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共 185 条记录，以下是21-30 订阅

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recent Research advances in Black and White Visual Cryptography Schemes 8th

Recent Research Advances in Black and White Visual Cryptogra...

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8th international conference on soft computing for Problem Solving (SocProS)

作者： Jisha, T. E. Monoth, Thomas Kannur Univ Dept Informat Technol Kannur 670567 Kerala India Kannur Univ Mary Matha Arts & Sci Coll Dept Comp Sci Wayanad 670645 Kerala India

ISBN: (纸本)9789811500350;9789811500343

Visual Cryptography (VC) is a type of image secret sharing scheme which decrypts an original secret image with Human Visual System (HVS). In this, the original image can be alienated into n shadows or shares and allocated to n participants;stacking any k shares reveals the secret image which ensures the security measures. In this paper, we examined the recent research advances in black and white VCSs. We reviewed the existing techniques and a comparative study of VC for binary images is presented. The study is performed with respect to different parameters and draws the current barriers related to the visual cryptography schemes.

关键词： Visual cryptography scheme Pixel expansion General access structure Basis matrices

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A Comparative Study of recent advances in Big Data Analytics in Vehicular Ad Hoc Networks

A Comparative Study of Recent Advances in Big Data Analytics...

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international Wireless Communications and Mobile computing conference, IWCMC

作者： Kawthar Zaraket Ismail Bennis Hassan Harb Ali Jaber Abedalhafid Abouaissa MERCEL Lab Doctoral School of Science and Technology Lebanese University Beirut Lebanon IRIMAS Universit&#x00E9 de Haute Alsace Mulhouse France

Big data is becoming a research focus in Intelligent Transportation Systems (ITS), which can be seen in many projects worldwide. Intelligent transportation systems will produce a large amount of data. The produced big data will profoundly impact the design and application of the ITS, which makes them safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper aims to provide a comparative study of some recent works in Big Data analytics for Vehicular Ad Hoc Networks. The study includes reviewing some frameworks that conduct big data analytics in ITS while discussing the data source and collection methods, data analytics methods, platforms and big data analytics application categories. The comparison and implementation of five recent works, with a focus on the data collection and application layers, are followed to pick up the best approach based on the Friedman test results.

关键词： Wireless communication Measurement Data analysis soft sensors Vehicular ad hoc networks Computer architecture Big Data

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Diagnosis, Treatment and Containment of Epilepsy Disease

Diagnosis, Treatment and Containment of Epilepsy Disease

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international conference on Parallel Distributed and Grid computing (PDGC)

作者： Shiv Ashish Dhondiyal Sushil Chandra Dimri Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9781665454025

The research work includes a thorough examination of the evolution of Epilepsy Disease, its symptoms, therapies, and progression, as well as algorithmic techniques for diagnosis. The majority of research on this condition is on identifying Epilepsy condition using EEG data and machine learning, and also on assessing the influence of medications on its development. recent advances show that selection of features and optimization computation are often used to extract a group of best-performing characteristics that deliver high accuracy rates while rejecting other comparable parameters. Several research gaps have also been found in this work that might be investigated further in the future, including (i) identification of initial biomarkers of epilepsy disease from various types of available datasets, using Machine Learning techniques is required. (ii) Need to design a machine learning classifier based on the various data sources and the user inputs (symptoms) to fast and more accurate detection of epilepsy diseases. (iii) Selection of appropriate feature sets for classification of the epilepsy disease.(iv) case study of different medicines required to control and treat the epilepsy disease as early as possible.

关键词： Machine learning algorithms soft sensors Epilepsy Medical treatment Machine learning Feature extraction Grid computing

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recent advances on soft computing and Data Mining: The Second international conference on soft computing and Data Mining (SCDM-2016), Bandung, Indonesia, August 18-20, 2016 Proceedings 1

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丛书名： advances in Intelligent Systems and computing

2017年

作者： Tutut Herawan, Rozaida Ghazali, Nazri Mohd Nawi, Mustafa Mat Deris (eds.)

ISBN: (数字)9783319512815

ISBN: (纸本)9783319512792;9783319512815

This book provides a comprehensive introduction and practical look at the concepts and techniques readers need to get the most out of their data in real-world, large-scale data mining projects. It also guides readers through the data-analytic thinking necessary for extracting useful knowledge and business value from the data. The book is based on the soft computing and Data Mining (SCDM-16) conference, which was held in Bandung, Indonesia on August 18th20th 2016 to discuss the state of the art in soft computing techniques, and offer participants sufficient knowledge to tackle a wide range of complex systems. The scope of the conference is reflected in the book, which presents a balance of soft computing techniques and data mining approaches. The two constituents are introduced to the reader systematically and brought together using different combinations of applications and practices. It offers engineers, data analysts, practitioners, scientists and managers the insights into the concepts, tools and techniques employed, and as such enables them to better understand the design choice and options of soft computing techniques and data mining approaches that are necessary to thrive in this data-driven ecosystem.

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Determination of Immunotherapeutic Epitopes of Human Papillomavirus Type 16 by Bioinformatics Approaches

Determination of Immunotherapeutic Epitopes of Human Papillo...

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conference of recent Trends and Applications of soft computing in Engineering, RTASCE 2024

作者： Akçeşme, Betül Aldadah, Raneem Galijatović, Aiša Hajdarpašić, Maida Department of Medical Biology Hamidiye Faculty of Medicine University of Health Sciences İstanbul Turkey Department of Genetics and Bioengineering International University of Sarajevo Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9783031828805

Cervical cancer is the fourth most common cancer among women caused by mostly Human papillomavirus (HPV) infection. Considering its high incidence and mortality rate especially developing and least developed countries, it is essential to explore therapeutics against HPV infection. Preventive vaccines exist against HPV infections, which are the main cause of cervical cancer, still there is need for therapeutic vaccines for those already exposed to HPV. Immunotherapy has gained significant attention as a potential strategy which targets HPV oncoproteins. With advances in computational biology and bioinformatics, novel in silico tools have been designed to develop novel therapeutic vaccines in the field of immunotherapy. In this study, by performing computational biology approaches, it was aimed to identify potential B-cell and T-cell epitopes for vaccine targeting HPV 16, as one of the two types of strains (with HPV 18) causes approximately 70% of all cervical cancers. Both linear and discontinuous B-cell epitope predictions by using BepiPred, ABCpred and DiscoTope servers were applied. Additionally, NetMHCpan-4.1 and NetMHCIIpan-4.0 were used to predict MHC class I and MHC class II epitopes by utilizing Immune Epitope Database (IEDB), a manually compiled database of experimentally identified immune epitopes. The antigenicity prediction test was done to ensure that identified binding sites would effectively trigger the immune system. Our results suggest that computational biology approaches can be a useful tool for the design of epitope vaccines targeting HPV-16. However, further studies are necessary to validate our results and to determine the effectiveness of the vaccine in vitro and animal models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Bioinformatics

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soft computing based Implementation of Autonomous Drone Vehicle using Fuzzy Logic based Control Systems

Soft Computing based Implementation of Autonomous Drone Vehi...

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Augmented Intelligence and Sustainable Systems (ICAISS), international conference on

作者： Soni Mishra S. Punitha T. Harikrishns K. Narayana Swamy Loganayagi S Department of Physics Graphic Era Hill University Dehradun India Department of Mathematics Vinayaka Mission’s Kirupananda Variyar Engineering College Vinayaka Mission’s Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of AI&ML Annamacharya Institute of Technology & Sciences Rajampet Andhra Pradesh India SoEEE(School of Electrical and Electronics Engineering) REVA University Kattigenahalli Yelahanka Bengaluru North Bengaluru India Department of Computer Science and Engineering Saveetha School of Engineering(SIMATS) Thandalam Chennai Tamil Nadu India

In recent years, there has been a lot of interest in the use of fuzzy logic-based control systems to construct autonomous drone vehicles. This method makes use of fuzzy logic's adaptability and flexibility to guide drones safely through challenging surroundings. This study gives a comprehensive framework for the design and implementation of fuzzy logic-based control systems for autonomous drone vehicles. The three primary parts of the proposed framework are perception, decision-making, and control. The perception module processes sensor input, including visual and depth data, to provide accurate representations of the world. The decision-making module interprets the perception data and uses fuzzy logic methods to arrive at sensible choices for navigation and obstacle avoidance. The fuzzy logic system's decisions are converted by the control module into the right control signals that the drone will follow. The fuzzy logic-based control system also shows resilience and adaptation to changing environmental circumstances and uncertainties, making it appropriate for real-time applications. New opportunities for autonomous drone applications, such as surveillance, delivery services, and search and rescue missions, are made possible by the adoption of this architecture. Using fuzzy logic-based control systems to create autonomous drone vehicles is a viable way to provide effective and precise drone navigation in challenging conditions. By offering a holistic framework that blends perception, decision-making, and control utilizing fuzzy logic approaches, this research advances the development of autonomous systems.

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Modelling the Mechanical Stress Dependence of 2D Magnetic Permeability in soft Magnetic Materials

Modelling the Mechanical Stress Dependence of 2D Magnetic Pe...

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international Mechatronics conference

作者： Szewczyk, R. Warsaw Univ Technol Fac Mechatron Inst Metrol & Biomed Engn Sw A Boboli 8 PL-02525 Warsaw Poland

ISBN: (纸本)9783030299934;9783030299927

Paper presents the application of anhysteretic curve concept presented in Jiles-Atherton model of magnetic materials for modelling of stress dependence of magnetic permeability. Due to connection of average anisotropy energy density with mechanical stresses, the anisotropic characteristic of relative permeability can be estimated. Presented solution opens new ways of modelling the characteristics of magnetoelastic sensors with non-uniform stress distribution, where it is necessary to consider not only the value of mechanical stresses, but also the connection between the direction of magnetizing field and stress-induced uniaxial anisotropy easy axis.

关键词： Anhysteretic curve Jiles-Atherton model Uniaxial anisotropy

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Lungs Cancer Nodules Detection from CT Scan Images with Convolutional Neural Networks 4th

Lungs Cancer Nodules Detection from CT Scan Images with Conv...

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4th international conference on soft computing and Data Mining (SCDM)

作者： Rehman, Muhammad Zubair Nawi, Nazri Mohd Tanveer, Aisha Zafar, Hassan Munir, Hamza Hassan, Sher Univ Malaysia Pahang Fac Comp Pekan Malaysia Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Soft Comp & Data Min Ctr SMC Batu Pahat Malaysia Univ Lahore Machine Learning Res Grp MLRG Islamabad Pakistan

ISBN: (纸本)9783030360566;9783030360559

Lungs cancer is a life-taking disease and is causing a problem around the world for a long time. The only plausible solution for this type of disease is the early detection of the disease because at preliminary stages it can be treated or cured. With the recent medical advancements, Computerized Tomography (CT) scan is the best technique out there to get the images of internal body organs. Sometimes, even experienced doctors are not able to identify cancer just by looking at the CT scan. During the past few years, a lot of research work is devoted to achieve the task for lung cancer detection but they failed to achieve accuracy. The main objective of this piece of this research was to find an appropriate method for classification of nodules and non-nodules. For classification, the dataset was taken from Japanese Society of Radiological Technology (JSRT) with 247 three-dimensional images. The images were preprocessed into gray-scale images. The lung cancer detection model was built using Convolutional Neural Networks (CNN). The model was able to achieve an accuracy of 88% with lowest loss rate of 0.21% and was found better than other highly complex methods for classification.

关键词： Lungs cancer Convolutional neural networks Features extraction Computed tomography Preprocessing Deep learning Computer vision

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An Enhanced Model for Digital Reference Services 4th

An Enhanced Model for Digital Reference Services

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4th international conference on soft computing and Data Mining (SCDM)

作者： Shahzad, Asim Nawi, Nazri Mohd Mahdin, Hairulnizam Khan, Sundas Naqeeb Hamid, Norhamreeza Abdul Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Soft Comp & Data Min Ctr SMC Parit Raja Malaysia

ISBN: (纸本)9783030360566;9783030360559

Digital Reference Service (DRS) play a vital role in the Digital Library (DL) research. DRS is a very valuable service provided by DL. Unfortunately, the reference service movement towards digital environment begins late, and this shift was not model based. So, a journey towards a digital environment without following a proper model raises some issues. A few researchers presented a general process model (GPM) in the late 1990s, but this process model could not overcome the problems of DRS. This paper proposes an enhanced model for DRS that use the storage and re-use mechanism with other vital components like DRS search engine and ready reference for solving the issues in DRS. Initially, storage and re-use mechanism are designed and finally, DRS search engine is designed to search appropriate answers in the knowledge base. We improved the GPM by incorporating the new components. The simulation results clearly states that the proposed model increased the service efficiency by reducing the response time from days to seconds for repeated questions and decreased the workload of librarians.

关键词： DRS model Virtual reference services Digital reference services Model for digital reference services

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A Spiking Neural Networks Model with Fuzzy-Weighted k-Nearest Neighbour Classifier for Real-World Flood Risk Assessment 4th

A Spiking Neural Networks Model with Fuzzy-Weighted k-Neares...

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4th international conference on soft computing and Data Mining (SCDM)

作者： Abdullah, Mohd Hafizul Afifi Othman, Muhaini Kasim, Shahreen Saharuddin, Shaznoor Shakira Mohamed, Siti Aisyah Univ Tun Husssein Onn Malaysia Fac Comp Sci & Informat Technol Batu Pahat 86400 Johor Malaysia Univ Tun Husssein Onn Malaysia Soft Comp & Data Min Ctr Batu Pahat 86400 Johor Malaysia

ISBN: (纸本)9783030360566;9783030360559

Inspired by the brain working mechanism, the spiking neural networks has proven the capability of revealing significant association between different variables spike behavior during an event. The combination of the capability of SNN to produce personalised model has allowed high-precision for data classification. The exiting accuracy of weighted k-nearest neighbors classifier being used in the spiking neural networks architecture, noticeably can be further improved by implementing fuzzy-weights on the features, therefore allowing data to be classified more precisely to the high-impacting features. Simulation has been done by using three classifiers-Multi-layer Perceptron, weighted k-nearest neighbors, and Fuzzy-weighted k-nearest neighbors (FwkNN) using a real-world flood case study dataset and two benchmark dataset. Based on the result using the Kuala Krai Rainfall Dataset, FwkNN classifier has improved accuracy by 3.48% and 3.57% for 3-days earlier and 1-day earlier classification respectively. As compared to, FwkNN classifier has proven the capability to reduce misclassification and increase the accuracy of dataset classification.

关键词： Personalised modelling Spiking neural network Spatio-temporal data NeuCube Fuzzy k-NN Feature weighting Classification Flood data

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