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A comprehensive survey of state-of-art techniques in digital watermarking 4

A comprehensive survey of state-of-art techniques in digital...

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4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Allwadhi, Sachin Joshi, Kamaldeep Yadav, Ashok Kumar Nandal, Rainu Jain, Rishabh M.D.University Deptt. of Computer Science & Engg Haryana Rohtak India Amity University Amity School of Engg. & Technology U.P Noida India Galgotias College of Engineering and Technology Deptt. of Computer Science & Engg Greater Noida India

ISBN: (纸本)9781665474368

Digital image watermarking refers to implanting secret information for copyright protection, content authentication of a given media to avoid future conflicts or illegal possession of data. A number of applications are present today for which suitable watermarking techniques can be used to mitigate this problem. Based on the domain, image watermarking can be categorized into spatial or transformation domains. Transform domain techniques are widely used by researchers because of their robustness and security. there is always a tradeoff between robustness, payload, and imperceptibility as maintained by various researchers that is also reviewed. Also, this paper presents various state-of-art watermarking techniques with their performance metrices such as PSNR, MSE, NC, BER, etc. along with their limitations. this survey help the researcher to develop a new watermarking model based on the application domain. © 2022 IEEE.

关键词： Copyrights

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LoRaX: Repurposing LoRa as a Low data Rate Messaging System to Extend Internet Boundaries 22

LoRaX: Repurposing LoRa as a Low Data Rate Messaging System ...

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4th ACM SIGCAS/SIGCHI conference on computing and Sustainable Societies (COMPASS)

作者： Vigil-Hayes, Morgan Hossain, Md Nazmul Elliott, Alexander Belding, Elizabeth Zegura, Ellen No Arizona Univ Flagstaff AZ 86011 USA Georgia Inst Technol Atlanta GA USA Univ Calif Santa Barbara Santa Barbara CA USA

ISBN: (纸本)9781450393478

Globally, 43% of households lack Internet access, primarily in regions where deployment and/or service costs are prohibitive, including in the least developed countries, rural locations, and regions with high concentrations of ethnic minorities and low-income populations. Unfortunately, this lack of Internet access increasingly equates to a lack of access to essential services, such as healthcare, education, and economic opportunities. In an environment of marginal economics, creative and varied approaches to obtaining access have flourished, including Internet kiosks long popular in the Global South, libraries as public access in the Global North, parking lot use of open WiFi access points, and spectrum-based solutions such as TV whitespace links and citizen band radio. In the near future, local 5G and the deployment of satellite constellations promise yet additional options in the price/performance space for access. In this context we are interested in the following research question: How can the presence of multiple networks, with different price, performance, and geographic reach profiles, be best used in concert to improve access to critical services? We propose that a robust answer to this question bears a holistic, cross-layer examination of new communication paradigms, network architecture innovation, and application design. We make this concrete by running to ground a specific case study of two networks, one high performance yet limited in geographic scope and the other low performance yet pervasive. Specifically our LoRaX (LoRa eXtends the Internet) system combines high bandwidth but non-pervasive Internet access with a low data rate, low power, yet ubiquitious network made possible by IoT developments. By focusing on two networks with extreme differences, we explore a design space that offers users new opportunities for participating in Internet-based services-even when high speed Internet connectivity is intermittent. We also reflect on the generality of

关键词： digital divide last mile connectivity challenged networking environment low data rate messaging LoRa LPWAN

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User Centered Agile Software Development: Emerging themes and Future Trends 4

User Centered Agile Software Development: Emerging Themes an...

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4th international conference on data Analytics for Business and Industry, ICDABI 2023

作者： Ramjaun, Umme Suhailah Abdullah, Khairul Hafezad Rana, Muhammad Ehsan Asia Pacific University of Technology and Innovation Graduate School of Technology Kuala Lumpur Malaysia Universiti Teknologi MARA Perlis Branch Science Laboratory Unit Perlis Malaysia Asia Pacific University of Technology and Innovation School of Computing & Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350369786

Despite having several advantages, agile methodology and its variants tend to hamper the development of innovative solutions as end-user needs are not considered. the integration of Agile to User Centered Design is therefore a topic of great interest for both academicians and industry professionals as it enables organizations to benefit from the advantages of agile methods while working on its drawbacks. the analytical tools ScientoPy, VOSviewer and bibliometrix were used to understand the co-relationships between 542 articles obtained from the Scopus database using the scientometric technique. Following a review which spans from the conception of agile in 2001 to the present date, generic concepts such as usability, humans, and usability evaluation and specific terms such as commerce, aged, adult and outcome assessment surfaced as the main keywords. Furthermore, most of the concepts referred to in 2002-2012 have emerged as software design in the last 10 years. Usability, agile methods, and usability evaluation were identified as the trendiest topics even though these three are below 20% with the slightest percentage of documents published in the last years 2021-2022. the trend analysis shows the decline of usability, agile methods, and usability evaluation and the emergence of systematic literature review, UX, MHealth and mobile apps. © 2023 IEEE.

关键词： User centered design

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Car Price Prediction 4

Car Price Prediction

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4th international conference on Information Management and Machine Intelligence, ICIMMI 2022

作者： Venkatesan, Sanchith Handa, Shivam Baskar, M. Department of Computing Technologies Srm Institute of Science and Technology India

ISBN: (纸本)9781450399937

Because of the intense rivalry among automakers, the price of a car has a tremendous impact on a model's sales and profit. this tool assists us in analyzing the price of a model based on various components and features. the result is derived using machine learning. We made use of the linear regression algorithm using Scikit-Learn. the Machine learning model is fit with data consisting of various variables for training. We have shown how the data is modified to increase accuracy and efficiency. the results help us determine a fair price, allowing us to estimate the appropriate value of a car. © 2022 ACM.

关键词： Automobiles

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An Intelligent Model of Email Spam Classification 4

An Intelligent Model of Email Spam Classification

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4th international conference on Emerging Research in Electronics, Computer science and Technology, ICERECT 2022

作者： Chakravarty, Ajay Manikandan, V. Teerthanker Mahaveer University Department of Computing Science and IT Moradabad India Jain University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665456357

One of the biggest dangers to the modern Internet is email spam. Numerous anti-spam filters have been developed to counter this danger. Predicting the labels of emails in a personalised mailbox is one of these filters' biggest challenges. Private data loss is another risk posed by these spam mailings. Modern studies have classified text messages as spam by using some stylistic characteristics of the texts. the detection of email spam can be significantly impacted by the use of well-known terms, phrases, abbreviations, and idioms. Nowadays, spam emails are successfully filtered automatically using machine learning algorithms. We evaluated the absolute most famous machine learning methods (Bayesian classification, k-NN, ANNs, SVMs, Counterfeit safe framework, and Unpleasant sets) and of their appropriateness to the issue of spam in this paper, where we introduced the NLP component that can channel spam and non-spam emails and furthermore classify into various spam sends. categorization of email. the algorithms' descriptions and a comparison of how well they performed on the SpamAssassin spam corpus are offered. © 2022 IEEE.

关键词： Electronic mail

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A Comparative Exploration of Machine Learning Algorithms for Disease Detection 4

A Comparative Exploration of Machine Learning Algorithms for...

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4th international conference on Emerging Research in Electronics, Computer science and Technology, ICERECT 2022

作者： Saxena, Ashendra Kumar Shashikala, H.K. Teerthanker Mahaveer University Department of Computing Science and IT Moradabad India Jain University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665456357

Alzheimer's disease (Promotion) is an unsolvable ongoing issue that causes cerebral disintegration, which results in memory loss and mental deterioration. Machine learning techniques are widely used in a variety of healthcare disciplines. A remarkable amount of research has gone into characterizing Alzheimer's disease. In order to provide a better understanding of the work that has been completed in the subject of Alzheimer's disease, we have reviewed several articles by various experts that use various machine learning techniques collectively. One of the major problems in recent years has been Alzheimer's disease. Due to this illness, people lost their capacity for comprehension and reasoning, among other things. Applying machine learning research to attractive reverberation imaging (X-ray) techniques can help determine promotion more quickly and may help predict the progression of the disease. It was also possible to predict individual dementia in older adults using Promotion screening data and ML classifiers. the X-ray segment data and the patient's prior states can help in improving the classifier execution to predict the Promotion subject status. © 2022 IEEE.

关键词： Disintegration

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A comparative study of various techniques for crop Disease Detection and segmentation 4

A comparative study of various techniques for crop Disease D...

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4th international conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Prakash, Varun Singh, Sanjay Aggarwal, Ankur Kumar Raghav, Sagar Sood, Shubham Pandian, M. thurai Dept. of Computer Science and Technology Manav Rachna University Faridabad India School of Computing and Information Technology REVA University Bengaluru India

ISBN: (纸本)9781665401180

Plant diseases have always been a major concern with food security in particular, the quality assurance of the agricultural products usually depends on the appropriate identification of the diseases, if any, in the crops used in their production. Moreover, since a vast majority of world's economy usually depends on the agriculture, it becomes more important to cut short on the losses incurred with the traditional methods of crop disease detection that involves human supervision and observations taken with naked eyes which are often inaccurate, time consuming and error prone. Since food production and its consumption could have consequence that spans multiple spheres ranging from a country's economic growth to health of its denizen, it becomes more than relevant to establish a rather reliable and automatic process for crop disease detection. this study proposes a comparative study underlying the various researches involving computer vision in particular, Machine Learning and Deep Learning, for crop disease detection automation, as a part of underlying framework, this study intends to present the necessary methods and techniques such as acquiring data, feature extraction, image segmentation, etc., that withstood the test of the time in the required context. © 2022 IEEE

关键词： Computer vision

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Research progress and trends of classroom teaching behavior coding 4

Research progress and trends of classroom teaching behavior ...

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4th international conference on Computer science and Educational Informatization, CSEI 2022

作者： Bail, Yuzhen Zhang, Yuzhuo School of Information and Science Technology Yunnan Normal University Kunming China

ISBN: (数字)9781839537882

ISBN: (纸本)9781839537882

Classroom teaching behavior coding has received widespread attention from classroom analysis researchers in China in recent years. However, in the context of big data, how to effectively analyze the data of classroom teaching behavior is an important problem that urgently needs to be solved. In combination with bibliometric means, 422 articles published from 2004 to 2022 are encoded and analyzed. the analysis found that with the vigorous development of classroom teaching behavior coding, a new trend of interdisciplinary integration and the applied topic fields are gradually expanding from micro to macro. © 2022 IET conference Proceedings. All rights reserved.

关键词： behavioural sciences computing data analysis educational administrative data processing information analysis teaching Big data

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Implementation of Dynamic Resource Allocation using Adaptive Fuzzy Multi-Objective Genetic Algorithm for IoT based Cloud System 4

Implementation of Dynamic Resource Allocation using Adaptive...

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4th international conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Srinadh, V. Rao, P.V. Nageswara Department of Computer Science and Engineering GMR Institute of Technology Andhra Pradesh India University Andhra Pradesh India

ISBN: (纸本)9781665401180

IoT based cloud computing system faces new challenges every day, due to the complex structure of system clusters and high volume of data processed by the systems. the ability of acquiring resources in an elastic manner is considered as the primary rationale for adopting IoT based cloud computing system. Elasticity mainly supports the facility to grow and shrink the virtual resources dynamically according to the requirement of IoT based cloud users. this work proposes enhanced genetic algorithm is proposed to solve these problems, which is used to accomplish better virtual machine allocation across IoT based cloud servers for maintaining vertical elasticity. the resource underutilization problem and high operational cost of IoT based cloud system noticed in the dynamic resource allocation (DRA) technique motivated to propose resource allocation technique. Further, the DRA operation is improved by adaptive fuzzy multi-objective genetic algorithm (AFMOGA) for efficient resource allocation using horizontal elasticity approach in second contribution. the objectives of the problem are to maximize the load distribution and to improve the resource utilization, thereby tried to provide the horizontal elasticity for resource allocation in an efficient manner. Further, the proposed technique also reduces the virtual machine migration count. Moreover, the proposed algorithm provides perfect balance between the exploration and exploitation processes by efficiently making use of adaptive genetic operators integrated with the fuzzy inference system (FIS). the simulation results shows that the proposed method outperforms compared to the state of art approaches. © 2022 IEEE

关键词： Internet of things

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the Impact of Parameters on Semantic Segmentation: A Case Study on the CamVid dataset 23

The Impact of Parameters on Semantic Segmentation: A Case St...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhang, Yue Yang, Ruiqi Wang, Jun Chen, Nan Dai, Qinling Southwest Forestry University Swfu Faculty of Forestry Yunnan Kunming China College of Big Data and Intelligent Engineering Southwest Forestry University Swfu Yunnan Kunming China Art and Design College Southwest Forestry University Swfu Yunnan Kunming China

ISBN: (纸本)9781665494571

Image segmentation is an important topic of remote sensing image processing. High-resolution remote sensing images contain complex object information. the application of traditional segmentation methods is greatly restricted. Deep learning is suitable for processing remote sensing images due to the complex multi-layer nonlinear transformation characteristics. Many image semantic segmentation methods based on deep learning were proposed, which realizes an end-to-end semantic segmentation workflow. Most of those studies focused on the design of network architectures. In contrast, the impact of parameters on final segmentation accuracy was not fully exploited. In this context, with the CamVid dataset and popular U-Net convolutional neural network and DeeplabV3+, this paper explored the impact of the max Epoch and the initial learning rate on the segmentation results. the study results show that in Deeplab V3+, when the MaxEpochs parameters are different and the InitialLearningRate parameters are the same, the improvement of Global Accuracy is not obvious. When the InitialLearningRate parameters are different and the MaxEpochs parameters are the same, the Global Accuracy has been greatly improved, increasing by about 3%. In U-Net, when the MaxEpochs parameters are different and the InitialLearningRate parameters are the same, the Global Accuracy is improved by about 1%. When the InitialLearningRate parameters are different and the MaxEpochs parameters are the same, the Global Accuracy is greatly improved, increasing by 24%. they can be concluded that the max epochs have slight impact on the final global accuracy, in contrast, the InitialLearningRate has a great impact on the experimental results. © 2021 IEEE.

关键词： Deep learning Image segmentation Smart cities Semantics Network architecture Convolutional neural networks Remote sensing

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