanization has become an underlying cause of LUCC, and more than 55% of the world’s population resides in cities. The speedy growth, development and expansion of urban centers, rapid inhabitant’s growth, land insufficiency, the necessity for more manufacture, advancement of technologies remain among the several drivers of LUCC around the globe at present. In this study, the urban expansion or sprawl, together with spatial dynamics of Hyderabad, Pakistan over the last four decades were investigated and reviewed, based on remotely sensed Landsat images from 1979 to 2020. In particular, radiometric and atmospheric corrections were applied to these raw images, then the Gaussian-based Radial Basis Function (RBF) kernel was used for training, within the 10-fold support vector machine (SVM) supervised classification framework. After spatial LUCC maps were retrieved, different metrics like Producer’s Accuracy (PA), User’s Accuracy (UA) and KAPPA coefficient (KC) were adopted for spatial accuracy assessment to ensure the reliability of the proposed satellite-based retrieval mechanism. Landsat-derived results showed that there was an increase in the amount of built-up area and a decrease in vegetation and agricultural lands. Built-up area in 1979 only covered 30.69% of the total area, while it has increased and reached 65.04% after four decades. In contrast, continuous reduction of agricultural land, vegetation, waterbody, and barren land was observed. Overall, throughout the four-decade period, the portions of agricultural land, vegetation, waterbody, and barren land have decreased by 13.74%, 46.41%, 49.64% and 85.27%, respectively. These remotely observed changes highlight and symbolize the spatial characteristics of “rural to urban transition” and socioeconomic development within a modernized city, Hyderabad, which open new windows for detecting potential land-use changes and laying down feasible future urban development and planning strategies.