https://reunir.unir.net/handle/123456789/12726 2026-03-30T23:07:01Z 2026-03-30T23:07:01Z Verdú, Elena https://reunir.unir.net/handle/123456789/12758 2022-03-30T09:05:50Z

Editor's Note Verdú, Elena The International Journal of Interactive Multimedia and Artificial Intelligence - IJIMAI (ISSN 1989 - 1660) provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence (AI) tools or tools that use AI with interactive multimedia techniques. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-30T09:05:50Z No. of bitstreams: 1 ijimai_6_2_0_0.pdf: 218730 bytes, checksum: fb02c3bff097ce53b5a827ec0f0ad6e5 (MD5); Made available in DSpace on 2022-03-30T09:05:50Z (GMT). No. of bitstreams: 1 ijimai_6_2_0_0.pdf: 218730 bytes, checksum: fb02c3bff097ce53b5a827ec0f0ad6e5 (MD5)

García-Holgado, Alicia Marcos-Pablos, Samuel García-Peñalvo, Francisco https://reunir.unir.net/handle/123456789/12757 2022-03-30T09:02:48Z

Guidelines for performing Systematic Research Projects Reviews García-Holgado, Alicia; Marcos-Pablos, Samuel; García-Peñalvo, Francisco There are different methods and techniques to carry out systematic reviews in order to address a set of research questions or getting the state of the art of a particular topic, but there is no a method to carry out a systematic analysis of research projects not only based on scientific publications. The main challenge is the difference between research projects and scientific literature. Research projects are a collection of information in different formats and available in different places. Even projects from the same funding call follow a different structure in most of the cases, despite there were some requirements that they should meet at the end of the funding period. Furthermore, the sources in which the scientific literature is available provide metadata and powerful search tools, meanwhile most of the research projects are not stored in public and accessible databases, or the databases usually do not provide enough information and tools to conduct a systematic search. For this reason, this work provides the guidelines to support systematics reviews of research projects following the method called Systematic Research Projects Review (SRPR). This methodology is based on the Kitchenham’s adaptation of the systematic literature review. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-30T09:02:48Z No. of bitstreams: 1 ijimai_6_2_14.pdf: 440071 bytes, checksum: a53edc965492366b973371efa1c5b57a (MD5); Made available in DSpace on 2022-03-30T09:02:48Z (GMT). No. of bitstreams: 1 ijimai_6_2_14.pdf: 440071 bytes, checksum: a53edc965492366b973371efa1c5b57a (MD5)

Villagrá-Arnedo, Carlos Gallego-Durán, Francisco Llorens-Largo, Faraón Satorre-Cuerda, Rosana Compañ-Rosique, Patricia Molina-Carmona, Rafael https://reunir.unir.net/handle/123456789/12756 2022-03-30T08:55:35Z

Time-Dependent Performance Prediction System for Early Insight in Learning Trends Villagrá-Arnedo, Carlos; Gallego-Durán, Francisco; Llorens-Largo, Faraón; Satorre-Cuerda, Rosana; Compañ-Rosique, Patricia; Molina-Carmona, Rafael Performance prediction systems allow knowing the learning status of students during a term and produce estimations on future status, what is invaluable information for teachers. The majority of current systems statically classify students once in time and show results in simple visual modes. This paper presents an innovative system with progressive, time-dependent and probabilistic performance predictions. The system produces by-weekly probabilistic classifications of students in three groups: high, medium or low performance. The system is empirically tested and data is gathered, analysed and presented. Predictions are shown as point graphs over time, along with calculated learning trends. Summary blocks are with latest predictions and trends are also provided for teacher efficiency. Moreover, some methods for selecting best moments for teacher intervention are derived from predictions. Evidence gathered shows potential to give teachers insights on students' learning trends, early diagnose learning status and selecting best moment for intervention. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-30T08:55:35Z No. of bitstreams: 1 ijimai_6_2_12.pdf: 1394113 bytes, checksum: cb7d0a7cbf4cf8d8122808ff1f837de4 (MD5); Made available in DSpace on 2022-03-30T08:55:35Z (GMT). No. of bitstreams: 1 ijimai_6_2_12.pdf: 1394113 bytes, checksum: cb7d0a7cbf4cf8d8122808ff1f837de4 (MD5)

Rathore, Pramod Singh Kumar, Abhishek García-Díaz, Vicente https://reunir.unir.net/handle/123456789/12755 2022-03-30T08:31:00Z

A Holistic Methodology for Improved RFID Network Lifetime by Advanced Cluster Head Selection using Dragonfly Algorithm Rathore, Pramod Singh; Kumar, Abhishek; García-Díaz, Vicente Radio Frequency Identification (RFID) networks usually require many tags along with readers and computation facilities. Those networks have limitations with respect to computing power and energy consumption. Thus, for saving energy and to make the best use of the resources, networks should operate and be able to recover in an efficient way. This will also reduce the energy expenditure of RFID readers. In this work, the RFID network life span will be enlarged through an energy-efficient cluster-based protocol used together with the Dragonfly algorithm. There are two stages in the processing of the clustering system: the cluster formation from the whole structure and the election of a cluster leader. After completing those procedures, the cluster leader controls the other nodes that are not leaders. The system works with a large energy node that provides an amount of energy while transmitting aggregated data near a base station. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-30T08:31:00Z No. of bitstreams: 1 ijimai_6_2_6.pdf: 599844 bytes, checksum: f53bc1f2b8cd1e0072373f85570ad7cf (MD5); Made available in DSpace on 2022-03-30T08:31:00Z (GMT). No. of bitstreams: 1 ijimai_6_2_6.pdf: 599844 bytes, checksum: f53bc1f2b8cd1e0072373f85570ad7cf (MD5)

Fyad, Houda Barigou, Fatiha Bouamrane, Karim https://reunir.unir.net/handle/123456789/12752 2022-03-29T12:25:31Z

An Experimental Study on Microarray Expression Data from Plants under Salt Stress by using Clustering Methods Fyad, Houda; Barigou, Fatiha; Bouamrane, Karim Current Genome-wide advancements in Gene chips technology provide in the “Omics (genomics, proteomics and transcriptomics) research”, an opportunity to analyze the expression levels of thousand of genes across multiple experiments. In this regard, many machine learning approaches were proposed to deal with this deluge of information. Clustering methods are one of these approaches. Their process consists of grouping data (gene profiles) into homogeneous clusters using distance measurements. Various clustering techniques are applied, but there is no consensus for the best one. In this context, a comparison of seven clustering algorithms was performed and tested against the gene expression datasets of three model plants under salt stress. These techniques are evaluated by internal and relative validity measures. It appears that the AGNES algorithm is the best one for internal validity measures for the three plant datasets. Also, K-Means profiles a trend for relative validity measures for these datasets. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-29T12:25:31Z No. of bitstreams: 1 ijimai_6_2_5.pdf: 436078 bytes, checksum: f887b8f6a1bb40e3b530450ae5972038 (MD5); Made available in DSpace on 2022-03-29T12:25:31Z (GMT). No. of bitstreams: 1 ijimai_6_2_5.pdf: 436078 bytes, checksum: f887b8f6a1bb40e3b530450ae5972038 (MD5)

Huang, Wenlin Wu, Qun Dey, Nilanjan Ashour, Amira Fong, Simon James González-Crespo, Rubén https://reunir.unir.net/handle/123456789/12751 2024-08-21T09:31:48Z

Adjectives Grouping in a Dimensionality Affective Clustering Model for Fuzzy Perceptual Evaluation Huang, Wenlin; Wu, Qun; Dey, Nilanjan; Ashour, Amira; Fong, Simon James; González-Crespo, Rubén More and more products are no longer limited to the satisfaction of the basic needs, but reflect the emotional interaction between people and environment. The characteristics of user emotions and their evaluation scales are relatively simple. This paper proposes a three-dimensional space model valence-arousal-dominance (VAD) based on the theory of psychological dimensional emotions. It studies the clustering and evaluation of emotional phrases, called VAdC (VAD-dimensional clustering), which is a kind of the affective computing technology. Firstly, a Gaussian Mixture Model (GMM) based information presentation system was introduced, including the type of the presentation, such as single point, plain, and sphere. Subsequently, the border of the presentation was defined. To increase the ability of the proposed algorithm to handle a high dimensional affective space, the distance and inference mechanics were addressed to avoid lacking of local measurement by using fuzzy perceptual evaluation. By comparing the performance of the proposed method with fuzzy c-mean (FCM), k-mean, hard -c-mean (HCM), extra fuzzy c-mean (EFCM), the proposed VADdC performs high effectiveness in fitness, inter-distance, intra-distance, and accuracy. The results were based on the dataset created from a questionnaire on products of the Ming style chairs online evaluation system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-29T12:19:04Z No. of bitstreams: 1 ijimai_6_2_4.pdf: 1578366 bytes, checksum: b6f221007c63f054582f09f1b62d94a5 (MD5); Made available in DSpace on 2022-03-29T12:19:04Z (GMT). No. of bitstreams: 1 ijimai_6_2_4.pdf: 1578366 bytes, checksum: b6f221007c63f054582f09f1b62d94a5 (MD5)

Rios-Aguilar, Sergio Sarría, Íñigo Beltrán Pardo, Marta https://reunir.unir.net/handle/123456789/12750 2026-03-23T16:40:32Z

NFC and VLC based Mobile Business Information System for Registering Class Attendance Rios-Aguilar, Sergio; Sarría, Íñigo; Beltrán Pardo, Marta This work proposes a Mobile Information System for class attendance control using Visible Light Communications (VLC), and the students’ own mobile devices for automatic clocking in and clocking out. The proposed information system includes (a) VLC physical infrastructure, (b) native Android and iOS apps for the students, and (c) a web application for classroom attendance management. A proof of concept has been developed, setting up a testbed representing a real-world classroom environment for experimentation, using two VLC-enabled LED lighting sources. After three rounds of testing (n=225) under different conditions, it has been concluded that the system is viable and shows consistent positive detections when the smartphones are on the classroom desk within non-overlapped areas of the light circles generated by the LED lighting sources on the table surface. The performed tests also show that if mobile devices are placed within those overlapping areas, the likelihood of a detection error could increase up to nearly 10%, due to multipath effects, and actions can be taken should it happen. Finally, it has to be highlighted that the proposed autonomous class attendance system allows lecturers to focus on making the most of their time in class, transferring knowledge instead of spending time in attendance management task. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-29T11:43:05Z No. of bitstreams: 1 ijimai_6_2_8.pdf: 453041 bytes, checksum: 89edf5d7abd49f4725ef9b1d6e30995a (MD5); Made available in DSpace on 2022-03-29T11:43:05Z (GMT). No. of bitstreams: 1 ijimai_6_2_8.pdf: 453041 bytes, checksum: 89edf5d7abd49f4725ef9b1d6e30995a (MD5)

Saiz, Fátima Barandiaran, Iñigo https://reunir.unir.net/handle/123456789/12749 2022-03-29T11:32:54Z

COVID-19 Detection in Chest X-ray Images using a Deep Learning Approach Saiz, Fátima; Barandiaran, Iñigo The Corona Virus Disease (COVID-19) is an infectious disease caused by a new virus that has not been detected in humans before. The virus causes a respiratory illness like the flu with various symptoms such as cough or fever that, in severe cases, may cause pneumonia. The COVID-19 spreads so quickly between people, affecting to 1,200,000 people worldwide at the time of writing this paper (April 2020). Due to the number of contagious and deaths are continually growing day by day, the aim of this study is to develop a quick method to detect COVID-19 in chest X-ray images using deep learning techniques. For this purpose, an object detection architecture is proposed, trained and tested with a public available dataset composed with 1500 images of non-infected patients and infected with COVID-19 and pneumonia. The main goal of our method is to classify the patient status either negative or positive COVID-19 case. In our experiments using SDD300 model we achieve a 94.92% of sensibility and 92.00% of specificity in COVID-19 detection, demonstrating the usefulness application of deep learning models to classify COVID-19 in X-ray images. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-29T11:32:54Z No. of bitstreams: 1 ijimai_6_2_2.pdf: 562218 bytes, checksum: b0cd720d8a9ec1a16263f8044cd49428 (MD5); Made available in DSpace on 2022-03-29T11:32:54Z (GMT). No. of bitstreams: 1 ijimai_6_2_2.pdf: 562218 bytes, checksum: b0cd720d8a9ec1a16263f8044cd49428 (MD5)

Verma, Kamal Kant Singh, Brij Mohan Mandoria, H L Chauhan, Prachi https://reunir.unir.net/handle/123456789/12733 2022-03-28T09:36:45Z

Two-Stage Human Activity Recognition Using 2D-ConvNet Verma, Kamal Kant; Singh, Brij Mohan; Mandoria, H L; Chauhan, Prachi There is huge requirement of continuous intelligent monitoring system for human activity recognition in various domains like public places, automated teller machines or healthcare sector. Increasing demand of automatic recognition of human activity in these sectors and need to reduce the cost involved in manual surveillance have motivated the research community towards deep learning techniques so that a smart monitoring system for recognition of human activities can be designed and developed. Because of low cost, high resolution and ease of availability of surveillance cameras, the authors developed a new two-stage intelligent framework for detection and recognition of human activity types inside the premises. This paper, introduces a novel framework to recognize single-limb and multi-limb human activities using a Convolution Neural Network. In the first phase single-limb and multi-limb activities are separated. Next, these separated single and multi-limb activities have been recognized using sequence-classification. For training and validation of our framework we have used the UTKinect-Action Dataset having 199 actions sequences performed by 10 users. We have achieved an overall accuracy of 97.88% in real-time recognition of the activity sequences. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-28T09:36:45Z No. of bitstreams: 1 ijimai_6_2_13.pdf: 1246366 bytes, checksum: 03e24ca5fdcf5ed6e210eb3694602aa7 (MD5); Made available in DSpace on 2022-03-28T09:36:45Z (GMT). No. of bitstreams: 1 ijimai_6_2_13.pdf: 1246366 bytes, checksum: 03e24ca5fdcf5ed6e210eb3694602aa7 (MD5)

Dur-e-Ahmad, Muhammad Imran, Mudassar https://reunir.unir.net/handle/123456789/12732 2022-03-28T09:19:04Z

Transmission Dynamics Model of Coronavirus COVID-19 for the Outbreak in Most Affected Countries of the World Dur-e-Ahmad, Muhammad; Imran, Mudassar The wide spread of coronavirus (COVID-19) has threatened millions of lives and damaged the economy worldwide. Due to the severity and damage caused by the disease, it is very important to fore-tell the epidemic lifetime in order to take timely actions. Unfortunately, the lack of accurate information and unavailability of large amount of data at this stage make the task more difficult. In this paper, we used the available data from the mostly affected countries by COVID-19, (China, Iran, South Korea and Italy) and fit this with the SEIR type model in order to estimate the basic reproduction number R_0. We also discussed the development trend of the disease. Our model is quite accurate in predicting the current pattern of the infected population. We also performed sensitivity analysis on all the parameters used that are affecting the value of R0. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-28T09:19:04Z No. of bitstreams: 1 ijimai_6_2_1.pdf: 642181 bytes, checksum: 767b7c1683e6d15fc221c4904e39ffd5 (MD5); Made available in DSpace on 2022-03-28T09:19:04Z (GMT). No. of bitstreams: 1 ijimai_6_2_1.pdf: 642181 bytes, checksum: 767b7c1683e6d15fc221c4904e39ffd5 (MD5)

Kamel, Salah Jurado, Francisco Sultan, Hamdy Menesy, Ahmed https://reunir.unir.net/handle/123456789/12731 2022-03-28T09:00:58Z

Tree Growth Algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cell Models Kamel, Salah; Jurado, Francisco; Sultan, Hamdy; Menesy, Ahmed Demonstrating an accurate mathematical model is a mandatory issue for realistic simulation, optimization and performance evaluation of proton exchange membrane fuel cells (PEMFCs). The main goal of this study is to demonstrate a precise mathematical model of PEMFCs through estimating the optimal values of the unknown parameters of these cells. In this paper, an efficient optimization technique, namely, Tree Growth Algorithm (TGA) is applied for extracting the optimal parameters of different PEMFC stacks. The total of the squared deviations (TSD) between the experimentally measured data and the estimated ones is adopted as the objective function. The effectiveness of the developed parameter identification algorithm is validated through four case studies of commercial PEMFC stacks under various operating conditions. Moreover, comprehensive comparisons with other optimization algorithms under the same study cases are demonstrated. Statistical analysis is presented to evaluate the accuracy and reliability of the developed algorithm in solving the studied optimization problem. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-28T09:00:58Z No. of bitstreams: 1 ijimai_6_2_11.pdf: 1414303 bytes, checksum: 346adb416c4a4a7d1eacf7846db92438 (MD5); Made available in DSpace on 2022-03-28T09:00:58Z (GMT). No. of bitstreams: 1 ijimai_6_2_11.pdf: 1414303 bytes, checksum: 346adb416c4a4a7d1eacf7846db92438 (MD5)

Bobadilla, Jesús Gutiérrez, Abraham Alonso, Santiago Hurtado, Remigio https://reunir.unir.net/handle/123456789/12730 2022-03-28T08:37:43Z

A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups Bobadilla, Jesús; Gutiérrez, Abraham; Alonso, Santiago; Hurtado, Remigio In the collaborative filtering recommender systems (CFRS) field, recommendation to group of users is mainly focused on stablished, occasional or random groups. These groups have a little number of users: relatives, friends, colleagues, etc. Our proposal deals with large numbers of automatically detected groups. Marketing and electronic commerce are typical targets of large homogenous groups. Large groups present a major difficulty in terms of automatically achieving homogeneity, equilibrated size and accurate recommendations. We provide a method that combines diverse machine learning algorithms in an original way: homogeneous groups are detected by means of a clustering based on hidden factors instead of ratings. Predictions are made using a virtual user model, and virtual users are obtained by performing a hidden factors aggregation. Additionally, this paper selects the most appropriate dimensionality reduction for the explained RS aim. We conduct a set of experiments to catch the maximum cumulative deviation of the ratings information. Results show an improvement on recommendations made to large homogeneous groups. It is also shown the desirability of designing specific methods and algorithms to deal with automatically detected groups. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-28T08:37:43Z No. of bitstreams: 1 ijimai_6_2_10.pdf: 1543399 bytes, checksum: 2cb0399275af9245af7cd3074d62a74a (MD5); Made available in DSpace on 2022-03-28T08:37:43Z (GMT). No. of bitstreams: 1 ijimai_6_2_10.pdf: 1543399 bytes, checksum: 2cb0399275af9245af7cd3074d62a74a (MD5)

Núñez-Valdez, Edward Solanki, Vijender Kumar Balakrishna, Sivadi Thirumaran, M https://reunir.unir.net/handle/123456789/12729 2022-03-28T08:10:35Z

Incremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data Núñez-Valdez, Edward; Solanki, Vijender Kumar; Balakrishna, Sivadi; Thirumaran, M In the Internet of Things (IoT), Cyber-Physical Systems (CPS), and sensor technologies huge and variety of streaming sensor data is generated. The unification of streaming sensor data is a challenging problem. Moreover, the huge amount of raw data has implied the insufficiency of manual and semi-automatic annotation and leads to an increase of the research of automatic semantic annotation. However, many of the existing semantic annotation mechanisms require many joint conditions that could generate redundant processing of transitional results for annotating the sensor data using SPARQL queries. In this paper, we present an Incremental Clustering Driven Automatic Annotation for IoT Streaming Data (IHC-AA-IoTSD) using SPARQL to improve the annotation efficiency. The processes and corresponding algorithms of the incremental hierarchical clustering driven automatic annotation mechanism are presented in detail, including data classification, incremental hierarchical clustering, querying the extracted data, semantic data annotation, and semantic data integration. The IHCAA-IoTSD has been implemented and experimented on three healthcare datasets and compared with leading approaches namely- Agent-based Text Labelling and Automatic Selection (ATLAS), Fuzzy-based Automatic Semantic Annotation Method (FBASAM), and an Ontology-based Semantic Annotation Approach (OBSAA), yielding encouraging results with Accuracy of 86.67%, Precision of 87.36%, Recall of 85.48%, and F-score of 85.92% at 100k triple data. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-28T08:10:35Z No. of bitstreams: 1 ijimai_6_2_7_0.pdf: 1930171 bytes, checksum: fd01fa306746a128013500d6eadbf064 (MD5); Made available in DSpace on 2022-03-28T08:10:35Z (GMT). No. of bitstreams: 1 ijimai_6_2_7_0.pdf: 1930171 bytes, checksum: fd01fa306746a128013500d6eadbf064 (MD5)

Shikha, B Gitanjali, P Kumar, D. Pawan https://reunir.unir.net/handle/123456789/12728 2022-03-28T08:04:31Z

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System Shikha, B; Gitanjali, P; Kumar, D. Pawan The process of searching, indexing and retrieving images from a massive database is a challenging task and the solution to these problems is an efficient image retrieval system. In this paper, a unique hybrid Content-based image retrieval system is proposed where different attributes of an image like texture, color and shape are extracted by using Gray level co-occurrence matrix (GLCM), color moment and various region props procedure respectively. A hybrid feature matrix or vector (HFV) is formed by an integration of feature vectors belonging to three individual visual attributes. This HFV is given as an input to an Extreme learning machine (ELM) classifier which is based on a solitary hidden layer of neurons and also is a type of feed-forward neural system. ELM performs efficient class prediction of the query image based on the pre-trained data. Lastly, to capture the high level human semantic information, Relevance feedback (RF) is utilized to retrain or reformulate the training of ELM. The advantage of the proposed system is that a combination of an ELM-RF framework leads to an evolution of a modified learning and intelligent classification system. To measure the efficiency of the proposed system, various parameters like Precision, Recall and Accuracy are evaluated. Average precision of 93.05%, 81.03%, 75.8% and 90.14% is obtained respectively on Corel-1K, Corel-5K, Corel-10K and GHIM-10 benchmark datasets. The experimental analysis portrays that the implemented technique outmatches many state-of-the-art related approaches depicting varied hybrid CBIR system. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-28T08:04:31Z No. of bitstreams: 1 ijimai_6_2_3.pdf: 1610842 bytes, checksum: 966a7ce3d029e4a6814811094f4f3b41 (MD5); Made available in DSpace on 2022-03-28T08:04:31Z (GMT). No. of bitstreams: 1 ijimai_6_2_3.pdf: 1610842 bytes, checksum: 966a7ce3d029e4a6814811094f4f3b41 (MD5)

Saleem, Nasir Khattak, Muhammad Irfan Verdú, Elena https://reunir.unir.net/handle/123456789/12727 2022-05-19T06:46:13Z

On Improvement of Speech Intelligibility and Quality: A Survey of Unsupervised Single Channel Speech Enhancement Algorithms Saleem, Nasir; Khattak, Muhammad Irfan; Verdú, Elena Many forms of human communication exist; for instance, text and nonverbal based. Speech is, however, the most powerful and dexterous form for the humans. Speech signals enable humans to communicate and this usefulness of the speech signals has led to a variety of speech processing applications. Successful use of these applications is, however, significantly aggravated in presence of the background noise distortions. These noise signals overlap and mask the target speech signals. To deal with these overlapping background noise distortions, a speech enhancement algorithm at front end is crucial in order to make noisy speech intelligible and pleasant. Speech enhancement has become a very important research and engineering problem for the last couple of decades. In this paper, we present an all-inclusive survey on unsupervised single-channel speech enhancement (U-SCSE) algorithms. A taxonomy based review of the U-SCSE algorithms is presented and the associated studies regarding improving the intelligibility and quality are outlined. The studies on the speech enhancement algorithms in unsupervised perspective are presented. Objective experiments have been performed to evaluate the potential of the U-SCSE algorithms in terms of improving the speech intelligibility and quality. It is found that unsupervised speech enhancement improves the speech quality but the speech intelligibility improvement is deprived. To finish, several research problems are identified that require further research. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2022-03-28T07:57:47Z No. of bitstreams: 1 ijimai_6_2_9.pdf: 775025 bytes, checksum: 8c9cc953150755cb001aceebac2501f5 (MD5); Made available in DSpace on 2022-03-28T07:57:47Z (GMT). No. of bitstreams: 1 ijimai_6_2_9.pdf: 775025 bytes, checksum: 8c9cc953150755cb001aceebac2501f5 (MD5)