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Learning Topology: Bridging Computational Topology and Machine Learning 25th

Learning Topology: Bridging Computational Topology and Machi...

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25th international conference on Pattern Recognition Workshops, ICPR 2020

作者： Moroni, Davide Pascali, Maria Antonietta Institute of Information Science and Technologies National Research Council of Italy Via Moruzzi 1 Pisa56124 Italy

ISBN: (纸本)9783030688202

Topology is a classical branch of mathematics, born essentially from Euler’s studies in the XVII century, which deals with the abstract notion of shape and geometry. Last decades were characterised by a renewed interest in topology and topology-based tools, due to the birth of computational topology and Topological data Analysis (TDA). A large and novel family of methods and algorithms computing topological features and descriptors (e.g. persistent homology) have proved to be effective tools for the analysis of graphs, 3d objects, 2D images, and even heterogeneous datasets. This survey is intended to be a concise but complete compendium that, offering the essential basic references, allows you to orient yourself among the recent advances in TDA and its applications, with an eye to those related to machine learning and deep learning. © 2021, Springer Nature Switzerland AG.

关键词： Topology

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Distributed architecture for fake news detection 13th

Distributed architecture for fake news detection

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13th international conference on Computational Intelligence in Security for Information Systems, CISIS 2020

作者： Kozik, Rafal Choraś, Michal Kula, Sebastian Pawlicki, Marek UTP University of Science and Technology Kaliskiego 7 Bydgoszcz85-976 Poland Kazimierz Wielki University UKW Bydgoszcz Bydgoszcz Poland

ISBN: (纸本)9783030578046

Countering the fake news phenomenon has become one of the most important challenges for democratic societies, governments and non-profit organizations, as well as for the researchers coming from several domains. This is not a local problem, and demands a holistic approach to analyzing heterogeneous data and storing the results. The major contribution of this paper is the proposition of an innovative distributed architecture to tackle the above-mentioned problems. The architecture uses state-of-the-art technologies with focus on efficiency, scalability and also openness, so that community-created components and analyzers could be added. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Deep learning

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A Parallel Global TFIDF Feature Selection Using Hadoop for Big data Text Classification 1st

A Parallel Global TFIDF Feature Selection Using Hadoop for B...

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1st international conference of Advanced computing and Informatics, ICACIN 2020

作者： Amazal, Houda Ramdani, Mohammed Kissi, Mohamed Computer Science Laboratory Faculty of Sciences and Technologies Hassan II University of Casablanca BP 146 Mohammedia20650 Morocco

ISBN: (纸本)9789811560477

Feature selection (FS) is a fundamental process in machine learning. This process aims to remove irrelevant features, reduce the dimensionality of the feature space, and increase the accuracy of classification algorithms. For this, many feature selection techniques were proposed through several studies. However, as big data is still coming, the classical feature selection methods are enabled to store or process efficiently large datasets. Therefore, this work aims to improve the well-known term weighting method term frequency inverse document frequency (TFIDF) using MapReduce programming model on Hadoop framework. Our proposed approach, we called global TFIDF (GTFIDF), aims to introduce a simple and efficient parallel feature selection method which addresses the drawbacks of the classical TFIDF. The proposed approach includes two weighting schemes based on averaging and maximizing the GTFIDF. Experimental results on two well-known datasets prove that GTFIDF improves the performance of classification in term of the Micro-F1 and Macro-F1 measures. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

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Agent Based Queue Aware Scheduling for Distributed Multicore System

Agent Based Queue Aware Scheduling for Distributed Multicore...

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Recent advances and Innovations in Engineering (ICRAIE)

作者： G. Muneeswari Reeja S R School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh

In the current world of big data analytics and huge processing request from the client server system the service requirement from the computing cores has been growing exponentially. In this paper we proposed a novel idea wherein the agent will check the ready queue length dynamically and based on the threshold value comparison further scheduling has been done. There are three different variants of scheduling and load balancing are identified with respect to the queue length. Always the queue length is directly proportional to the number of tasks given to a particular node in the distributed system. Compared to the traditional scheduling algorithms, our approach increases the cores' throughput by 2.5%. In addition, we calculated that as compared to the preceding approaches, the typical hold time for work in the ready state has decreased to 1.25%. The primary benefit of this endeavor is when multiple jobs are submitted to the server then the proposed algorithm speed up the process of getting the jobs executed at a faster rate.

关键词： Technological innovation Scheduling algorithms Multicore processing Linux Systems architecture Load management Throughput

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Cultivate Smart and Healthy Ageing 9th

Cultivate Smart and Healthy Ageing

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9th EAI international conference on IoT technologies for HealthCare, HealthyIoT 2022

作者： Andreou, Andreas Mavromoustakis, Constandinos X. Markakis, Evangelos Mastorakis, George Batalla, Jordi Mongay Pallis, Evangelos Dobre, Ciprian Department of Computer Science University of Nicosia and University of Nicosia Research Foundation Nicosia Cyprus Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece Department of Management Science and Technology Hellenic Mediterranean University Agios Nikolaos Crete Greece Warsaw University of Technology Warsaw Poland Department of Industrial Design and Production Engineering School of Engineering University of West Attica Athens Greece National Institute for Research and Development in Informatics Bucharest Romania

ISBN: (纸本)9783031286629

The essential criterion for developing an intelligent age-friendly environment is evaluating and monitoring the provision of services. Our research aims to allow the deployment of a broad range of digital healthcare solutions interconnected in an IoT network ecosystem. Through their interoperability, we will be able to cultivate age-friendly smart homes for older adults. Thus, we will enable interpreted living and wellbeing. The intelligent integrations of digital solutions will allow the acquisition and evaluation of health-related data. Standardization, interoperability and scalability of the integration will increase efficiency in healthcare, improving older adults’ Quality of Life (QoL). Also, interpreting their needs will allow key stakeholders to optimize the Quality of Service (QoS). The proposed Internet of Health (IoH) framework will encapsulate sustainable and affordable innovative solutions and their benefits for a comfortable, meaningful, and independent life in an intelligent environment. Therefore, the frame mediated by edge computing, in-home and community settings will be able to interact with healthcare networks contributing to hospitalizations and institutional care. © 2023, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Internet of things

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Power-Efficient Secured Hardware Design of AES Algorithm on High Performance FPGA

Power-Efficient Secured Hardware Design of AES Algorithm on ...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Keshav Kumar Vijay Singh Gaurav Mishra B Ravindra Babu. Nandita Tripathi Pramod Kumar University Institute of Computing Chandigarh University Punjab India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Amity School of Engineering & Tchnology Amity University Uttar Pradesh Graeter Noida Uttar Pradesh India CSE Department Adama Science and Technology University Adama Ethiopia the Institution AIPS Amity University Greater Noida Uttar Pradesh India Department of ECE Lloyd Institute of Engineering and Technology Greater Noida India

With the expansion and growth of industries, the two major issues exist that affect both civilization and the environment. Technology development has made it more difficult to communicate and transmit data over secure channels. The use of Green Communication (GC) technology and energy-efficient parts can lessen the lack of electricity and energy. The usage of various technologies under one framework is the main topic of this study. In this article, a hardware implementation of the Advanced Encryption Standard (AES) algorithm is shown. Field Programmable Gate Array (FPGA) devices are taken into consideration for hardware implementations. The power calculation of the AES algorithm is computed for several clock speeds of the Spartan-7 FPGA, and the results are examined using VIVADO tool.

关键词： Performance evaluation Industries Green products Logic gates Hardware Encryption Informatics

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Open data in Higher Education - A Systematic Literature Review 2nd

Open Data in Higher Education - A Systematic Literature Revi...

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2nd international conference on advances in Emerging Trends and technologies, ICAETT 2020

作者： Rodriguez-F, Ivonne E. Arcos-Medina, Gloria Pástor, Danilo Oñate, Alejandra Gómez, Omar S. Escuela Superior Politécnica de Chimborazo Riobamba060155 Ecuador GrIISoft Research Group – Escuela Superior Politécnica de Chimborazo Riobamba060155 Ecuador Universidad Nacional de Colombia Medellín050034 Colombia

ISBN: (纸本)9783030636647

The open data (OD) initiative fosters open access to public information in an organization. It is a relatively new but very significant and potentially powerful approach that starts being adopted in different organizations. In the context of higher education, the adoption of this initiative starts being reported in the literature. In order to get a better understanding of open data (OD) in higher education, we conducted a systematic literature review. From a sample of 595 papers, we identified and selected 25 relevant papers for the review. Our findings suggest that open science, open government, and open university are main reasons to adopt OD. Models, frameworks, methods, and standards about OD have been adopted in higher education context. Different technological tools have been used for: data modeling, RDF data generation, data publishing, linking, and exploitation. However, some issues have been reported in the moment of adopting OD initiatives, mainly related to intellectual property, standardization, legal and operation aspects. Finally, the largest number of OD initiatives reported in the context of higher educations have been applied in European countries. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Open data

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Flower Pollination Algorithm in Optimization of Interval Type-2 Fuzzy for Telemedical Problem 1

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11th international conference on Innovations in Bio-Inspired computing and Applications, IBICA 2020 and 10th World Congress on Information and Communication technologies, WICT 2020

作者： Umoh, Uduak Abayomi, Abdultaofeek Udoh, Samuel Abdulazeez, Alimot Department of Computer Science University of Uyo PMB 1017 Uyo Akwa Ibom State Nigeria Department of Information and Communication Technology Mangosuthu University of Technology P.O. Box 12363 Jacobs Durban4026 South Africa

ISBN: (数字)9783030736033

ISBN: (纸本)9783030736026

Determining the best parameters of the membership functions (MFs) in developing interval type-2 fuzzy logic controller (IT2FLC) is always difficult and complex because of the presence of high level of uncertainty and imprecision. Flower Pollination Algorithm (FPA) is a nature-inspired global optimization technique that imitates the pollination behavior of flowering plants, which avoids trapping in local optimum solution. This paper describes the optimization of IT2FLC using FPA for telemedical problem and applies to predict shock level in cardiac patients. The optimized IT2FLC is used to handle the uncertainties inherent in the membership functions (MFs) of the type-2 using cardiac patients’ data for remote vital signs monitoring. The parameters of the MFs of the IT2FLC had not been previously optimized as this study considers applying Flower Pollination Algorithm (FPA) to optimize interval Footprint of Uncertainty (FOU) MFs of IT2FLS. We use the FPA to find the best MFs parameters of the IT2FL and the choice of FPA is because of the linear nature of the algorithm which gives attractive performances when compared to state-of-the-art algorithms. The empirical comparison is made on the designed system using cardiac patients’ health datasets obtained both from real-time and clinical scenarios. Simulation results indicate that FPA-based interval type-2 fuzzy algorithm outperforms those results of a single IT2FL algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Membership functions

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Deep Learning and Mosaic algorithm: An efficient combination for single cell segmentation

Deep Learning and Mosaic algorithm: An efficient combination...

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IEEE international conference on Research, Innovation and Vision for the Future

作者： Khang Luu My Nguyen Toai Ton Thang Pham Thanh Do Information Technology HUFLIT Ho Chi Minh Vietnam Computer Science DEAKIN Ho Chi Minh Vietnam

ISBN: (纸本)9781665461672

In biomedical industry, Deep Learning is considered one of the most effective tools to achieve high performance, especially in image segmentation [1]. In order to get high credibility, Deep Learning requires a lot of data to learn its pattern, while the resources of biomedical images are relatively small compared to other fields. To be able to completely and accurately segment individual cells in images is difficult due to the diversity of cell shapes, unequal frequency of cells in images, and the class distribution being extremely imbalanced. Cellpose model [2] which specializes in biomedical image segmentation was applied to perform our task. We trained our model on an open-source dataset called Sartorius, this dataset includes 606 images for training and 240 images for evaluating. The number of images is small and the data are also naturally imbalanced among the object classes. To deal with these problems, some data augmentation techniques and our customized Mosaic algorithm that is suitable for the dataset were used. The auxiliary model, Size Model was also trained which supports Cellpose in the inference phase. Our method overcomes these Sartorius's difficulties and also makes prediction more reliable. On the test set, our model achieves an AP score of 31.3%, compared with 25.3% of Mark Wijkhuizen who trained Mask RCNN [3] with backbone EfficientNetV2 [4] on this dataset.

关键词： Deep learning Training Image segmentation Shape Biological system modeling Prediction algorithms Inference algorithms

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A Novel Approach for Error Analysis in Classified Big data in Health Care

A Novel Approach for Error Analysis in Classified Big Data i...

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international conference on Artificial Intelligence and data Engineering, AIDE 2019

作者： Kavitha, S. Nayak, Mahesh S. Hanumanthappa, M. Dayananda Sagar Institutions Bangalore India Research and Development Centre Bharathiar University Coimbatore India Department of Computer Science and Applications Bangalore University Bangalore India

ISBN: (纸本)9789811535130

The healthcare industry has observed an abundant development ensuring the growth of novel computer technologies and that moved this zone to create more medicinal information, which brought manifold arenas of research. It has encouraged scholars to relate the entire mechanical revolutions such as enormous information analysis, extrapolative analysis, instrument erudition and erudition procedures to mine valuable information as well as assist in resolutions construction. Many exertions are not only completed to manage the outburst of medicinal information but also to attain suitable data from it on the other hand. Due to the possibilities of prognostic analysis in immense information, and the usage of mechanism erudition procedures, forecasting imminent will not be a challenging chore, particularly for medication as forecasting illnesses as well as antedating the treatment suited imaginable. This paper brings a map of the development of enormous information in healthcare arena, and a knowledge procedure is related based on medicinal information. The aim is to envisage enduring kidney ailments by employing decision tree (B4.8) procedure. © 2021, Springer Nature Singapore Pte Ltd.

关键词： data mining

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