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Movie Recommendation systems using Machine Learning: An Empirical Study 2

Movie Recommendation Systems using Machine Learning: An Empi...

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2nd international conference on Advancements in Smart, Secure and intelligent computing, ASSIC 2024

作者： Lourembam, Elton Kipgen, L. Prince Mazumder, Monojeet Ahmed, Syed Sazzad Assam Don Bosco University Department of Computer Science & Engineering Guwahati India Assam Don Bosco University Department of Cse Guwahati India

ISBN: (纸本)9798350370188

Traditional recommendation systems help users find relevant content or products by predicting user preferences based on historical data. This project focuses on analysing and comparing different machine learning-based recommendation systems for movies. We have examined the effectiveness of some of the techniques used in recommending movies to users. A dataset consisting of movie ratings and user information is used to examine the recommendation system's performance. Our study aims to provide insights into different movie recommendation techniques and to identify the most effective approach. After implementation, we have evaluated their performance and did a comparative analysis. Our study reveals that model based collaborative filtering gives the best hit ratio of 0.96 followed by content based filtering. Comparatively, lower hit ratio was found in memory based filtering recommendation systems. © 2024 ieee.

关键词： Recommender systems

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Ant Colony Optimization for Network Customization in Cognitive Human-Computer Communication 1

Ant Colony Optimization for Network Customization in Cogniti...

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1st ieee international conference on Cognitive Robotics and intelligent systems, ICC - ROBINS 2024

作者： Khan, Shamim Ahmad Pande, S.S. Al Ansari, Mohammed Saleh Siddiqui, Asfar H. Abdurasul, Bobonazarov Ponni Valavan, M. Glocal University U.P India IT Pict Pune India University of Bahrain Department of Chemical Engineering Bahrain Yeshwantrao Chavan College of Engineering Department of Science and Humanities Maharashtra Nagpur India Turin Polytechnic University Tashkent Uzbekistan K. Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Trichy India

ISBN: (纸本)9798350372748

In the era of cognitive computing, seamless and efficient human-computer communication is paramount for enhancing user experience and system performance. This paper introduces a novel approach, the Ant Colony Optimized-Long Short-Term Memory (ACO-LSTM) framework, designed for network customization to optimize communication channels in cognitive computing environments. Inspired by the collective intelligence of ant colonies, the ACO-LSTM framework combines the adaptive learning capabilities of LSTM neural networks with the optimization prowess of ant colony algorithms. The ACO-LSTM framework operates by leveraging ant-like agents to explore and exploit the solution space for optimal network configurations. These agents dynamically adapt the communication pathways based on environmental cues and user interactions, ensuring adaptability to changing conditions. The LSTM component of the framework captures temporal dependencies and patterns in the communication data, facilitating intelligent decision-making for network customization. To validate the efficacy of the proposed approach, experiments were conducted in diverse cognitive computing scenarios. The proposed ACO-LSTM model shows better accuracy with 97.9% which is 13.1% higher when compared with other methods. The integration of ant colony optimization and LSTM provides a synergistic solution for optimizing network configurations in cognitive human-computer communication, offering a promising avenue for advancing the field of intelligent systems. © 2024 ieee.

关键词： Human computer interaction

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intelligent Transportation systems IoT Solutions for Traffic Optimization and Autonomous Vehicle Networks

Intelligent Transportation Systems IoT Solutions for Traffic...

引用

2024 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Donthi, Ranadheer Thiyagarajan, V.S. Arunjayakar, S. Amutha, M. Kholikov, Alisher Soataliyev, Rakhimjon St.Martin's Engineering College Department of Mathematics Telangana Secunderabad India Karpaga Vinayaga College of Engineering and Technology Department of Cse Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Electronics and Instrumentation Engineering Tamil Nadu Sathyamangalam India Sona College of Technology Department of Ece Tamil Nadu Salem India Department of Construction and maintenance of automobile roads Tashkent state transport university Mirobod Tashkent Uzbekistan

ISBN: (纸本)9798331543617

Many existing frameworks to support intelligent Transportation systems (ITS) lean on static infrastructure and sparse real-time data, which have inefficiencies that may exacerbate safety concerns such as are manifested with the emergence of autonomous vehicles. In the paper, a novel IoT-enabled system combining vehicle-to-infrastructure (V2I) communication with complex machine Learning (ML) models is presented to develop an intelligent adaptive traffic control approach. The use of IoT sensors for collecting real-time data to predict traffic flow and improve road safety. Advances between 20 and up to for average travel time, and up-to-32 % reduction in the Traffic Congestion Index were reported from existing methods. Furthermore, accident levels have fallen by 33%, indicating increased safety. Overall, the proposed ITS architecture provides a robust solution for contemporary urban transportation with several fundamental differences from traditional systems without hindering or impeding autonomous vehicles to be integrated in a ubiquitous manner. © 2024 ieee.

关键词： Traffic congestion

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Real-time and Scalability of Smart Two Ticket System Based on Edge computing in Power Grid Management 2

Real-time and Scalability of Smart Two Ticket System Based o...

引用

2nd international conference on data Science and Information System, ICDSIS 2024

作者： Huang, Chengwei Zeng, Jia Zhang, Fayan Zhang, Yubo Zhang, Wei Power Supply Bureau Of Guangxi Hezhou Power Grid Co. Ltd. Hezhou China Jia Zeng Guangxi Power Grid Co. Ltd. Nanning China Electric Power Research Institute Of Guangxi Power Grid Co. Ltd. Guangxi Key Laboratory Of Intelligent Control And Operation And Maintenance Of Power Equipment Nanning China

ISBN: (纸本)9798350381665

With the continuous development of Internet of Things (IoT) technology, edge processing platforms for power system characteristics have become an important technology. At present, many professional information systems of State Grid Corporation of China are faced with the problem that information cannot be shared and collected. It is urgent to use the idea of the Internet of Things, high-performance edge computing and other methods to build an intelligent Internet of Things system. In this paper, a new intelligent Internet of Things system based on edge computing was studied according to the application requirements of smart grid for dynamic control, strategy management, data preprocessing, etc., aiming to reduce network transmission capacity, reduce operating costs, and further optimize equipment maintenance costs. Simulation experiments have shown that when the number of Centralized Unit of Execution and Heterogeneous Unit of Execution in the network was increased to 70 and 100, respectively, the unloading completion times of the schemes 'HUE Cloud Edge' and 'HUE-Cloud Edge GM' were between 0.25-0.45 and 0.2-0.4, respectively, which were lower than those of cloud and HUECloud Randomness. © 2024 ieee.

关键词： Edge computing

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Practice and Exploration of Multidimensional Resource Scheduling and Optimization in Cloud computing Big data Environment

Practice and Exploration of Multidimensional Resource Schedu...

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2024 international conference on Interactive intelligent systems and Techniques, IIST 2024

作者： Yang, Yue Shanghai Aurora Vocational College Teachers' Career Development Center Shanghai China

ISBN: (数字)9798350374421

ISBN: (纸本)9798350374421

In the cloud computing big data environment, multi-dimensional resource scheduling and optimization play a crucial role. The dynamic resource supply based on virtual technology is one of the core features of cloud computing systems, and its "Pay as you go"service model brings flexibility and efficiency to users. However, facing challenges such as dynamic load characteristics, uneven resource utilization, and reliable workflow tasks, ensuring effective resource utilization and reliable task execution has become an urgent task. In response to the instability caused by dynamic load changes, this paper proposes a decomposition synthesis load prediction algorithm with error compensation. This algorithm uses statistical analysis and Kalman filtering techniques to decompose dynamic and complex host loads into simple task loads, and predicts and corrects them, thereby reducing the impact of uncertainty on scheduling and improving resource utilization efficiency. Secondly, in response to the imbalance of resource utilization, this article introduces a multidimensional resource scheduling mechanism that combines virtual machines and containers. This article provides an in-depth analysis of the dynamic characteristics of cloud environments and proposes corresponding solutions to address the challenges, demonstrating good results and providing useful references for the practice and exploration of multi-dimensional resource scheduling and optimization in cloud computing big data environments. © 2024 ieee.

关键词： Cloud platforms

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Unsupervised Pre-Training of Deep Neural Classifiers

Unsupervised Pre-Training of Deep Neural Classifiers

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ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Aliaksandr Kroshchanka Vladimir Golovko Shi Peiwen Zofia Lubańska Quintary AI Paphos Cyprus John Paul II University in Biala-Podlaska Biala-Podlaska Poland Jilin International Studies University Changchun City China

The paper is devoted to studying the issues of pre-training of deep neural network models. A generalized approach for pre-training deep models is proposed, which allows achieving better performance in final accuracy and can be used when training on small datasets. The effectiveness of the proposed approach is demonstrated on examples of training deep neural classifiers models using the MNIST and CIFAR-10 datasets. The obtained results can be used to train fully connected and convolutional types of layers.

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An optimal scheduling method for power data centre arithmetic resources based on edge computing 4

An optimal scheduling method for power data centre arithmeti...

引用

4th international conference on intelligent Power and systems, ICIPS 2024

作者： Zhang, Ximing Zhu, Jinwei Lin, Zhida Mao, Zhengxiong Wu, Wenpeng China Southern Power Grid Company Limited Guangzhou Guangdong 510623 China Design Institute Co. Ltd. China Energy Engineering Group Guangdong Electric Power China Guangdong Provincial Key Laboratory of Digital Grid Technology Guangdong Guangzhou510000 China Southern Power Grid Digital Grid Research Institute Co. Ltd China Guangdong Provincial Key Laboratory of Digital Grid Technology China

ISBN: (纸本)9798331532765

Traditional power scientific computing acceleration is mostly carried out in a single server or a simple server cluster, which is limited by computing resources and acceleration performance. Therefore, in this paper, the optimal scheduling of arithmetic resources from complex power scientific computing tasks to data centres is investigated. Firstly, through in-depth analysis of several important power science computing solution models, we extract their common acceleration kernel, i.e., the decomposition-coordination computing paradigm originated from the node conductivity matrix, in order to construct a new model of multi-task computation-communication equilibrium directed acyclic graph corresponding to it. Following the analysis of this task flow directed acyclic graph and the characteristics of the data centre network architecture, the proposed minimum cut binary tree algorithm is used to prioritize the mapping of the virtual machines where the key tasks are located to the physical machines, taking into account the computational time complexity of the tasks at each node in the task flow directed acyclic graph and the weight of the data interactions between the tasks, in order to minimize the overall solution time of the scientific computation. After the research on CloudSim data centre simulation platform, we conducted a series of hybrid running experiments on scientific computing for ieee 118 to case 13659-pegase very large-scale power systems. The experimental results show that our algorithms perform well in both lightly or heavily loaded data centre scenarios, and effectively improve the computational acceleration performance by increasing the physical machine resource utilization and reducing the data centre network throughput. These findings provide important theoretical guidance and technical support for solving the data centre real-time solution of complex power scientific computing for large-scale power grids. © 2024 ieee.

关键词： Trees (mathematics)

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Trading-Off Interpretability and Accuracy in Medical Applications: A Study Toward Optimal Explainability of Hoeffding Trees

Trading-Off Interpretability and Accuracy in Medical Applica...

引用

ieee international conference on Fuzzy systems (FUZZ-ieee)

作者： Sharma, Arnab Leite, Daniel Demir, Caglar Ngomo, Axel-Cyrille Ngonga Paderborn Univ Dept Comp Sci Paderborn Germany

ISBN: (纸本)9798350319552;9798350319545

With an increased number of applications of machine learning models to support decision-making in critical domains, there is a pressing need to understand the internal behavior of these models. Essentially, explaining learning models to humans has expedited the development of methods to extract information and access models' inner components. Researchers have proposed approaches to compute explanations for different types of models. While these approaches are often effective in explaining complex relations and rough input-output maps, studies on model optimality, which trade off accuracy and interpretability are scarce. We conducted a study to understand the relationship between accuracy and interpretability of Hoeffding trees, developed from evolving data streams. We employed formal reasoning techniques, founded on theoretical guarantees, to generate subset-minimal explanations for a set of examples. Rankings of features, according to their importance to the model outcomes, were obtained. After computing model accuracy and interpretability, the least important feature based on the ranking was removed. By repeating this procedure, we leveraged the setup that leads to an optimal (accurate and interpretable) tree model. Application examples considered medical datasets, namely the Parkinson's Disease telemonitoring and the EEG eye-state datasets, to generate Hoeffding regression and classification trees, respectively. The study has shown that as tree interpretability increases, accuracy tends to decrease;however, an optimal solution can be established by balancing conflicting aspects.

关键词： Explainable AI Incremental Learning Hoeffding Tree Evolving intelligent systems

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Cross-Domain Deterministic Communication System for Differentiated Concurrent Services Based on intelligent Compression 4

Cross-Domain Deterministic Communication System for Differen...

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4th international conference on Computer Science, Electronic Information Engineering and intelligent Control Technology, CEI 2024

作者： Gao, Chengtao Wu, Runze Guo, Haobo College of Electrical and Electronic Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798350378542

Under the background of the 'dual carbon' goal and related policies and the construction of new power systems, the large-scale deployment of sensing terminals and the explosive growth of the perception amount of data in the distribution system, the low transmission efficiency of the traditional distribution network has been unable to meet the needs of 'comprehensive perception and efficient collaboration'. How to achieve rapid transmission and real-time analysis of massive sensing data has become one of the main challenges of the new distribution network. Adding lossless compression to edge computing provides a way to transmit lightweight data at cloud edge. Considering the QoS requirements of differentiated concurrent services, this paper proposes an intelligent compressed cross-domain deterministic communication system for differentiated concurrent services. First, a cross-domain deterministic communication system model for differentiated concurrent services is constructed. The problem is optimized according to service QoS requirements and model constraints, and the service compression ratio is minimized under the condition that the service requirements are met. Then deep reinforcement learning method is used to solve the problem, and finally the proposed system effectively improves the certainty of massive distributed PV through simulation. © 2024 ieee.

关键词： DC distribution systems

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The Frontier of Geospatial AI Deep Learning Applications in data Mining and Spatial Analysis

The Frontier of Geospatial AI Deep Learning Applications in ...

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2024 ieee international conference on intelligent systems and advanced Applications, ICISAA 2024

作者： Mishra, Ajit Kumar Verma, Vikash Manivannan, R. Kumar, N. Krishna Srividya, Mudunuru Sharma, Sanskriti IES College of Technology Department of Electrical & Electronics Engineering M.P Bhopal462044 India GGITS Dept of Artificial Intelligence & Machine Learning M.P Jabalpur India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Computer Science & Engineering TN Chennai India Guru Nanak Institutions Technical Campus Ibrahimpatam Dept.of CSE Telangana Hyderabad India MLR Institute of Technology Dept. of CSE Telangana Hyderabad India GGITS Dept of Computer Science & Business System M.P Jabalpur India

ISBN: (纸本)9798331539948

Modern artificial intelligence uses geographical data mining and regional analysis to analyze large datasets. Deep learning is a new geographic AI method. Example: Geographic Deep Mining Network (GDMN). The Spatial Feature Extractor (SFE), Temporal Geospatial Attention Mechanism (TGAM), and Geospatial data Fusion Module (GDFM) comprise this technique. We examined six popular approaches and found GDMN performed best. It worked successfully for several reasons. GDMN always outperforms its competitors in memory, F1 score, IoU, training time, and inference time. This is always true. These findings suggest that GDMN might boost geospatial AI's data mining and location analysis abilities. After all the effort, we have developed a solid basis for location intelligence, making it useful in various subject areas. Because it's versatile, mapping specialists may use GDMN for better environmental monitoring or city development. Deep learning helps GDMN make better space choices and uncover insights in large, challenging data sets that no one knew about. GDMN's cutting-edge approaches make space and time research and prediction simpler. This wide perspective offers a more full and all-encompassing picture of today's challenges than previous techniques. This makes it impossible to overstate how significant this work is to location intelligence. Its unmatched research and insights usher in a new era. © 2024 ieee.

关键词： Adversarial machine learning

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