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Towards Eco-Sustainability and Green Analytics Model to Measure the Performance of Big Data Systems 9

Towards Eco-Sustainability and Green Analytics Model to Meas...

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9th International Conference on information Management, ICIM 2023

作者： Jusoh, Yusmadi Yah Abdullah, Rusli Nor, Rozi Nor Haizan Yahaya, Jamaiah Hj. Muhammad, Shireen Arunachalam, Aishwharya Raani Universiti Putra Malaysia Faculty of Computer Science and Information Technology Dept. of Software Engineering and Information Systems Serdang Malaysia Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi Malaysia Faculty of Communication Selangor Malaysia

ISBN: (纸本)9798350336382

Many BDS is doomed to failure because of the missing knowledge on measuring the performance of BDS. The failure to identify the performance measurement and correct will make the problems worsen. This will complicate the efforts of fixing such problems and more importantly, will affect the quality of knowledge, insights, and results expected by the users in the future. In achieving durable performance, the organization needsto understand the factors that contribute to the performance of the eco-sustainability and green analytics metrics for BDS. Although the existence of its importance and awareness was largely neglected. Therefore, the eco-sustainability and green analytic measurement of the BDS is examined in this research. Measuring the BDS performance has the benefit of identifying problems and launching corrective actions before these problems happen and become worsen. The research will investigate the eco-sustainability and green analytic measures which will be proposed and used to capture performance for each process of BDS. Then, based on such measures and metrics, as well as existing performance concepts, frameworks, and models, a measurement model for BDS will be created. The model will be validated through expert evaluations and confirmatory studies with users and practitioners of BDS users. The research has a greater significance in leveraging existing performance concepts in BDS settings and the research proposes users' participation in the sustainability and continuous performance evaluation of theirBDS. © 2023 IEEE.

关键词： Big data

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Design of Violence Event Detection System Based on CCTVs by Human Body Pose Recognition

Design of Violence Event Detection System Based on CCTVs by ...

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chang, Kuei-Chung Liao, Yi-Ching National Yunlin University of Science and Technology International Graduate School of Artificial Intelligence Taiwan National Changhua University of Education Dept. of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

Violent incidents have happened regularly and usually cause tragedies due to without rescued immediately. Therefore, we expect to study a violence detection and alarm system based on human body pose recognition of images captured from public CCTVs. The system uses OpenPose for real-time skeleton recognition to detect poses based on the abnormal angles or speeds of movement between the various joints of the limbs to determine whether it is violent behavior or not. If possible violent events are detected, the system will trigger an alarm and notify the police or security guard to deter the attacker. Victims will be able to get assistance as soon as possible through this recognition system even if they have obstacles for calling help by themselves. Experimental results show that the target violence behaviors can be detected successfully. © 2023 IEEE.

关键词： Gesture recognition

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Optimizing Convolutional Neural Network Hyperparameters for MNIST Image Classification: Insights and Implications 2

Optimizing Convolutional Neural Network Hyperparameters for ...

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2nd International Conference on Advances in Computation, Communication and information Technology, ICAICCIT 2024

作者： Dar, Eman Rana, Khadija Yousaf, Hoor Fatima Asif, Talha Hussain, Muhammad Zunnurain Ijaz, Hussain Hasan, Muhammad Zulkifl Dept. of Computer Engineering Information Technology University Lahore Pakistan Bahria University Lahore Campus Pakistan NCBA&E DHA Campus Pakistan Dept. of Computer Science Bahria University Lahore Campus Pakistan Cloud Engineer Information Technology University Pakistan Researcher Cyber Security Center Universitie Putra Malaysia Malaysia

ISBN: (纸本)9798331541217

The recognition of handwritten digits has been among the most enduring fundamental problems explored in the field of machine learning and computer vision. The objective of this work is to design a state-of-art Convolutional Neural Network (CNN) for the image classification on the MNIST dataset to resolve issues related to data pre-processing and parameter tuning effectively. The work starts with standardizing the image data and then using one-hot encoding for the models that will improve the generalization and representation. The CNN structure followed in this work includes the convolutional layer, batch normalization, pooling layers, and fully connected layers with an efficient integration of features. To measure such performance indicators as accuracy, the given dataset is divided for training and validation. The results are also preprocessed to control for overfitting and convergence using back propagation with early stop to stop training when significant accuracy improvement is not observed and learning rate decay to encourage the model to search for better weights in the final stages of training. The model's performance is tested and evaluated by using different epoch intervals and learning rates, and it is shown how critical it is to select the correct default hyperparameters for achieving efficiency. This paper also presents the approach for the practical application of the preprocessing of images and the systematic assessment in enhancing CNN-based handwritten digit recognition. © 2024 IEEE.

关键词： Convolutional neural networks

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Vader protocol based sentiment analysis technique using LSTM for Stock Trend prediction 14

Vader protocol based sentiment analysis technique using LSTM...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Ticku, Amrita Tripathy, Niva Mishra, Pankaj Kumar Sinha, Anurag Jadon, Shivangi Raj, Aditya Kulshrestha, Harsh Rai, Vipin Shamim, Rejuwan Bharti Vidyapeeth's College of Engineering India Driems Autonomous Engineering College Cuttack India Rungta College of Engg. & Technology R1 Department of Electronics and Telecommunication Engg. CG Bhilai India Ignou School of Computing and Information Science New Delhi India Galgotias University Dept. of Computer Science and Engineering Noida India Galgotias University Department of Computer Science and Engineering Noida India Maharishi University of Information Technology Dept. of Computer Science and Engineering with Data Science Noida India

ISBN: (纸本)9798350335095

Stock prediction has always been a difficult task, and numerous strategies for predicting stock market behavior have been offered. Researchers have recently begun to investigate the use of sentimental analysis and deep learning models such as Long Short-Term Memory (LSTM) to predict stock prices. We offer a stock prediction model that integrates sentimental analysis and LSTM in this research. The model considers the mood of stock market news items and use LSTM to forecast future stock prices. The results of our suggested model's evaluation on the S& P 500 dataset reveal that it outperforms traditional stock prediction approaches. © 2023 IEEE.

关键词： Forecasting

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A New Approach Towards Deployment and Management of Machine Learning Models Using K-serve Platform for Building Robust Machine Learning Systems 4

A New Approach Towards Deployment and Management of Machine ...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Gill, Kanwarpartap Singh Anand, Vatsala Gupta, Rupesh Khosla, Charu Hsiung, Pao-Ann Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dept. of Computer Science and Information Engineering National Chung Cheng university Chiayi County Taiwan

ISBN: (纸本)9798350305258

Kserve, commonly referred to as KFServing, is an open-source serving framework for machine learning (ML) models that runs on Kubernetes. enables the deployment, servicing, and management of ML models in a production environment in a scalable and effective manner. Kserve resources make it easier to develop scalable and reliable machine learning serving systems by streamlining the deployment and management of ML models in production. With contributions from several organisations and developers in the ML community, it is a dynamic open-source project. The influence that Kserve's work has had on the Data science community is extensively covered in this research. This research also discusses the Kserve design, elements, and features. The additional Kserve platforms that aid in the deployment and administration of machine learning models for creating reliable machine learning systems have also been brought to light. © 2023 IEEE.

关键词： Artificial Intelligence Data science Deep Learning Docker Kserve Kubernetes Machine Learning

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A Review on Federated Learning approach in Artificial Intelligence 6

A Review on Federated Learning approach in Artificial Intell...

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6th International Conference on Computing, Communication, Control and Automation, ICCUBEA 2022

作者： Govindwar, Ganesh D. Dhande, Sheetal S. Sipna Coet Dept. of Information Technology Amravati India Sipna Coet Dept. of Computer Science and Engineering Amravati India

ISBN: (纸本)9781665484510

In today's era, there is exponential increase in generation of data due to high speed internet availability, use of various electronics gadgets and IOT Use of that data for decision making has been increased proportionally. Typically, Machine Learning (ML) process has been done by collecting the data centrally on the cloud or server from each connected client nodes for training a model of machine learning which will be referred as global model. Then it is distributed and applied to all devices for generating expected throughput. The issue is that, user data is personal and confidential. As there is increase in volume, velocity and veracity of data that directly increases the concerns of data privacy. Collection of accurate data is the major concern for the classical centralized machine learning process of training and building ML prototypes with high efficiency, because in this method client must have to provide their private information to server. So here we are proposing an approach of federated learning which will be the interchange of local learning update parameters with the global learning model. Federated Learning is based on machine learning in which it trains an algorithm over multiple decentralized devices using local data, without transferring the data from client to server. The main proposition behind federated learning implementation is to bring the centralized model parameter to the connected decentralized devices and so it abolish the need of uploading user data on server. As users personal data detain on the client node itself, this helps protect data from its misuse and ensure security, as only the prototype result parameter are shared with the global model. This machine learning privacy enhancement is groundbreaking and opens up new ways for machine learning systems to handle sensitive data. © 2022 IEEE.

关键词： Sensitive data

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Water Quality Prediction using Classification techniques on XGBoost, KNeighbors, SVC, Random Forest, AdaBoost, and GaussianNB Classifier

Water Quality Prediction using Classification techniques on ...

引用

2023 IEEE Global Conference on information Technologies and Communications, GCITC 2023

作者： Gill, Kanwarpartap Singh Tuteja, Gaurav Anand, Vatsala Gupta, Rupesh Hsiung, Pao-Ann Chitkara University Institute of Engineering and Technology Chitkara University Punjab India National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi County Taiwan

ISBN: (纸本)9798350308167

A key environmental problem that is extensively researched for academic purposes is water contamination. To understand the origins, impacts, and potential solutions to water contamination, researchers look into a variety of issues. Environmental science, chemistry, biology, engineering, and policy are essential for study of water pollution. Classifiers as proposed help create early warning systems for probable pollution events by categorising water quality based on previous and current data. These systems aid in prompt reactions to occurrences of pollution prevention or mitigation. It is essential to tackling one of the most urgent issues affecting the wellbeing of ecosystems and human communities. By training models to predict the degree of pollution or the category of pollutants based on input characteristics, machine learning classifiers can classify water contamination. This study focuses on XGBoost, KNeighbour, SVC, Random Forest, Adaboost, and GaussianNB classifiers' various classification methodologies for predicting water quality. The XGBoost classifier, which may be used for future study, emerges as the best classifier out of all of these. © 2023 IEEE.

关键词： Artificial Intelligence Classification Data science Deep Learning Machine Learning ROC Curve Water Pollution

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FloraNet: An Approach to Prediction of Sea Buckthorn Disease based on a Convolutional Neural Network and a Random Forest Method 5

FloraNet: An Approach to Prediction of Sea Buckthorn Disease...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Pushpakumari, J. Bhaskar, Nuthanakanti Divya, G. Kanthi, Murali Raju, K. Srujan Narasimharao, Jonnadula VNRVJIET Telangana Hyderabad India Dept. of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India Dept. of Information Technology CMR Technical Campus Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India

ISBN: (纸本)9798350361155

In the current study, FloraNet was used to present an improved method for diagnosing leaf diseases in Sea Buckthorn. In the proposed model, Convolutional Neural Networks (CNN) and Random Forests (RF) are integrated. Essentially, CNN architecture is composed of a pair of convolutional layers, followed by a max-pooling layer;it is then followed by another pair of convolutional layers, before finishing off with a final pair of convolutional layers. Performance indicators such as Precision, Recall, F1-Score, or Accuracy are used to determine the performance of the model in terms of its performance. There were five disorders under investigation: Phakopsora, Erysiphe, Septoria, Colletotrichum, Phoma, as well as viral infections. Overall, the results show that the Micro Average accuracy for all classes is 95.11 %. According to the results, the individual accuracy ranges from 0.98 to 0.99 for each illness, while the F1-Scores range from 94.02% to 95.91% for each illness. This study concludes that the weighted average accuracy of 95.11 demonstrates a high level of proficiency of the model in identifying diseases, and indicates that the model is capable of identifying a wide variety of diseases. By using this integrated approach, agricultural scientists and researchers are able to detect Sea Buckthorn leaf diseases. © 2024 IEEE.

关键词： Diseases

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IoT Systems for Healthy and Safe Life Environments 7

IoT Systems for Healthy and Safe Life Environments

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7th IEEE Forum on Research and Technologies for Society and Industry Innovation, RTSI 2022

作者： Capuzzo, Martina Zanella, Andrea Zuccotto, Maddalena Cunico, Federico Cristani, Marco Castellini, Alberto Farinelli, Alessandro Gamberini, Luciano Research Centre Italy University of Padova Dept. of Information Engineering Italy University of Verona Dept. of Computer Science Italy

ISBN: (数字)9781665497398

ISBN: (纸本)9781665497398

The past two years have been sadly marked by the worldwide spread of the SARS-Cov-19 pandemic. The first line of defense against this and other pandemic threats is to respect interpersonal distances, use masks, and sanitize hands, air, and objects. Some of these countermeasures are becoming part of our daily lives, as they are now considered good practices to reduce the risk of infection and contagion. In this context, we present Safe Place, a modular system enabled by Internet of Things (IoT) that is designed to improve the safety and healthiness of living environments. This system combines several sensors and actuators produced by different vendors with self-regulating procedures and Artificial Intelligence (AI) algorithms to limit the spread of viruses and other pathogens, and increase the quality and comfort offered to people while minimizing the energy *** discuss the main objectives of the system and its implementation, showing preliminary results that assess its potentials in enhancing the conditions of living and working spaces. © 2022 IEEE.

关键词： COVID-19

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Adaptive Strategies for Scalable and Heterogeneous VANET Network 5

Adaptive Strategies for Scalable and Heterogeneous VANET Net...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Thangam, S. Jha, Priyanshu Hawari, Jawed Sah, Nabin Kumar Jain, Nikunj Jesy Janet Kumari, J.J. Saleem Raja, A. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India College of Computing and Information Sciences University of Technology and Applied Sciences It Department Shinas Oman

ISBN: (纸本)9798350385649

Despite technological advancements, Vehicular Ad-hoc Networks (VANETs) continue to face challenges related to reliability and high-speed mobility. This study focuses on enhancing scalability, improving mobility, and addressing network heterogeneity in the context of data dissemination. Data dissemination involves the distribution or transmission of statistical data to users. Tools like NS3, SUMO utilizing OSM for realistic traffic simulations, MATLAB for Data Analysis and evaluation of various protocols like ADHOC, Dynamic Source Routing (DSR), Optimized Link State Routing Protocol (OLSR), Destination Sequenced Distance Vector Routing (DSDV). The research aimed to refine VANET simulations for effective decision making, as its primary objective data dissemination i.e. distribution or transmission of statistical data to users, forming the central focus of this investigation. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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