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Certain Investigations on Cognitive based Movie Recommendation system using Pairwise Cosine Similarity 9

Certain Investigations on Cognitive based Movie Recommendati...

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9th international conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Sumathi, S. Madhumitha, S. Sushma, R. Suriya, M. Subitsha, M. Swetha, S. Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India Sri Eshwar College of Engineering Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350397376

The Internet serves as a major source in various sectors to communicate and share data. It is being used by every individual across the world for several purposes. The main objective of the recommender system is to provide users with suitable products and information. The movie recommendation system also helps in providing customized decisions to the user, thus improving the experience of customer or user. Content-based filtering manipulates item attributes to suggest other items that are relevant to user likes, this suggestion is made using past actions of customers or users. Cosine Similarity is an evaluation metric that helps in finding how similar the items are regardless of their dimensions. The proposed study, furnish users with suggestions that rest on the prevalence and category of a movie. Content based recommendation system is built using cosine similarity. The movie recommendation system has its separate algorithms for making suggestions by examining users' past data. Many attributes are considered like rating of each movie, name of the director and genre of the film. The system make recommendation by considering one attribute or mixture of attributes. For the proposed system, two different datasets used namely, the credits dataset and the movie dataset. The analysis of data is done with help of Python. © 2023 IEEE.

关键词： Python

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data Engineering Challenges in AI automation 6

Data Engineering Challenges in AI automation

引用

6th international conference on computing, Electronics and Communications Engineering, iCCECE 2023

作者： Reddy, Deexith Dallas TX United States

ISBN: (纸本)9798350340921

Artificial Intelligence (AI) systems have seen a meteoric rise in adoption, permeating across diverse sectors such as healthcare, finance, and e-commerce. The next step in the evolution of the practical use of AI is the automation of these systems. Since these systems hinge largely on the quality, volume, and careful management of data for training, optimization, and validation of data science models, the promise of AI automation hinges significantly on the ability to efficiently handle data ingestion, ensure real-Time processing, and respond swiftly to changes in *** is a requirement of a multi-faceted approach in terms of data engineering to help practitioners implement automated AI. The need for robust data engineering practices that ensure scalability, efficiency, and reliability in AI applications and their automation is evident. Comprehensive investigations of the impact of these practices on the automation of AI are required to create future-proof, reliable AI *** paper will highlight some areas of data engineering that are important for automation of AI, the challenges they pose and the impact they have on performance of AI systems. There is a solution section for each problem that gives the scope of automation and human involvement required along with the tools that are currently being used. © 2023 IEEE.

关键词： Automation

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Forecasting Future Sea Level Rise: A data-driven Approach using Climate Analysis 2

Forecasting Future Sea Level Rise: A Data-driven Approach us...

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2nd international conference on Edge computing and Applications, ICECAA 2023

作者： Dasavandi Krishnamurthy, Vinay Nagarad Degadwala, Sheshang Vyas, Dhairya Bosch MI United States Sigma University Computer Engineering Department Gujarat Vadodara India The Maharaja Sayajirao University of Baroda Gujarat Vadodara India

ISBN: (纸本)9798350347579

This research article presents a data-driven approach for predicting future sea level rise using climate data analysis. By employing advanced statistical techniques and machine learning algorithms, the study establishes correlations between historical climate variables and observed sea level rise. Ensemble modeling techniques are utilized to explore uncertainties and generate multiple simulations, offering a range of potential outcomes. The findings provide valuable insights for policymakers and coastal communities, enabling informed decision-making and the development of effective strategies to address the challenges posed by rising sea levels. Overall, this research contributes to the field of climate science by providing a robust framework for predicting sea level rise and preparing for its impacts in a changing climate. © 2023 IEEE.

关键词： Forecasting

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Impact of multi-GPUs and hyperparameters on parallel algorithms based on pytorch distributed 3

Impact of multi-GPUs and hyperparameters on parallel algorit...

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3rd international conference on Signal processing and Machine Learning, CONF-SPML 2023

作者： Wei, Shiyu SWUFE-UD Institute of Data Science Southwestern University of Finance and Economics Chengdu610000 China

Neural networks, as powerful models for solving many genuine problems, are suffering from the issue of being too computationally intensive due to the excessive size of the models themselves and the large datasets used for training. This paper's work focuses on a parallel algorithm, experimented on the classical convolutional neural network LeNet-5, aiming to reduce the time it takes for the neural network to train. This applies a massively parallel GPU structure to take full advantage of its high computing power. Among the approaches to improve the performance of parallel algorithms, many efforts have been made to improve the design of parallel structures, while this work focuses more on the enhancement of parallel algorithms by reasonable settings of neural network hyperparameters. This paper tries to approach (1) reducing the running time by testing parallelism with different numbers of GPUs and (2) further investigating the relationship between parallelism and model accuracy degradation and experimentally deriving a parallel scheme that can reduce the training time while guaranteeing accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Graphics processing unit

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Scout:An Efficient Federated Learning Client Selection Algorithm Driven by Heterogeneous data and Resource 14

Scout:An Efficient Federated Learning Client Selection Algor...

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IEEE 14th international conference on Joint Cloud computing (JCC)

作者： Zhang, Ruilin Xu, Zhenan Yin, Hao Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350328554

Federated Learning is a novel distributed machine learning paradigm that leverages the computing power of numerous decentralized data sources for jointly training machine learning models while ensuring user privacy. In the most commonly used cross-device scenarios, the client cluster typically cover a vast number of heterogeneous end devices. Due to physical limitations such as bandwidth, only a few clients can participate in each round of training. The core issue of the client selection is to determine an appropriate client set for each training round. However, existing selection algorithms, especially the widely adopted random selection, suffer from a number of issues that prevent them from achieving a good balance between training efficiency and speed. Therefore, we propose Scout, which utilizes the heterogeneity features of clients' data and resources to jointly model the utility function, and enhances the utilization of correlation among clients and the diversity among selected clients to achieve better training efficiency and speed. Furthermore, Scout maintains the scalability and fairness. Our experiments demonstrate that in large-scale heterogeneous clients scenarios, Scout outperforms three baseline algorithms and the state-of-the-art dual-feature dimension algorithm Oort in evaluation metrics.

关键词： federated learning client selection combinatorial optimization utility function

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Prediction of Smart Grid Stability Utilizing Machine Learning algorithms 3

Prediction of Smart Grid Stability Utilizing Machine Learnin...

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3rd international Mobile, Intelligent, and Ubiquitous computing conference, MIUCC 2023

作者： Bayomi, Ghada Hamdy, Alaa Hasheem, Mohamed Ali, Ahmed.A. Misr International University Cairo Aast Arab Academy for Science Technology & Maritime Transport Egypt Helwan University Cairo Egypt

ISBN: (纸本)9798350306231

The smart grid encountered numerous issues when attempting to continually monitor participant usages, bidirectional data flow, and utility costs as a result of the rise in the use of renewable energy sources. Specialists must detect system failure before it occurs and ascertain whether the smart grid is stable in order to achieve a robust, dependable smart grid. Conventionally in a smart grid, data on consumer demand is gathered and centrally evaluated against current supply conditions where consumers are then given the resulting recommended pricing information so they can decide how much to use. Because the entire traditional process is time-sensitive, dynamically evaluating grid stability becomes not only a concern but also an absolute necessity. Power supply and demand need to be balanced in order to maintain grid stability. Thus, machine learning might significantly aid in classifying whether or not the smart grid is stable by applying contemporary data mining approaches. In this research, different machine learning algorithms will be applied to assess and predict grid stability. The algorithms used in this paper are XG-classifier, Random Forest, Neural Network, KNN, SVM. The experiment results show that the XG-classifier achieved the highest performance yielding Classification accuracy, AUC, F1 and Recall of 97.79%, 96.7%, 95.6%, 95.4%, 94.5% respectively. © 2023 IEEE.

关键词： Smart power grids

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Enhancing Recommendation Quality in Dense data Environments: An Improved Collaborative Filtering Approach

Enhancing Recommendation Quality in Dense Data Environments:...

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2023 international conference on algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Huang, Kangpeng Feng, Feng School of Information Engineering Ningxia University Yinchuan750021 China

ISBN: (纸本)9781510671881

In recent years, the proliferation of smartphones and advancements in internet technology have led to the emergence of various internet applications, resulting in a surge in data volume. However, this expansion has also brought about information overload. To address this issue, recommendation systems have gained prominence, leveraging recommendation algorithms to extract user-relevant items from extensive datasets based on user interests. These systems have found applications in various domains such as e-commerce and social networks. However, most collaborative filtering algorithms are tailored for sparse user-item rating matrices, neglecting scenarios with densely rated popular items by heavy users. This paper presents an improved collaborative filtering algorithm for dense data scenarios. It utilizes the k-nearest neighbors (KNN) approach, focusing on user rating predictions to tackle challenges arising from dense data environments. The proposed algorithm divides user ratings into liked and disliked item sets, enhancing prediction accuracy by calculating similarities separately. Experimental evaluations are performed using a real-world dataset from the "Kuaishou" video sharing app, demonstrating the algorithm's superiority in terms of F-Score compared to traditional collaborative filtering methods. The algorithm's simplicity, scalability, and performance are highlighted, showcasing its effectiveness in addressing recommendation challenges in real-world settings. Future research directions could explore its applicability across diverse datasets and domains. © 2023 SPIE. All rights reserved.

关键词： Recommender systems

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Efficient Clumping of Wireless Sensor Networks using Mobile data Collector Built on Firefly Maximisation Model 14

Efficient Clumping of Wireless Sensor Networks using Mobile ...

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14th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Philip, Vinod K. Vaithianathan, V. JNN Institute of Engineering India SSN College of Engineering India

An effective transmission method is crucial to the long-term network life of a wireless sensor network. Energy constraints in sensor networks are critical since sensor nodes are often battery-powered and it is either impossible or extremely expensive to replenish them in most implementations. The early mortality of sensor nodes and indeed the network's lifespan are commonly caused by an imbalance in data flow in the communication channel. Proposed solutions address this issue and increase network longevity by altering transmission range based on distances between cluster chiefs and their members. In addition, a firefly optimization algorithm-based mobile data collector is used to extend the network's lifespan. For each case, the new method is compared against existing benchmark algorithms. Simulated findings show that the proposed technique beats current ones in terms of network lifespan. © Grenze Scientific Society, 2023.

关键词： Sensor nodes

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Architecture design of energy Internet regulation system based on cloud-edge collaboration 6

Architecture design of energy Internet regulation system bas...

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6th international conference on Renewable Energy and Power Engineering (REPE)

作者： Xu, Lei Zhang, Kaiyue Han, Xuehua Huang, Huang Ren, Hehe Wang, Qiang Jiang, Ning Coal Ind Co Ltd Jinan Coal Technol Res Inst Jinan Peoples R China Beijing Inst Petrochem Technol Beijing Peoples R China Qingdao Inst Ind Software Qingdao Peoples R China

ISBN: (纸本)9798350306194;9798350306187

With the emergence of a large number of energy prosumer units, the edge side of the regulation system has become an intelligent computing unit, which has transformed the energy Internet control mode from centralized to decentralized centralized collaborative operation. Combined with the theory of cloud-edge collaborative computing, this paper analyzes the data transmission characteristics of the energy Internet regulation system, and designs the regulation architecture of cloud-edge collaboration, including information collection, edge computing, core computing, management control, business applications and security auxiliary layers. According to the real-time requirements of the regulation and control business, the energy Internet regulation business processing process is designed, and the typical business scenarios of the energy Internet are sorted out and analyzed. The simulation results show that the real-time data transmission of the energy Internet regulation system based on cloud-edge collaboration has been greatly improved, which can ensure the operation reliability of the new energy Internet service scenario.

关键词： dispatch and control cloud-edge collaboration business scenarios data transmission

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Image Fusion using Xilinx System Generator for MRI and CT Medical Image Modalities 5

Image Fusion using Xilinx System Generator for MRI and CT Me...

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5th international conference on Emerging Smart computing and Informatics, ESCI 2023

作者： Neelima, K. Meruva, Kumar Raja Subhas, C. School of Engineering Mohan Babu University Department of Ece Tirupati India Aws Data Engineer Capgemini Hyderabad India Jntua College of Engineering Kalikiri India

ISBN: (纸本)9781665475242

This paper aims at development of Fusion algorithms for Multimodal Images using Xilinx System Generator for ease of using available built-in Field Programmable Gate Array based hardware algorithms for image fusion. This facilitates the development of new enhanced algorithms easily by using built-in blocks. Further this reduces the design effort, enhances hardware utilization. Further the comparison parameters like mean, standard deviation, peak signal to noise ratio, variance, root mean square error, kurtosis etc are compared with the existing fusion algorithms like DWT, SIDWT, PCA, DCT, etc. Xilinx ISE 14.5 is used as synthesis tool for Zynq 7000 Series 28nm FPGA board with part number XC7Z100-1FFG1156. The existing and modified architectures are implemented using Xilinx System Generator (XSG) as a cosimulation with MATLAB 2015a Simulink workspace. The proposed XSG based FPGA Image Fusion Algorithm proves to be a better choice of implementation with a scope of reconfigurability. © 2023 IEEE.

关键词： Magnetic resonance imaging

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