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Computational Intelligence, Networks and Security (ICCINS), International Conference on

作者： A. Vamshi R. Eswari X. I. Nirmal Joel A. Divine Regan Department of Computer Applications National Institute of Technology Tiruchirappalli India Department of Data Science St. Joseph's College Tiruchirappalli India

With the wide spread of the digital world, IoT devices generate huge amounts of data and store it in cloud servers as they have less storage space. When data is outsourced to the cloud servers, IoT device users lose control of it, which means the confidentiality and integrity of the data can be compromised. Further, several Third-Party Auditors (TPAs) are available to check the integrity of the data. But most of the TPAs are based on trust. In some cases, TPAs are being hired by Cloud data centers and in some cases, the TPAs may have direct connection with data consumers. In both the cases, there is a bias and the data may be at high risk. Therefore, to overcome the trust issue, in this paper an Efficient Machine Learning based data Auditing Scheme for Cloud Users is proposed using a hybrid recommendation system. A repository is maintained which keeps track of all TPA's history. The user submits auditing request to the repository which will run the recommendation system to select appropriate TPA to check the integrity of the user data in the cloud. It chooses the best TPA and gives the auditing results to the user. The proposed scheme reduces the risk of user data.

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The QUasi-Affine TRansformation Evolution (QUATRE) Algorithm: An Overview 4th

The QUasi-Affine TRansformation Evolution (QUATRE) Algorithm...

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4th Euro-China Conference on Intelligent data Analysis and applications, ECC 2017

作者： Meng, Zhenyu Pan, Jeng-Shyang Li, Xiaoqing Department of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783319685267

QUasi-Affine TRansformation Evolution (QUATRE) algorithm is a new simple but powerful stochastic optimization algorithm proposed recently. The QUATRE algorithm aims to tackle the representational/positional bias inborn with DE algorithm and secures an overall better performance on commonly used Conference of Evolutionary Computation (CEC) benchmark functions. Recently, several QUATRE variants have been already proposed since its inception in 2016 and performed very well on many benchmark functions. In this paper, we mainly have a brief overview of all these proposed QUATRE variants first and then make simple contrasts between these QUATRE variants and several state-of-the-art DE variants under CEC2013 test suites for real-parameter single objective optimization benchmark functions. Experiment results show that the movement trajectory of individuals in the QUATRE structure is much more efficient than DE structure on most of the tested benchmark functions. © 2018, Springer International Publishing AG.

关键词： Global optimization

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Continuous Tweet Stream Analysis using Apache Spark Streaming API & Kafka 15

Continuous Tweet Stream Analysis using Apache Spark Streamin...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Nisha, A.G. Shivaprasad, N. Nischitha, C.B. Shashikala, B.M. Sujithra, M. Chaba, Yogesh Electronics & Communications Engineering JSS Science & Technology University Mysuru India Dept.of Electronics & Communications Engineering Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Dept.of Computer Science St.Joseph's First Grade College Mysore India Dept.of Computer Applications Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Guru Jambeshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9798331300579

Social media has become a trend nowadays people are spending more time in social media platform like Facebook, Twitter, Instagram to share their opinions, creating new interests, ideas and generate *** provide a major platform for collecting information which helps us to analyze them for our *** such microblogging social network is twitter which allows information collaboration and quick information sharing that provides relief from rising *** has several millions of people tweeting and expressing their perspectives on a due to its distinctively brief and straightforward expressing *** enormous volume of raw data from tweets can be utilised for analysis and processing based on industrial or business *** this technical paper, the application of tweet sentiment analysis by extracting a vast number of tweets using continuous tweet stream provided by Spark and Kafka has been *** system fetches live tweets from Twitter based on the search criteria, process the tweets, by using text mining techniques and classify them based on different labels. © Grenze Scientific Society, 2024.

关键词： Tweets

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Combining and comparing regional epidemic dynamics in Italy: Bayesian meta-analysis of compartmental models and model assessment via global sensitivity analysis

Research Square

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Research Square 2021年

作者： Cereda, Giulia Viscardi, Cecilia Baccini, Michela Department of Statistics Computer Science Applications University of Florence Florence50134 Italy Florence Center for Data Science University of Florence Florence50134 Italy

During autumn 2020, Italy faced a second important SARS-CoV-2 epidemic wave. We explored the time pattern of the instantaneous reproductive number, R0(t), and estimated the prevalence of infections by region from August to December calibrating SIRD models on COVID19-related deaths, fixing at values from literature Infection Fatality Rate (IFR) and infection duration. A Global Sensitivity Analysis (GSA) was performed on the regional SIRD models. Then, we used Bayesian meta-analysis and meta-regression to combine and compare the regional results and investigate their heterogeneity. The meta-analytic R0(t) curves were similar in the Northern and Central regions, while a less peaked curve was estimated for the South. The maximum R0(t) ranged from 2.61 (North) to 2.15 (South) with an increase following school reopening and a decline at the end of October. Average temperature, urbanization, characteristics of family medicine and health care system, economic dynamism, and use of public transport could partly explain the regional heterogeneity. The GSA indicated the robustness of the regional R0(t) curves to different assumptions on IFR. The infectious period turned out to have a key role in determining the model results, but without compromising between-region comparisons. © 2021, CC BY.

关键词： Coronavirus

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data Analytics for Core Temperature Estimation in Battery Management System for Electric and Hybrid Vehicles 5

Data Analytics for Core Temperature Estimation in Battery Ma...

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5th International Conference on Energy, Power, and Environment, ICEPE 2023

作者： Chhetri, Ahilya Surya, Sumukh Mohan Krishna, S. Rao, Vidya Cactus Communications Engineering and Technology Mumbai India Bosch Global Software Technologies Pvt. Limited Bengaluru India Aced Alliance University Department of Electrical and Electronics Engineering Bengaluru India Manipal Institute of Technology Manipal Academy of Higher Education Department of Data Science and Computer Applications Manipal India

ISBN: (纸本)9798350313123

Compared to conventional means of transport thatuse fossil fuels, electric vehicles are known to reduce pollution levels as they take power from renewable sources of energy stored in energy storage devices like batteries and fuel cells. Core temperature estimation of batteriesis extremely important inbattery management systems for preventing thermal runaway and ensuring safe operation. In this study, core temperature is estimated using a Kalman filter for two different battery chemistries viz;lithium polymer and lithium iron phosphate using a second-order thermal model. Further, a linear regression model is applied to verify the prediction over the trained and tested dataset. The lithium iron phosphate prediction curvehad a fit of approximately 82-83%, whilelithium polymerhad a fit of 72-82% during the charging and discharging of OCV-SOC variation. © 2023 IEEE.

关键词： Simulink

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Identification of the Video Level for Video Communication in a Resource-Constrained Network

Identification of the Video Level for Video Communication in...

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Artificial Intelligence and data Engineering (AIDE), International Conference on

作者： Gururaj Bijur Ramakrishna M Karunakar A. K Department of Computer Science & Engineering Manipal Institute of Technology MAHE Manipal Udupi Karnataka India Department of Data Science & Computer Applications Manipal Institute of Technology MAHE Manipal Udupi Karnataka India

HEVC, which employs layered video coding techniques, is an extension of H.264 Advanced Video Coding. Layered video coding creates a valid bitstream by removing layers. Video streaming via wireless networks is made possible by this capability. The elements that affect the quality of video communication include node mobility, the kind of routing protocols, communication distance, etc. The video adaptation method calculates the number of video levels that will be lost and then extracts at Media Aware Network Elements in accordance with terminal capabilities and resource availability. Building the information needed to select the extraction point or adaption level is the goal of this work. The extraction points for streaming Multi-level Video over the resource constrained network have been calculated in this article.

关键词： Wireless networks Dynamics Bandwidth Streaming media Media Routing Routing protocols

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Character Classification and Actor Recognition in Yakshagana Images Using Machine Learning Techniques and Facial Makeup Pattern Analysis

Character Classification and Actor Recognition in Yakshagana...

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IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Anantha Murthy Prathwini Sanjeev Kulkarni Savitha G Department of Master of Computer Applications NMAM Institute of Technology NITTE (Deemed to be University) Nitte Karkala and and Research Scholar Institute of Computer Science and Information Science” Srinivas University India Department of Master of Computer Applications NMAM Institute of Technology NITTE (Deemed to be University) Nitte Karkala India Department of Computer Science and Engineering Srinivas University Institute of Engineering and Technology Mangalore India Department of Data Science and Computer Applications Manipal Academy of higher Education Manipal Institute of Technology Manipal

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form.

关键词： YOLO Accuracy Image recognition Recurrent neural networks Machine learning Documentation Logic gates Character recognition Cultural differences Image classification

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Improved UNET Architecture for Effective COVID-19 Severity Prediction from Pulmonary CT Images

Improved UNET Architecture for Effective COVID-19 Severity P...

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Innovations in Communications, Electrical and computer Engineering (ICICEC), International Conference on

作者： M. Ramla R. Anita Jasmine M. Ramesh Femilda Josephin Joseph Shobana Bai Department of Computer Applications SRM Institute of Science and Technology Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai India Department of Computer Science SRM Institute of Science and Technology Chennai India Department of Computer Engineering Istinye University Istanbul Turkey

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

COVID-19, is one of the deadliest pandemic hit globally crises in the recent years, continues to persist with emerging variants of concern. Due to the virus's high transmissibility, rapid and precise detection of COVID-19 is crucial for timely medical intervention. This research presents an advanced deep transfer learning approach for quantifying the severity of COVID-19 infection from CT lung images. Using computer vision techniques, lung regions were extracted from CT scans through semantic segmentation using SegNet. An enhanced U-Net architecture, integrated with the Atrous Spatial Pyramid Pooling (ASPP) technique, was employed to semantically segment the infected regions of the lungs. Based on the segmented infection areas, the severity of the infection was classified into mild, moderate, and severe. The proposed framework, which incorporates lung extraction followed by Ground-Glass Opacity (GGO) segmentation, achieves the segmentation accuracy of 96% and a prediction accuracy of 98.9%.

关键词： COVID-19 Image segmentation Technological innovation Accuracy Convolution Lungs Computed tomography Transfer learning computer architecture Predictive models

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Efficient mode decision in HEVC

Efficient mode decision in HEVC

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Artificial Intelligence and data Engineering (AIDE), International Conference on

HEVC is the new coding standard aiming to provide better coding efficiency compared to its previous standard. Meantime this increases the computational complexity. In HEVC, there are 35 intra prediction modes, where as in H.264/A VC there were only 9 intra prediction modes. In this paper, we proposed a fast rough mode decision (RMD) algorithm to decrease the computational complexity using up and left reference pixels. The three-stage search method is implemented in RMD algorithm. The results obtained when compared with HM16.18, shows that the proposed algorithm can save 12.78 % to 28.86% of the encoding time with acceptable loss in video quality.

关键词： Degradation Search methods Prediction algorithms data engineering Encoding Quality assessment Computational complexity

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Siamese Network Based Anomaly Detection Framework for Robust Federated Learning

Siamese Network Based Anomaly Detection Framework for Robust...

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Intelligent Technologies (CONIT), International Conference on

作者： Karthik Shenoy K Manohara M. M. Pai Radhika M. Pai Department of Information and Communication Technology Manipal Institute of Technology Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal India

Federated learning is a decentralized approach to machine learning that has increased in popularity in recent years. It enables several participants to train a common model without revealing their data. This strategy is, however, susceptible to attacks from malicious clients who can launch targeted model poisoning attacks and reduce learning performance by delivering false model updates to the server. It is necessary to identify and eliminate such fraudulent updates and the attackers behind them to preserve the robustness and security of the shared model. In this paper, a novel Siamese network-based architecture for robust federated learning is proposed. which can identify and eliminate harmful updates. Our method is assessed and compared with other approaches for adversarial detection in image classification tasks in a federated setting, using a CNN model. Experimental findings demonstrate that the system offers reliable federated learning that is resistant to both targeted model poisoning and untargeted Byzantine attacks. Overall, the study advances the development of secure federated learning systems, by suggesting a novel method for identifying and deleting fraudulent updates in federated learning,

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