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1
Maruta, Tatsuya. "Construction of Optimal Linear Codes by Geometric Puncturing." Serdica Journal of Computing 7, no. 1 (2013): 73–80. http://dx.doi.org/10.55630/sjc.2013.7.73-80.
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Ghorbani, Modjtaba. "Remarks on the Balaban Index." Serdica Journal of Computing 7, no. 1 (2013): 25–34. http://dx.doi.org/10.55630/sjc.2013.7.25-34.
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In this paper we compute some bounds of the Balaban index and then by means of group action we compute the Balaban index of vertex transitive graphs. ACM Computing Classification System (1998): G.2.2 , F.2.2.
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Von Collani, Elart. "Five Turning Points in the Historical Progress of Statistics - My Personal Vision." Serdica Journal of Computing 8, no. 3 (2015): 199–226. http://dx.doi.org/10.55630/sjc.2014.8.199-226.
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Statistics has penetrated almost all branches of science and allareas of human endeavor. At the same time, statistics is not only misunderstood, misused and abused to a frightening extent, but it is also oftenmuch disliked by students in colleges and universities. This lecture discusses/covers/addresses the historical development of statistics, aiming at identifying the most important turning points that led to the present stateof statistics and at answering the questions “What went wrong with statistics?” and “What to do next?”.ACM Computing Classification System (1998): A.0, A.m, G.3, K.3.2.
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Bouyukliev, Iliya, and Mariya Dzhumalieva-Stoeva. "Representing Equivalence Problems for Combinatorial Objects." Serdica Journal of Computing 8, no. 4 (2015): 327–54. http://dx.doi.org/10.55630/sjc.2014.8.327-354.
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Methods for representing equivalence problems of various combinatorial objectsas graphs or binary matrices are considered. Such representations can be used for isomorphism testing in classification or generation algorithms. Often it is easier to consider a graph or a binary matrix isomorphism problem than to implement heavy algorithms depending especially on particular combinatorialobjects. Moreover, there already exist well tested algorithms for the graph isomorphismproblem (nauty) and the binary matrix isomorphism problem as well (Q-Extension).ACM Computing Classification System (1998): F.2.1, G.4.
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López-ibáñez, Manuel, Juergen Branke, and Luís Paquete. "Reproducibility in Evolutionary Computation." ACM Transactions on Evolutionary Learning and Optimization 1, no. 4 (2021): 1–21. http://dx.doi.org/10.1145/3466624.
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Experimental studies are prevalent in Evolutionary Computation ( EC ), and concerns about the reproducibility and replicability of such studies have increased in recent times, reflecting similar concerns in other scientific fields. In this article, we discuss, within the context of EC, the different types of reproducibility and suggest a classification that refines the badge system of the Association of Computing Machinery ( ACM ) adopted by ACM Transactions on Evolutionary Learning and Optimization ( TELO ). We identify cultural and technical obstacles to reproducibility in the EC field. Finally, we provide guidelines and suggest tools that may help to overcome some of these reproducibility obstacles.
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Topalova, Svetlana, and Stela Zhelezova. "Orthogonal Resolutions and Latin Squares." Serdica Journal of Computing 7, no. 1 (2013): 13–24. http://dx.doi.org/10.55630/sjc.2013.7.13-24.
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Resolutions which are orthogonal to at least one other resolution (RORs) and sets of m mutually orthogonal resolutions (m-MORs) of 2-(v, k, λ) designs are considered. A dependence of the number of nonisomorphic RORs and m-MORs of multiple designs on the number of inequivalent sets of v/k − 1 mutually orthogonal latin squares (MOLS) of size m is obtained. ACM Computing Classification System (1998): G.2.1.
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Dobrinkova, Nina. "An Overview of Modelling Bulgarian Wildland Fire Behaviour by Application of a Mathematical Game Method and WRF-Fire Models." Serdica Journal of Computing 6, no. 4 (2013): 451–66. http://dx.doi.org/10.55630/sjc.2012.6.451-466.
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This paper presents the main achievements of the author’s PhD dissertation. The work is dedicated to mathematical and semi-empirical approaches applied to the case of Bulgarian wildland fires. After the introductory explanations, short information from every chapter is extracted to cover the main parts of the obtained results. The methods used are described in brief and main outcomes are listed. ACM Computing Classification System (1998): D.1.3, D.2.0, K.5.1.
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Sajid, Naseer Ahmed, Munir Ahmad, Muhammad Tanvir Afzal, and Atta-ur-Rahman. "Exploiting Papers’ Reference’s Section for Multi-Label Computer Science Research Papers’ Classification." Journal of Information & Knowledge Management 20, no. 01 (2021): 2150004. http://dx.doi.org/10.1142/s0219649221500040.
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The profusion of documents production at an exponential rate over the web has made it difficult for the scientific community to retrieve most relevant information against the query. The research community is busy in proposing innovative mechanisms to ensure the document retrieval in a flexible manner. The document classification is a core concept of information retrieval that classifies the documents into predefined categories. In scientific domain, classification of documents to predefined category (ies) is an important research problem and supports number of tasks such as information retrieval, finding experts, recommender systems, etc. In Computer Science, the Association for Computing Machinery (ACM) categorization system is commonly used for organizing research papers in the topical hierarchy defined by the ACM. Accurately assigning a research paper to a predefined category (ACM topic) is a difficult task especially when the paper belongs to multiple topics. In this paper, we exploit the reference section of a research paper to predict the topics of the paper. We have proposed a framework called Category-Based Category Identification (CBCI) for multi-label research papers classification. The proposed approach extracted references from training dataset and grouped them in a Topic-Reference (TR) pair such as TR {Topic, Reference}. The references of the focused paper are parsed and compared in the pair TR {Topic, Reference}. The approach collects the corresponding list of topics matched with the references in the said pair. We have evaluated our technique for two datasets that is Journal of Universal Computer Science (JUCS) and ACM. The proposed approach is able to predict the first node in the ACM topic (topic A to K) with 74% accuracy for both JUCS and ACM dataset for multi-label classification.
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Kratica, Jozef. "A Mixed Integer Quadratic Programming Model for the Low Autocorrelation Binary Sequence Problem." Serdica Journal of Computing 6, no. 4 (2013): 385–400. http://dx.doi.org/10.55630/sjc.2012.6.385-400.
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In this paper the low autocorrelation binary sequence problem (LABSP) is modeled as a mixed integer quadratic programming (MIQP) problem and proof of the model’s validity is given. Since the MIQP model is semidefinite, general optimization solvers can be used, and converge in a finite number of iterations. The experimental results show that IQP solvers, based on this MIQP formulation, are capable of optimally solving general/skew-symmetric LABSP instances of up to 30/51 elements in a moderate time. ACM Computing Classification System (1998): G.1.6, I.2.8.
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Hossein-Zadeh, S., A. Hamzeh, and A. R. Ashrafi. "The Wiener, Eccentric Connectivity and Zagreb Indices of the Hierarchical Product of Graphs." Serdica Journal of Computing 6, no. 4 (2013): 409–18. http://dx.doi.org/10.55630/sjc.2012.6.409-418.
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Let G1 = (V1, E1) and G2 = (V2, E2) be two graphs having a distinguished or root vertex, labeled 0. The hierarchical product G2 ⊓ G1 of G2 and G1 is a graph with vertex set V2 × V1. Two vertices y2y1 and x2x1 are adjacent if and only if y1x1 ∈ E1 and y2 = x2; or y2x2 ∈ E2 and y1 = x1 = 0. In this paper, the Wiener, eccentric connectivity and Zagreb indices of this new operation of graphs are computed. As an application, these topological indices for a class of alkanes are computed. ACM Computing Classification System (1998): G.2.2, G.2.3.
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Matic, Dragan. "A Variable Neighborhood Search Approach for Solving the Maximum Set Splitting Problem." Serdica Journal of Computing 6, no. 4 (2013): 369–84. http://dx.doi.org/10.55630/sjc.2012.6.369-384.
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This paper presents a Variable neighbourhood search (VNS) approach for solving the Maximum Set Splitting Problem (MSSP). The algorithm forms a system of neighborhoods based on changing the component for an increasing number of elements. An efficient local search procedure swaps the components of pairs of elements and yields a relatively short running time. Numerical experiments are performed on the instances known in the literature: minimum hitting set and Steiner triple systems. Computational results show that the proposed VNS achieves all optimal or best known solutions in short times. The experiments indicate that the VNS compares favorably with other methods previously used for solving the MSSP. ACM Computing Classification System (1998): I.2.8.
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Choe, Changil, and Hyonil Kim. "Noun Sense Disambiguation using Co-Occurrence Relation in Machine Translation." Serdica Journal of Computing 6, no. 4 (2013): 401–8. http://dx.doi.org/10.55630/sjc.2012.6.401-408.
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Word Sense Disambiguation, the process of identifying the meaning of a word in a sentence when the word has multiple meanings, is a critical problem of machine translation. It is generally very difficult to select the correct meaning of a word in a sentence, especially when the syntactical difference between the source and target language is big, e.g., English-Korean machine translation. To achieve a high level of accuracy of noun sense selection in machine translation, we introduced a statistical method based on co-occurrence relation of words in sentences and applied it to the English-Korean machine translator RyongNamSan. ACM Computing Classification System (1998): I.2.7.
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Shtrakov, Slavcho, and Jörg Koppitz. "Finite Symmetric Functions with Non-Trivial Arity Gap." Serdica Journal of Computing 6, no. 4 (2013): 419–36. http://dx.doi.org/10.55630/sjc.2012.6.419-436.
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Given an n-ary k-valued function f, gap(f) denotes the essential arity gap of f which is the minimal number of essential variables in f which become fictive when identifying any two distinct essential variables in f. In the present paper we study the properties of the symmetric function with non-trivial arity gap (2 ≤ gap(f)). We prove several results concerning decomposition of the symmetric functions with non-trivial arity gap with its minors or subfunctions. We show that all non-empty sets of essential variables in symmetric functions with non-trivial arity gap are separable. ACM Computing Classification System (1998): G.2.0.
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Stefanova, Eliza. "An Open Virtual World for Professional Development." Serdica Journal of Computing 7, no. 1 (2013): 81–100. http://dx.doi.org/10.55630/sjc.2013.7.81-100.
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The paper presents a study that focuses on the issue of sup-porting educational experts to choose the right combination of educational methodology and technology tools when designing training and learning programs. It is based on research in the field of adaptive intelligent e-learning systems. The object of study is the professional growth of teachers in technology and in particular that part of their qualification which is achieved by organizing targeted training of teachers. The article presents the process of creating and testing a system to support the decision on the design of training for teachers, leading to more effective implementation of technology in education and integration in diverse educational contexts. ACM Computing Classification System (1998): H.4.2, I.2.1, I.2, I.2.4, F.4.1.
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Muhammad Mulana Ramadan Purnama, I Nengah Sandi, Anak Agung Ngurah Gunawan, Gusti Ngurah Sutapa, I Gusti Agung Widagda, and Nyoman Wendri. "Classification of BI-RADS using convolutional neural network and effecientNet-B7." International Journal of Science and Research Archive 11, no. 1 (2024): 1022–28. http://dx.doi.org/10.30574/ijsra.2024.11.1.0164.
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From its development, image processing technology has become increasingly complex, such as detecting cancer in an image. In this context, the American College of Radiology (ACR) has developed a standard format and terminology called the breast imaging reporting and data system (BI-RADS). The aim of this research is to determine whether CNN can classify BI-RADS types (C1, C2, C3, C4), the best model, and the classification results of BI-RADS types on mammograms using CNN, and the best values for accuracy, sensitivity, specificity, and precision. Google Colaboratory and machine learning libraries were used in this research to create machine learning for BI-RADS classification. Then, a dataset from Kaggle titled "Mini-DDSM: Mammography-based Automatic Age Estimation" by Lekamlage et al., from the Association for Computing Machinery (ACM), Kyoto, Japan, was utilized. Based on the research, an accuracy of 85.9% was achieved for Category 1 classification. Category 2 classification attained an accuracy of 77.9%. The accuracy for Category 3 classification was 81.4%, and for Category 4, it reached 90.8%.
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Palejev, Dean. "A Bimodality Test in High Dimensions." Serdica Journal of Computing 6, no. 4 (2013): 437–50. http://dx.doi.org/10.55630/sjc.2012.6.437-450.
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We present a test for identifying clusters in high dimensional data based on the k-means algorithm when the null hypothesis is spherical normal. We show that projection techniques used for evaluating validity of clusters may be misleading for such data. In particular, we demonstrate that increasingly well-separated clusters are identified as the dimensionality increases, when no such clusters exist. Furthermore, in a case of true bimodality, increasing the dimensionality makes identifying the correct clusters more difficult. In addition to the original conservative test, we propose a practical test with the same asymptotic behavior that performs well for a moderate number of points and moderate dimensionality. ACM Computing Classification System (1998): I.5.3.
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Hol, Ana, Joan Richardson, Margaret Hamilton, and Jim McGovern. "Strengthening Undergraduate Information Systems Education in an Increasingly Complex Computing Disciplines Landscape." Communications of the Association for Information Systems 54, no. 1 (2024): 50–65. http://dx.doi.org/10.17705/1cais.05403.
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There are concerns that even at times when overall computing degree enrollments are increasing, IS bachelor’s degree programs and enrollments continue to decline. IS programs differ from other computing programs in that they include highly interrelated business and technology components. This inherent interdisciplinarity is the source of its value but also one of its challenges. This paper uses the Australian higher education sector as a case study to examine overall computing degree offerings including IS offerings using the ACM/AIS curriculum models and classification of computing disciplines. We find that IS program offerings are indeed trending down and that computing offerings are dominated by Computer Science and Information Technology degrees. IS is not widely present as a “base” discipline, nor is it providing a platform for the integration of new technologies, such as AI and Cyber Security into “business” settings. To strengthen UG IS programs and perceptions, we recommend that higher education providers develop structure and processes that support interdisciplinary UG IS program development and delivery and that professional bodies and curriculum models be revised to reflect and recognize the business outcome focus of IS. These actions, together with clearer messaging around the value of IS competencies, will improve the image of IS.
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Choe, Changil, Hyong-Chol O, and Song Han. "Approximate Model Checking of Real-Time Systems for Linear Duration Invariants." Serdica Journal of Computing 7, no. 1 (2013): 1–12. http://dx.doi.org/10.55630/sjc.2013.7.1-12.
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Real-time systems are usually modelled with timed automata and real-time requirements relating to the state durations of the system are often specifiable using Linear Duration Invariants, which is a decidable subclass of Duration Calculus formulas. Various algorithms have been developed to check timed automata or real-time automata for linear duration invariants, but each needs complicated preprocessing and exponential calculation. To the best of our knowledge, these algorithms have not been implemented. In this paper, we present an approximate model checking technique based on a genetic algorithm to check real-time automata for linear durration invariants in reasonable times. Genetic algorithm is a good optimization method when a problem needs massive computation and it works particularly well in our case because the fitness function which is derived from the linear duration invariant is linear. ACM Computing Classification System (1998): D.2.4, C.3.
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Kenderov, Petar S., and Toni K. Chehlarova. "Extending the Class of Mathematical Problems Solvable in School." Serdica Journal of Computing 9, no. 3-4 (2016): 191–206. http://dx.doi.org/10.55630/sjc.2015.9.191-206.
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The problems of practical importance which are considered in school today necessarily have to lead to a mathematical model that can be solved by school mathematics knowledge. This includes systems of equations of at most second degree, some simple trigonometry and/or some basic geometry.This restricts severely the class of such problems and conveys theimpression that mathematics is not applicable enough. We provide examples ofproblems related to practice which are difficult to solve by means of traditional schoolmathematics but are amenable for solving (at least with a certain precision) with the use of software systems dealing with mathematical problems.We also present the results of an experiment with such problems that were given toschool students participating in the second round of the competition “VIVA Mathematics with Computer”.ACM Computing Classification System (1998): K.3.1.
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Sredkov, Miloslav. "JSONYA/FN: Functional Computation in JSON." Serdica Journal of Computing 7, no. 1 (2013): 35–72. http://dx.doi.org/10.55630/sjc.2013.7.35-72.
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Functional programming has a lot to offer to the developers of global Internet-centric applications, but is often applicable only to a small part of the system or requires major architectural changes. The data model used for functional computation is often simply considered a consequence of the chosen programming style, although inappropriate choice of such model can make integration with imperative parts much harder. In this paper we do the opposite: we start from a data model based on JSON and then derive the functional approach from it. We outline the identified principles and present Jsonya/fn — a low-level functional language that is defined in and operates with the selected data model. We use several Jsonya/fn implementations and the architecture of a recently developed application to show that our approach can improve interoperability and can achieve additional reuse of representations and operations at relatively low cost. ACM Computing Classification System (1998): D.3.2, D.3.4.
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Baloian, Nelson, José A. Pino, Gustavo Zurita, Valeria Lobos-Ossandón, and Hermann Maurer. "Twenty-five Years of Journal of Universal Computer Science: A Bibliometric Overview." JUCS - Journal of Universal Computer Science 27, no. (1) (2021): 3–39. https://doi.org/10.3897/jucs.64594.
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The Journal of Universal Computer Science is a monthly peer-reviewed open-access scientific journal covering all aspects of computer science, launched in 1994, so becoming twenty-five years old in 2019. In order to celebrate its anniversary, this study presents a bibliometric overview of the leading publication and citation trends occurring in the journal. The aim of the work is to identify the most relevant authors, institutions, countries, and analyze their evolution through time. The article uses the Web of Science Core Collection citations and the ACM Computing Classification System in order to search for the bibliographic information. Our study also develops a graphical mapping of the bibliometric material by using the visualization of similarities (VOS) viewer. With this software, the work analyzes bibliographic coupling, citation and co-citation analysis, co-authorship, and co-occurrence of keywords. The results underline the significant growth of the journal through time and its international diversity having publications from countries all over the world and covering a wide range of categories which confirms the "universal" character of the journal.
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Naranjo-Zeledón, Luis, Jesús Peral, Antonio Ferrández, and Mario Chacón-Rivas. "A Systematic Mapping of Translation-Enabling Technologies for Sign Languages." Electronics 8, no. 9 (2019): 1047. http://dx.doi.org/10.3390/electronics8091047.
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Sign languages (SL) are the first language for most deaf people. Consequently, bidirectional communication among deaf and non-deaf people has always been a challenging issue. Sign language usage has increased due to inclusion policies and general public agreement, which must then become evident in information technologies, in the many facets that comprise sign language understanding and its computational treatment. In this study, we conduct a thorough systematic mapping of translation-enabling technologies for sign languages. This mapping has considered the most recommended guidelines for systematic reviews, i.e., those pertaining software engineering, since there is a need to account for interdisciplinary areas of accessibility, human computer interaction, natural language processing, and education, all of them part of ACM (Association for Computing Machinery) computing classification system directly related to software engineering. An ongoing development of a software tool called SYMPLE (SYstematic Mapping and Parallel Loading Engine) facilitated the querying and construction of a base set of candidate studies. A great diversity of topics has been studied over the last 25 years or so, but this systematic mapping allows for comfortable visualization of predominant areas, venues, top authors, and different measures of concentration and dispersion. The systematic review clearly shows a large number of classifications and subclassifications interspersed over time. This is an area of study in which there is much interest, with a basically steady level of scientific publications over the last decade, concentrated mainly in the European continent. The publications by country, nevertheless, usually favor their local sign language.
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Lestari, Ayu Puji, Maskur S.Kom, M.Kom., and Nur Hayatin. "Klasifikasi Teks Berbasis Ontologi Untuk Dokumen Tugas Akhir Berbahasa Indonesia." Jurnal Repositor 1, no. 2 (2019): 79. http://dx.doi.org/10.22219/repositor.v1i2.23.
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In this research, the classification of the final project document in Informatics Engineering UMM. The problem faced is the difficulty of finding relevant information and the difficulty of categorizing the TA documents according to areas of interest if it should be done manually. The purpose of this research is to get information based on abstract TA according to the category and make it easier to classify the TA document according to the field of interest. The categories used are the areas of interest in the study program: RPL, Computer Networking, Intelligent Game and Data Science. The data used TA documents as many as 500 data. The stage is to build and model ontology rules according to the data obtained with reference data of UMM Informatics Engineering Curriculum 2017 sourced from the Association for Computing Machinery (ACM) of the IEEE Computer Society. Ontology aims to classify the objects that exist in the collection without requiring the data train. To support the classification process used dao method. The dao method is used to calculate the similarity between documents from an existing node on the ontology by looking at the closest distance. Stage testing system using accuracy obtained result of 87%. This shows that ontologists are able to classify documents without using train data.
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Baloian, Nelson, José A. Pino, Gustavo Zurita, Valeria Lobos-Ossandón, and Hermann Maurer. "Twenty-five Years of Journal of Universal Computer Science: A Bibliometric Overview." JUCS - Journal of Universal Computer Science 27, no. 1 (2021): 3–39. http://dx.doi.org/10.3897/jucs.64594.
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The Journal of Universal Computer Science is a monthly peer-reviewed open-access scientific journal covering all aspects of computer science, launched in 1994, so becoming twenty-five years old in 2019. In order to celebrate its anniversary, this study presents a bibliometric overview of the leading publication and citation trends occurring in the journal. The aim of the work is to identify the most relevant authors, institutions, countries, and analyze their evolution through time. The article uses the Web of Science Core Collection citations and the ACM Computing Classification System in order to search for the bibliographic information. Our study also develops a graphical mapping of the bibliometric material by using the visualization of similarities (VOS) viewer. With this software, the work analyzes bibliographic coupling, citation and co-citation analysis, co-authorship, and co-occurrence of keywords. The results underline the significant growth of the journal through time and its international diversity having publications from countries all over the world and covering a wide range of categories which confirms the “universal” character of the journal.
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Lazazzera, Remo, Pablo Laguna, Eduardo Gil, and Guy Carrault. "Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove." Sensors 21, no. 23 (2021): 7976. http://dx.doi.org/10.3390/s21237976.
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The present paper proposes the design of a sleep monitoring platform. It consists of an entire sleep monitoring system based on a smart glove sensor called UpNEA worn during the night for signals acquisition, a mobile application, and a remote server called AeneA for cloud computing. UpNEA acquires a 3-axis accelerometer signal, a photoplethysmography (PPG), and a peripheral oxygen saturation (SpO2) signal from the index finger. Overnight recordings are sent from the hardware to a mobile application and then transferred to AeneA. After cloud computing, the results are shown in a web application, accessible for the user and the clinician. The AeneA sleep monitoring activity performs different tasks: sleep stages classification and oxygen desaturation assessment; heart rate and respiration rate estimation; tachycardia, bradycardia, atrial fibrillation, and premature ventricular contraction detection; and apnea and hypopnea identification and classification. The PPG breathing rate estimation algorithm showed an absolute median error of 0.5 breaths per minute for the 32 s window and 0.2 for the 64 s window. The apnea and hypopnea detection algorithm showed an accuracy (Acc) of 75.1%, by windowing the PPG in one-minute segments. The classification task revealed 92.6% Acc in separating central from obstructive apnea, 83.7% in separating central apnea from central hypopnea and 82.7% in separating obstructive apnea from obstructive hypopnea. The novelty of the integrated algorithms and the top-notch cloud computing products deployed, encourage the production of the proposed solution for home sleep monitoring.
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Akritas, Alkiviadis G., Gennadi I. Malaschonok, and Panagiotis S. Vigklas. "Subresultant Polynomial Remainder Sequences Obtained by Polynomial Divisions in Q[x] or in Z[x]." Serdica Journal of Computing 10, no. 3-4 (2017): 197–217. http://dx.doi.org/10.55630/sjc.2016.10.197-217.
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In this paper we present two new methods for computing thesubresultant polynomial remainder sequence (prs) of two polynomials f, g ∈ Z[x]. We are now able to also correctly compute the Euclidean and modifiedEuclidean prs of f, g by using either of the functions employed by ourmethods to compute the remainder polynomials.Another innovation is that we are able to obtain subresultant prs’s inZ[x] by employing the function rem(f, g, x) to compute the remainderpolynomials in [x]. This is achieved by our method subresultants_amv_q(f, g, x), which is somewhat slow due to the inherent higher cost of com-putations in the field of rationals.To improve in speed, our second method, subresultants_amv(f, g,x), computes the remainder polynomials in the ring Z[x] by employing thefunction rem_z(f, g, x); the time complexity and performance of thismethod are very competitive.Our methods are two different implementations of Theorem 1 (Section 3),which establishes a one-to-one correspondence between the Euclidean andmodified Euclidean prs of f, g, on one hand, and the subresultant prs of f, g,on the other.By contrast, if – as is currently the practice – the remainder polynomi-als are obtained by the pseudo-remainders function prem(f, g, x) 3 , thenonly subresultant prs’s are correctly computed. Euclidean and modified Eu-clidean prs’s generated by this function may cause confusion with the signsand conflict with Theorem 1.ACM Computing Classification System (1998): F.2.1, G.1.5, I.1.2.
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Nandi, Avishek, Paramartha Dutta, and Md Nasir. "Automatic Facial Expression Recognition System Using Shape-Information-Matrix (SIM)." International Journal of Natural Computing Research 9, no. 4 (2020): 34–51. http://dx.doi.org/10.4018/ijncr.2020100103.
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Automatic recognition of facial expressions and modeling of human expressions are very essential in the field of affective computing. The authors have introduced a novel geometric and texture-based method to extract the shapio-geometric features from an image computed by landmarking the geometric locations of facial components using the active appearance model (AAM). Expression-specific analysis of facial landmark points is carried out to select a set of landmark points for each expression to identify features for each specific expression. The shape information matrix (SIM) is constructed the set salient landmark points assign to an expression. Finally, the histogram-oriented gradients (HoG) of SIM are computed which is used for classification with multi-layer perceptron (MLP). The proposed method is tested and validated on four well-known benchmark databases, which are CK+, JAFFE, MMI, and MUG. The proposed system achieved 98.5%, 97.6%, 96.4%, and 97.0% accuracy in CK+, JAFFE, MMI, and MUG database, respectively.
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Qian, Cheng, Joao Alexandre Lobo Marques, and Auzuir Ripardo de Alexandria. "Real-time emotion recognition based on facial expressions using Artificial Intelligence techniques: A review and future directions." Multidisciplinary Reviews 8, no. 10 (2025): 2025328. https://doi.org/10.31893/multirev.2025328.
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In recent years, the real-time facial expression recognition system based on artificial intelligence technology has garnered significant attention from academia and industry. This paper presents a systematic literature review and bibliometric analysis to examine the latest publications in this field, summarizing the development and research significance of facial expression recognition technology and emphasizing its vital role in human-computer interaction and affective computing. The study used PRISMA to review 386 articles published from January 2019 to December 2023 in Web of Science, Scopus, IEEE Xplore, and ACM Digital Library. It encompasses covering various research methodologies, datasets, and application areas, as well as artificial intelligence technology, algorithms, and models. This review highlights advancements in Facial Expression Recognition, particularly the predominant use of databases such as FER2013 and CK+ while identifying Convolutional Neural Networks as the primary technique for real-time emotion classification. A quantitative analysis of research trends over the past five years indicates a shift toward keywords like transfer learning and applications in domains such as healthcare and the Internet of Things. Contemporary deep learning models, including CNNs, ResNet, and VGG, demonstrate impressive accuracy in classifying seven basic emotions, facilitating real-time applications across multiple fields. However, challenges such as overfitting, sensitivity to environmental factors, and the necessity for high-performance computing resources impede the broader deployment of these systems. These findings underscore the urgent need for further research to address these limitations and enhance the ethical application of FER technologies. Finally, based on the review and analysis results, this paper outlines future research directions for this technology, including multimodal information fusion, computational modelling, personalized emotion recognition, and interdisciplinary cooperation, thereby providing valuable references and inspiration for future works.
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Reddy*, M. Venkata Krishna, and Pradeep S. "Envision Foundational of Convolution Neural Network." International Journal of Innovative Technology and Exploring Engineering 10, no. 6 (2021): 54–60. http://dx.doi.org/10.35940/ijitee.f8804.0410621.
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1. Bilal, A. Jourabloo, M. Ye, X. Liu, and L. Ren. Do Convolutional Neural Networks Learn Class Hierarchy? IEEE Transactions on Visualization and Computer Graphics, 24(1):152–162, Jan. 2018. 2. M. Carney, B. Webster, I. Alvarado, K. Phillips, N. Howell, J. Griffith, J. Jongejan, A. Pitaru, and A. Chen. Teachable Machine: Approachable Web-Based Tool for Exploring Machine Learning Classification. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI ’20. ACM, Honolulu, HI, USA, 2020. 3. A. Karpathy. CS231n Convolutional Neural Networks for Visual Recognition, 2016 4. M. Kahng, N. Thorat, D. H. Chau, F. B. Viegas, and M. Wattenberg. GANLab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation. IEEE Transactions on Visualization and Computer Graphics, 25(1):310–320, Jan. 2019. 5. J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson. Understanding Neural Networks Through Deep Visualization. In ICML Deep Learning Workshop, 2015 6. M. Kahng, P. Y. Andrews, A. Kalro, and D. H. Chau. ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models. IEEE Transactions on Visualization and Computer Graphics, 24(1):88–97, Jan. 2018. 7. https://cs231n.github.io/convolutional-networks/ 8. https://www.analyticsvidhya.com/blog/2020/02/learn-imageclassification-cnn-convolutional-neural-networks-3-datasets/ 9. https://towardsdatascience.com/understanding-cnn-convolutionalneural- network-69fd626ee7d4 10. https://medium.com/@birdortyedi_23820/deep-learning-lab-episode-2- cifar- 10-631aea84f11e 11. J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, and T. Chen. Recent advances in convolutional neural networks. Pattern Recognition, 77:354–377, May 2018. 12. Hamid, Y., Shah, F.A. and Sugumaram, M. (2014), ―Wavelet neural network model for network intrusion detection system‖, International Journal of Information Technology, Vol. 11 No. 2, pp. 251-263 13. G Sreeram , S Pradeep, K SrinivasRao , B.Deevan Raju , Parveen Nikhat , ― Moving ridge neuronal espionage network simulation for reticulum invasion sensing‖. International Journal of Pervasive Computing and Communications.https://doi.org/10.1108/IJPCC-05- 2020-0036 14. E. Stevens, L. Antiga, and T. Viehmann. Deep Learning with PyTorch. O’Reilly Media, 2019. 15. J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson. Understanding Neural Networks Through Deep Visualization. In ICML Deep Learning Workshop, 2015. 16. Aman Dureja, Payal Pahwa, ―Analysis of Non-Linear Activation Functions for Classification Tasks Using Convolutional Neural Networks‖, Recent Advances in Computer Science , Vol 2, Issue 3, 2019 ,PP-156-161 17. https://missinglink.ai/guides/neural-network-concepts/7-types-neuralnetwork-activation-functions-right/
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Karthiga, B., Danalakshmi Durairaj, Nishad Nawaz, Thiruppathy Kesavan Venkatasamy, Gopi Ramasamy, and A. Hariharasudan. "Intelligent Intrusion Detection System for VANET Using Machine Learning and Deep Learning Approaches." Wireless Communications and Mobile Computing 2022 (October 13, 2022): 1–13. http://dx.doi.org/10.1155/2022/5069104.
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Detecting the attacks in Vehicular Ad hoc Network (VANET) system is very important to provide more secure and reliable communication between all vehicles in the system. In this article, an effective Intelligent Intrusion Detection System (IDS) is proposed using machine learning and deep learning approaches such as Adaptive Neuro Fuzzy Inference System (ANFIS) and Convolutional Neural Networks (CNN), respectively. The existing methods focus on detecting only the known attacks in VANET environment. This limitation is overcome by proposing the Intelligent IDS system using soft computing techniques. The proposed method consists of Known IDS (KIDS) and Unknown IDS (UIDS) modules, which detect both known attacks and unknown attacks. The KIDS module uses ANFIS classification module to detect the known malicious attacks, whereas the UIDS module uses a deep learning algorithm to detect the unknown attacks in VANET. Modified LeeNET (MLNET) architecture is proposed in this article to identify the type of unknown attacks. In this work, DoS attacks, Botnet attacks, PortScan attacks, and Brute Force attacks are detected using this hybrid learning algorithm. The proposed system obtains 96.9% of Pr, 98.3% of Se, 98.7% of Sp, and 98.6% of Acc and consumed 1.75 s for detecting the DoS attack on i-VANET dataset. The proposed system obtains 98.1% of Pr, 98.9% of Se, 98.1% of Sp, and 98.1% of Acc and consumed 0.95 s for detecting the Botnet attack. The proposed system obtains 98.7% of Pr, 99.1% of Se, 98.9% of Sp, and 99.2% of Acc and consumed 1.38 s for detecting the PortScan attack. The proposed system obtains 99.1of Pr, 97.8% of Se, 98.7% of Sp, and 98.5% of Acc and consumed 1.29 s for detecting the Brute Force attack. The developed methodology is tested on the real-time CIC-IDS 2017 dataset, and the experimental results are compared with other state-of-the-art methods.
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Mohammed, Mohammed Swaleh, and Prof Franklin Wabwoba. "Simplifying the Opaqueness and Dilemma of the Computing Disciplines for the Common Person in Society." International Journal of Research and Innovation in Applied Science VIII, no. XII (2023): 18–30. http://dx.doi.org/10.51584/ijrias.2023.81203.
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The computing fields have grown increasingly influential in modern times, yet their complexities and technical terms can prove problematic for the public to comprehend. This paper proposes to bridge this gap, introducing readers to the five major computing disciplines as per the Association of Computing Machinery (ACM), 2016: computer science, Information System, software engineering, computer engineering, and information technology. An additional more recent discipline which does not fall under the 5 ACM disciplines, Information Communication Technology was also introduced. The paper first examines the evolution of the Computing disciplines from the 1960s. Then the paper briefly discusses each discipline and how the disciplines overlap with each other through using secondary data materials. A section on the new and emerging computing technologies outlines the recognition status by the society. The interdisciplinary nature of the computing disciplines necessitated the need to simplify it. Thus, a table was done which summarized various works of various researchers who had researched on the computing disciplines.
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Mi, Yunlong, Pei Quan, Yong Shi, and Zongrun Wang. "Concept-cognitive computing system for dynamic classification." European Journal of Operational Research 301, no. 1 (2022): 287–99. http://dx.doi.org/10.1016/j.ejor.2021.11.003.
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Coulter, Neal. "ACM'S computing classification system reflects changing times." Communications of the ACM 40, no. 12 (1997): 111–12. http://dx.doi.org/10.1145/265563.265579.
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Rous, Bernard. "Major update to ACM's Computing Classification System." Communications of the ACM 55, no. 11 (2012): 12. http://dx.doi.org/10.1145/2366316.2366320.
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Cong, Yuyu, Weiyan Jiang, Zehua Dong, et al. "ACM-Assessor: An Artificial Intelligence System for Assessing Angle Closure Mechanisms in Ultrasound Biomicroscopy." Bioengineering 12, no. 4 (2025): 415. https://doi.org/10.3390/bioengineering12040415.
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Primary angle-closure glaucoma (PACG), characterized by angle closure (AC) with insidious and irreversible progression, requires precise assessment of AC mechanisms for accurate diagnosis and treatment. This study developed an artificial intelligence system, ACM-Assessor, to evaluate AC mechanisms in ultrasound biomicroscopy (UBM) images. A dataset of 8482 UBM images from 1160 patients was retrospectively collected. ACM-Assessor comprises models for pixel-to-physical spacing conversion, anterior chamber angle boundary segmentation, and scleral spur localization, along with three binary classification models to assess pupillary block (PB), thick peripheral iris (TPI), and anteriorly located ciliary body (ALCB). The integrated assessment model classifies AC mechanisms into pure PB, pure non-PB, multiple mechanisms (MM), and others. ACM-Assessor’s evaluation encompassed external testing (2266 images), human–machine competition and assisting beginners’ assessment (an independent test set of 436 images). ACM-Assessor achieved accuracies of 0.924 (PB), 0.925 (TPI), 0.947 (ALCB), and 0.839 (integrated assessment). In man–machine comparisons, the system’s accuracy was comparable to experts (p > 0.05). With model assistance, beginners’ accuracy improved by 0.117 for binary classification and 0.219 for integrated assessment. ACM-Assessor demonstrates expert-level accuracy and enhances beginners’ learning in UBM analysis.
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Vaikhanskaya, T. G., T. V. Kurushko, Yu A. Persianskikh, and L. N. Sivitskaya. "Atrial cardiomyopathy — a new concept with a long history." Russian Journal of Cardiology 25, no. 11 (2020): 3942. http://dx.doi.org/10.15829/29/1560-4071-2020-3942.
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Atrial cardiomyopathy (ACM) is a relatively common but clinically underestimated disorder, which is characterized by an increased atrial size and dysfunction. Previously, ACM was considered a primary disorder, but in 2016 this concept was revised by European Heart Rhythm Association (EHRA) working group with inclusion of secondary atrial remodeling. The EHRA document details aspects of atrial anatomy and pathophysiology, proposes definitions of ACM, histological classification, outlines the molecular mechanisms of atrial arrhythmia and the problems of personalized treatment and optimization of indications for catheter ablation.Practical application of the proposed ACM classification system, the clinical significance of novel ACM concept and the potential role of this information for a practitioner are presented in this article. Two clinical cases of ACM with “primary” (familial form of ACM due to NPPA gene mutation with primary defect in atrial structure and function) and “secondary” atrial remodeling (ACM caused by a longterm supraventricular tachyarrhythmias due to SCN1B gene mutation).
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Trim, Michelle. "Computing Must Pay Attention to Outcomes to Achieve Equity." ACM SIGCAS Computers and Society 51, no. 2 (2022): 8–9. http://dx.doi.org/10.1145/3585066.3585068.
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There is a push right now across ACM that is gathering momentum. This push is for increased attention to cultural competency [16] in the training of computing professionals. In fact, the CS202X: ACM / IEEE-CS / AAAI Computer Science Curricula Taskforce [5] has a knowledge area subcommittee devoted to SEP or Society, Ethics, and Professionalism [6]. This subcommittee is currently proposing a new set of student learning outcomes under the heading of Justice, Equity, Diversity, and Inclusion. These new learning foci include the identification of underlying assumptions and values built into system designs and the recognition and description of the impact of diversity and under-representation of identities in the computing profession. The emphasis on underlying values and on describing impacts is not accidental. As many have come to recognize, computing has a legacy of compartmentalizing social concerns [3] (and social costs), and this segregation of intellectual concerns is often facilitated by the distance between those who design [10] and those who experience the impacts of those designs. This new curricular push within ACM and IEEE is about equipping future computer science graduates with the cultural competencies that they need to connect the values informing local design practices to the public impacts of those designs.
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Huang, Jia-Yen, and Ke-Wei Tan. "An Extension-Based Classification System of Cloud Computing Patents." International Journal of Information Technology & Decision Making 19, no. 04 (2020): 1149–72. http://dx.doi.org/10.1142/s0219622020500248.
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Owing to the large number of professional glossaries and unknown patent classification, analysts usually fail to collect and analyze patents efficiently. One solution to this problem is to conduct patent analysis using a patent classification system. However, in a corpus such as cloud patents, many keywords are common among different classes, making it difficult to classify the unknown class documents using the machine learning techniques proposed by previous studies. To remedy this problem, this study aims to establish an efficient classification system with a special focus on features extraction and application of extension theory. We first propose a compound method to determine the features, and then, we propose an extension-based classification method to develop an efficient patent classification system. Using cloud computing patents as the database, the experimental results show that our proposed scheme can outperform the classification quality of the traditional classifiers.
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杨, 洁. "The Clinical Research Progress of Cerebral Palsy and MRI Classification System." Advances in Clinical Medicine 14, no. 04 (2024): 2753–58. http://dx.doi.org/10.12677/acm.2024.1441354.
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Malhotra, Neha, Manju Bala, and Vikram Puri. "Fault diagnosis in mobile computing using TwinSVM." Ingeniería Solidaria 18, no. 1 (2022): 1–28. http://dx.doi.org/10.16925/2357-6014.2022.01.11.
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Introduction: Mobile computing systems (MCS) comes up with the challenge of low communication bandwidth and energy due to the mobile nature of the network. These features sometimes may come up with the undesirable behaviour of the system that eventually affects the efficiency of the system.
 Problem: Fault tolerance in MCS will increase the efficiency of the system even in the presence of faults.
 Objective: The main objective of this work is the development of the Monitoring Framework and Fault Detection and Classification.
 Methodology: For the Node Monitoring and for the detection and classification of faults in the system a neighbourhood comparison-based technique has been proposed. The proposed framework uses Twin Support Vector Machine (TWSVM) algorithm has been applied to build classifier for fault classification in the mobile network.
 Results: The proposed system has been compared with the existing techniques and has been evaluated towards calculating the detection accuracy, latency, energy consumption, packet delivery ratio, false classification rate and false positive rate.
 Conclusion: The proposed framework performs better in terms of all the selected parameters.
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Noh, Sun-Kuk. "Deep Learning System for Recycled Clothing Classification Linked to Cloud and Edge Computing." Computational Intelligence and Neuroscience 2022 (November 3, 2022): 1–9. http://dx.doi.org/10.1155/2022/6854626.
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Recently, IT technologies related to the Fourth Industrial Revolution (4IR), such as artificial intelligence (AI), Internet of things (IoT), cloud computing, and edge computing have been studied. Although there are many used clothing occurrences with 61 trillion worn of clothing consumption per year in Korea, it is not properly collected due to the efficiency of the used clothing collection system, and the collected used clothing is not properly recycled due to insufficient recycling system, lack of skilled labor force, and health problems of workers. To solve this problem, this study proposes a deep learning clothing classification system (DLCCS) using cloud and edge computing. The system proposed is to classify clothing image data input from camera terminals installed in various clothing classification sites in various regions into two classes, as well as nine classes, by deep learning using convolution neural network (CNN). And the classification results are stored in the cloud through edge computing. The edge computing enables the analysis of the data of the Internet of Things (IoT) device on the edge of the network before transmitting it to the cloud. The performance evaluation parameters that are considered for the proposed research study are transmission velocity and latency. Proposed system can efficiently improve the process and automation in the classification and processing of recycled clothing in various places. It is also expected that the waste of clothing resources and health problems of clothing classification workers will be improved.
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Kim, Kwihoon, Joo-Hyung Lee, Hyun-Kyo Lim, Se-Won Oh, and Youn-Hee Han. "Deep RNN-based network traffic classification scheme in edge computing system." Computer Science and Information Systems, no. 00 (2021): 38. http://dx.doi.org/10.2298/csis200424038k.
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This paper proposes a deep recurrent neural network (RNN)-based traffic classification scheme (deep RNN-TCS) for classifying applications from traffic patterns in a hybrid edge computing and cloud computing architecture.We can also classify traffic from a cloud server, but there will be a time delay when packets transfer to the server. Therefore, the traffic classification is possible almost in realtime when it performed on edge computing nodes. However, training takes a lot of time and needs a lot of computing resources to learn traffic patterns. Therefore, it is efficient to perform training on cloud server and to perform serving on edge computing node. Here, a cloud server collects and stores output labels corresponding to the application packets. Then, it trains those data and generates inferred functions. An edge computation node receives the inferred functions and executes classification. Compared to deep packet inspection (DPI), which requires the periodic verification of existing signatures and updated application information (e.g., versions adding new features), the proposed scheme can classify the applications in an automated manner. Also, deep learning can automatically make classifiers for traffic classification when there is enough data. Specifically, input features and output labels are defined for classification as traffic packets and target applications, respectively, which are created as two-dimensional images. As our training data, traffic packets measured at Universitat Politecnica de Catalunya Barcelonatech were utilized. Accordingly, the proposed deep RNN-TCS is implemented using a deep long short-term memory system. Through extensive simulation-based experiments, it is verified that the proposed deep RNN-TCS achieves almost 5% improvement in accuracy (96% accuracy) while operating 500 times faster (elapsed time) compared to the conventional scheme.
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Li, Yong Xia, Guang Xia Xu, and Shuang Yan Liu. "Research on Dependable Level in Network Computing System." Applied Mechanics and Materials 513-517 (February 2014): 1105–8. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1105.
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The most weakness link in credible monitoring is that how to process multidimensional dynamic behavior data effectively. System behavior monitoring often needs to deal with different kinds of behavior data and those data can adopt status snapshot in multi-dimensional vector form to express. Obviously, data has strong useful knowledge information, which is regarded as a kind of classification ability. So we need to finish the mapping and classification between a variety of network behavior snapshot and dependable level. This paper introduces on network state snapshot owning the characteristics of high dimension, heterogeneous and dynamic and uses the theory of interval intuitionistic fuzzy to judge credible degree in the system and generate behavior quality trust level of nodes.
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Ma, Tinghuai, Sen Yan, Jin Wang, and Sungyoung Lee. "Privacy preserving in ubiquitous computing: Classification & hierarchy." Computer Science and Information Systems 8, no. 4 (2011): 1185–206. http://dx.doi.org/10.2298/csis110315055m.
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In this paper, we adopt the classification of personal information and hierarchy of services to build a privacy system, in which one communicates with each other via pipes with different security levels. In each level, one has the corresponding rights to access each other. The requesters are not able to be infringed based on the personal information that service obtains from service providers. Privacy system can decrease the interaction, while in other circumstance the system strengthens and enhances the privacy preserving. Thus we strike a balance between two goals of Ubiquitous Computing: interaction and privacy preserving.
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Gec, Sandi, Vlado Stankovski, Dejan Lavbič, and Petar Kochovski. "A Recommender System for Robust Smart Contract Template Classification." Sensors 23, no. 2 (2023): 639. http://dx.doi.org/10.3390/s23020639.
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IoT environments are becoming increasingly heterogeneous in terms of their distributions and included entities by collaboratively involving not only data centers known from Cloud computing but also the different types of third-party entities that can provide computing resources. To transparently provide such resources and facilitate trust between the involved entities, it is necessary to develop and implement smart contracts. However, when developing smart contracts, developers face many challenges and concerns, such as security, contracts’ correctness, a lack of documentation and/or design patterns, and others. To address this problem, we propose a new recommender system to facilitate the development and implementation of low-cost EVM-enabled smart contracts. The recommender system’s algorithm provides the smart contract developer with smart contract templates that match their requirements and that are relevant to the typology of the fog architecture. It mainly relies on OpenZeppelin, a modular, reusable, and secure smart contract library that we use when classifying the smart contracts. The evaluation results indicate that by using our solution, the smart contracts’ development times are overall reduced. Moreover, such smart contracts are sustainable for fog-computing IoT environments and applications in low-cost EVM-based ledgers. The recommender system has been successfully implemented in the ONTOCHAIN ecosystem, thus presenting its applicability.
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Nilashi, Mehrbakhsh, Othman Bin Ibrahim, Abbas Mardani, Ali Ahani, and Ahmad Jusoh. "A soft computing approach for diabetes disease classification." Health Informatics Journal 24, no. 4 (2016): 379–93. http://dx.doi.org/10.1177/1460458216675500.
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As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use expectation maximization, principal component analysis and support vector machine for clustering, noise removal and classification tasks, respectively. We also develop the proposed method for incremental situation by applying the incremental principal component analysis and incremental support vector machine for incremental learning of data. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction and reduces computation time in relation to the non-incremental approaches. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system.
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Nobles, Calvin. "Investigating Cloud Computing Misconfiguration Errors using the Human Factors Analysis and Classification System." Scientific Bulletin 27, no. 1 (2022): 59–66. http://dx.doi.org/10.2478/bsaft-2022-0007.
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Abstract As organizations raced to leverage cloud computing to capitalize on its economic and flexible computing capacity, misconfiguration errors ascended to unprecedented levels. One security expert noted observing over 230 million misconfigurations daily. Cloud misconfiguration errors are problematic as malicious actors continually pursue missteps to gain unauthorized access. The complexity of cloud computing coupled with increased cloud usage and the lack of cloud expertise result in misconfiguration errors which are human errors. Leveraging the human factors analysis and classification system (HFACS) provides a framework for identifying and preventing causal pathways for misconfiguration errors. In cloud computing, practitioners can explore the linkages of the hierarchical levels and decision-making at each level that influences and enable the misconfigurations. Human errors in cybersecurity are rampant, and HFACS could provide comprehensive analyses to highlight how errors, mistakes, and violations propagate misconfiguration errors – a current blind spot in cloud computing.
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Karanam, Madhavi, Krishna Chythanya Nagaraju, Gotham Sai P, SaiKiran Manasa S, and Pranay Krishna G. "Granite classification using machine learning and edge computing." F1000Research 11 (November 8, 2022): 1276. http://dx.doi.org/10.12688/f1000research.124057.1.
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Background: The outlook and the aura of any place are highly dependent on how a place is decorated and what materials are used in designing it. Granite is such a kind of rock which is vastly used for this purpose. Granite flooring and counters have a major influence on the interior d ́ecor which is essential to set the mood and ambience of a house. A system is needed to help the end users differentiate between granites, which enhance the grandeur of their house and also check the frauds of different color granite being sent by the merchant as compared to what was selected by the end user. Several models have been developed for this cause using CNN and other image processing techniques. However, a solution for this purpose must be precise and computationally efficient. Methods: For this purpose,researchers in this work developed a machine learning based granite classifier using Edge Computing and a website to help users in choosing which granite would go well with their d ́ecor is also built. The developed system consists of a color sensor [TCS3200] integrated with an ESP8266 board. The data pertaining to RGB contrasts of different rocks is acquired by using the color sensor from a dealership.This data is used to train a Machine Learning algorithm to classify the rock into different granite types from a granite dealer and yield the category prediction. Results: The proposed system yields a result of 94% accuracy when classified using Random Forest Algorithm. Conclusion: Thus, this system provides an upper hand for the end users in differentiating between different types of granites.
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Pratibha, Rana* Ms. Jyotsna Singh. "A REVIEW ON CLASSIFICATION OF EEG SIGNAL DATA." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 5 (2016): 188–93. https://doi.org/10.5281/zenodo.51018.
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A Brain computing system is a communication channel between the human or animal brain and external environment; it’s a collaboration in which brain controls a mechanical device as a natural part of its representation of the body. It is a type of communication which practically uses both software and hardware systems for the communication. It’s the type of system which provides a new way of communication between non-muscular channels with the external hardware. Basically Brain computing system is broadly divided into two major categories 1) EEG data signal based pattern recognition approach which actually train the particular brain mental stage and machine observe this pattern, on the behalf on this pattern machine further labels mental state of mind using the classification of pattern. 2) The apparent conditioning approach based on the self-recognition of the EEG signal response. In this paper we review some of the classification techniques for first type of brain computing interface.
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Prajapati, Mahesh A. "A Survey on Challenges and Solutions of Traffic Surveillance Systems in IoT-Enabled Eco System." International Scientific Journal of Engineering and Management 03, no. 04 (2024): 1–9. http://dx.doi.org/10.55041/isjem01595.
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Computer vision is crucial in the development of numerous technologies for traffic safety. Here we present survey paper which focuses on the basic idea of Traffic Surveillance techniques used for its detection. Now days we can see that manually traffic monitoring becomes very critical task. Several research projects have been carried out to convert this manual system to fully automated. It will help us to easily track a vehicle. A computer network's availability and smooth functioning are the main goals of network traffic monitoring. In this work, we explore a variety of traffic monitoring approaches, including congestion detection, traffic light control algorithms, cloud computing design for vehicle data classification, and fog computing design for vehicle data classification. Key Words: Internet-of-Things, Cloud computing, Fog computing, Smart traffic monitoring