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A Study on the Potential Role of Blockchain in Future Wireless Mobile Networks

A Study on the Potential Role of Blockchain in Future Wirele...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Madan Lal Saini Jeidy Panduro-Ramirez Jesús Padilla-Caballero Archana Saxena Mohit Tiwari Kolipaka Ravi AIT-CSE Chandigarh University Mohali Punjab India Universidad Tecnológica del Perú Postgraduate Department Universidad César Vallejo Uttaranchal Institute of Technology Uttaranchal University India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

The "Internet of Things" or IoT has recently transformed the established sector into smart infrastructure with 6G data-driven design. Because of their decentralization, straightforwardness, absence of range assets, intrinsic protection and security, absence of interoperability, mystery, and prospering shrewd application regions like IoT Innovation and Industry 4.0, blockchains (BCT) has drawn in a ton of consideration. These facts served as the impetus for this paper's extensive survey, which focused on the potential benefits and difficulties of integrating blockchain technology into 6G cell networks, Industrial iot, and smart industries. These issues included power grid sharing, mathematical loads, response time, transmission capacity above, plans of action, maintainability goals, and edge insight. Specialists accentuated the combination of blockchain and IoT to empower knowledge dispersion in modern IoT later on, as well as the innovation model of 6G to understand the effective execution of BCT plans. This paper talked about the interesting issues that are now being faced, mitigating strategies, and potential future research directions that could aid in the realisation of this vision.

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Social Network Analysis of Twitter Tweets 3

Social Network Analysis of Twitter Tweets

引用

3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Nisha Singh, Gurpreet Chandigarh University Department of Computer Science Engineering Punjab Gharuan India

ISBN: (纸本)9781665438117

Twitter is an online broadcast medium where the people use it for blogs and some another purpose. It is that platform in which active user growing rapidly. Every month, 328 million people are added on this platform. Influencer post some short massages called the tweet. People post their tweet regarding any topic or for chitchat purpose. The huge amount of data is collected by twitter influencer regarding the different aspect of human life. The goal of this research is to understand the tweets posted in one year with time interval. Then the analysis is performed for the tweets of twitter and also which tweet belongs to which category. The analysis of this data allows achieving some meaningful conclusion about the behavior of twitter influencers. Twitter is that platform in which we can share our thoughts, ideas, text, video, like, dislike, trust, distrust, positive or negative points of views to each other. These interactions help to connect with the real-world scenarios. Twitter is widely used platform in know days. The dataset used has been based on the detection of tweets and steady the users core based on the various tweets recommendations activities. In this paper we work on (i)Sharing the Twitter dataset and measurements. (ii)Earning by the tweets (iii)Time dependency etc. © 2021 IEEE.

关键词： Graph theory

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Real-Time Multilingual Farming Assistance using NLP Integrated Web API

Real-Time Multilingual Farming Assistance using NLP Integrat...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： N. Shirisha M. Srivani K. Kowsalyadevi G Bhaskar Phani Ram Pavan Kumar Vadrevu V. Bhaskara Murthy Computer Science and Engineering MLR Institutions of Technology Dundigal Hyderabad India Department of Computer Science & Engineering(DS) CVR College of Engineering Hyderabad India Department of CSE Sona College of Technology Salem TamilNadu India Department of ECE Vidya Jyothi Institute of Technology Hyderabad Department of Information Technology Shri Vishnu Engineering College for Women Department of MCA B V Raju College Vishnupur Bhimavaram

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

This research introduces a smart helper designed to assist farmers in remote locations. The proposed system provides timely and relevant information on various aspects of farming, including soil management, pest control, and crop selection. Key features include personalized advice, real-time weather updates, market insights, and multilingual support. By providing farmers with easy access to critical information and decision-making tools, this system aims to enhance agricultural productivity, improve livelihoods, and promote sustainable farming practices.

关键词： Productivity Renewable energy sources Soil Programming Pest control Real-time systems Multilingual Monitoring Farming Meteorology

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Multi-modal Action Segmentation in the Kitchen with a Feature Fusion Approach 15

Multi-modal Action Segmentation in the Kitchen with a Featur...

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15th international conference on Quality control by Artificial vision

作者： Kogure, Shunsuke Aoki, Yoshimitsu Keio Univ Kohoku Ku 3-14-1 Hiyoshi Yokohama Kanagawa Japan

ISBN: (数字)9781510644274

ISBN: (纸本)9781510644274

In this paper, we propose a "Multi-modal Action Segmentation approach" that uses three modalities: (i) video, (ii) audio, (iii) thermal to classify cooking behavior in the kitchen. These 3 modalities are assumed to be features related to cooking. However, there is no public dataset containing these three modalities. Therefore, we built the original dataset and frame-level annotation. We then examined the usefulness of Action Segmentation using multi-modal features. We analyzed the effects of each modality using three evaluation metrics. As a result, the accuracy, edit distance, and F1 value were improved by up to about 1%, 2%, and 8%, respectively, compared to the case when only images were used.

关键词： computer vision Machine Learning Action Segmentation Dataset Construction Multi-modal Learning

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A CNN and LSTM-based Data Prediction Model for WSN 3

A CNN and LSTM-based Data Prediction Model for WSN

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3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Dohare, Anand Kumar Tulika SHUATS University Dept. of Computer Science IT Prayagraj India

ISBN: (纸本)9781665438117

Due to the limited energy, the growth in network size and sensory data causes a slew of severe issues for wireless sensor networks. Data prediction methods are useful for reducing network traffic and increasing network lifespan, particularly when data correlation across sensory data is explored. In the event that these sensor nodes fail, data prediction may be utilised to retrieve aberrant or lost data. Current wireless sensor network prediction techniques do not fully use the spatial-temporal relationship between remote sensor hubs, resulting in greater prediction error. In a wireless sensor network, this article offers a new approach for multi step sensory data prediction. To begin, we present ANN (artificial neural networks) based on 1-D CNN that use pre-processed sensory input to extract abstract characteristics of various qualities. Then, using these abstract characteristics, one-step prediction is obtained. Finally, by repeatedly utilising chronicled information and the forecast outcomes of the preceding phase, the multi-step expectation is presented. The suggested multi-step prescient model can foresee multi step (mid and short term) tactile information after choosing appropriate hub mixes in which the spatial-fleeting connection is emphasised, according to experiment findings, and its performance is superior than other similar approach. © 2021 IEEE.

关键词： Sensor nodes

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Comparative Study of Implementation of Very Deep Super Resolution Neural Network and Bicubic Interpolation for Single Image Super Resolution Quality Enhancement

Comparative Study of Implementation of Very Deep Super Resol...

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international conference on Applied Electromagnetics, Signal Processing and Communication (AESPC)

作者： Ganesan P M. Ravichandran B.S. Sathish L.M.I.Leo Joseph G. Sajiv R. Murugesan Dept. of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Hyderabad India Dept. of Computer Science Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept. of Computer Science Engineering AI Sri Venkateshwara College of Engineering Bengaluru India Dept. of Electronics and Communication Engineering SR University Warangal Telangana India Saveetha School of Engineering SIMATS Chennai India Dept. of Electronics and Communication Engineering Narshima Reddy Engineering College Secunderabad India

To improve the resolution and quality of a low level image, single image super resolution (SISR) is a challenging endeavor in the turf of computer vision. It is essential to many applications, including image editing, security systems, medical imaging, and others. In order to increase the spatial quality of a low level image, SISR attempts to approximate the high frequency details that are missing from the image. The field was formerly based on manually created features and interpolation techniques, but current developments in deep learning have completely changed it. The suggested work compares the effectiveness of bicubic interpolation and neural network based very deep super resolution for single image super resolution image quality improvement. Utilizing blind and complete reference picture quality measures, the effectiveness of the two approaches is evaluated. The effectiveness of the very deep super resolution for single image is clearly demonstrated by the experimental results.

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A Survey on Pest and Disease Monitoring of Crops 3

A Survey on Pest and Disease Monitoring of Crops

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3rd international conference on Signal Processing and Communication (ICPSC)

作者： Deepika, P. Kaliraj, S. Easwari Engn Coll Dept Informat Technol Chennai Tamil Nadu India SRM Inst Sci & Technol Dept Software Engn Chennai Tamil Nadu India

ISBN: (纸本)9781665428644

Maintenance of Crop health is essential for the successful farming for both yield and product quality. Pest and disease in crops are serious problem to be monitored. pest and disease occur in different stages or phases of crop development. Due to introduction of genetically modified seeds the natural resistance of crops to prevent them from pest and disease is less. Major crop loss is due to pest and disease attack in crops. It damages the leaves, buds, flowers and fruits of the crops. Affected areas and damage levels of pest and diseases attacks are growing rapidly based on global climate change. Weather Conditions plays a major role in pest and disease attacks in crops. Naked eye inspection of pest and disease is complex and difficult for wide range of field. And at the same time taking lab samples to detect disease is also inefficient and time-consuming process. Early identification of diseases is important to take necessary actions for preventing crop loss and to avoid disease spreads. So, Timely and effective monitoring of crop health is important. Several technologies have been developed to detect pest and disease in crops. In this paper we discuss the various technologies implemented by using AI and Deep Learning for pest and disease detection. And also, briefly discusses their Advantages and limitations on using certain technology for monitoring of crops.

关键词： Deep Leaning Pest Monitoring Disease Monitoring computer vision

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A Detailed Review and Study of Implementation of Security System Design for Machine Learning-Based Software Systems: Issues And Challenges

A Detailed Review and Study of Implementation of Security Sy...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Melanie Lourens Vikas Tripathi Joel Alanya-Beltran Shaik Vaseem Akram Bandi Bhaskar Firos A Department of Human Resources Management Durban University of Technology Berea Durban South Africa Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Universidad Tecnológica del Perú Perú Uttaranchal Institute of Technology Uttaranchal University School of CS& AI SR University Warangal Telangana Department of Computer Science and Engineering Rajiv Gandhi University (A Central University) Doimukh Arunachal Pradesh India

The rapidly growing technology specially in the field of software, Machine Learning (ML) has played an important role in a range of tasks, including voice, video, and computer vision. It is currently being utilised in software systems to automate the crucial processes more and more. Machine learning-based modern software systems (MLBSS) are currently difficult to build safely, which will severely limit the uses in security and safety-critical domains. Recently, majority of articles are published and still research work is going on the safety problems for ML and Deep Learning (DL), which place a strong prominence on the models and data both, adversaries’ threats have been taken into consideration. In this paper, we address the prospect that system bugs or external adversarial assaults might lead to security vulnerabilities for machine learning-based software systems, and we propose that safe development techniques ought to be applied throughout the whole lifecycle. We conclude by providing a thorough study of the security for MLBSS, which includes a comprehensive analysis based on a review of the structure of three distinctive features in terms of security issues. The entire state-of-the-art for MLBSS secure development is also provided.

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DeepCatch: A New Testing Framework for Convolutional Neural Networks

DeepCatch: A New Testing Framework for Convolutional Neural ...

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computer Science, Electronic Information Engineering and Intelligent control Technology (CEI), IEEE international conference on

作者： Qichen Bi Shengbo Chen School of Computer Engineering and Science Shanghai University Shanghai China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

DNNs have achieved great success in many fields over the past decades, however, due to a number of problems that often occur with DNNs, there is a growing interest in systematic testing of DNNs. Researchers aim to identify additional corner cases for the model to enhance its robustness. Therefore, we designed DeepCatch, a white-box testing framework designed to perform better in finding corner cases. We designed comparative experiments in image categorization to demonstrate the advantages of DeepCatch, which can find more corner cases while these corner cases are distributed over more categories.

关键词： Software testing computer science Systematics Computational modeling Neurons Image categorization Robustness

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Posture data automatic extraction of ornamental turtle based on computer vision technology 3

Posture data automatic extraction of ornamental turtle based...

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2021 3rd international conference on Artificial Intelligence and computer Science, AICS 2021

作者： Ma, Yinchi Wei, Wei Beijing Fisheries Research Institute Beijing China Beijing Municipal Research Institute of Environmental Protection Beijing China

The posture data of ornamental turtle is a quantitative index to evaluate the quality of the turtle. From the young turtle to the adult turtle, the posture data is constantly changing and the quality can also change accordingly. An integrated observation platform is designed and implemented. The automatic extraction of five posture data, i.e., the length of carapace, the width of carapace, the height of carapace, the length of plastron, and the width of plastron, is realized by using OpenCV based computer vision technology. The results of the experiment and the data of artificial measurement were compared and analyzed. The average fitting degree of the two values reached 99.2%. This method can extract the posture data of ornamental turtle quickly and accurately, and then track its growth and development. It will greatly reduce the workload of data collection in the process of ornamental turtle breeding, and effectively improve the accuracy of data collection. At the same time, it will reduce the risk of damage, and have important production and application value. © Journal of Physics: conference Series 2021.

关键词： computer vision

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