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Communication, computer sciences and Engineering (IC3SE), International Conference on

作者： Shweta Kumari Kapil Rajput Gurwinder Singh Arpit Jain Savya Sachi Manika Manwal Department of Computer Science & Engineering Shri Fanishwar Nath Renu Engineering College Araria Bihar India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of AITCSE Chandigarh University Mohali Punjab India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Information Technology L N Mishra College of Business Management Muzaffarpur Bihar India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Application specific Network on Chip (NoC) designs are quickly becoming the technology of choice for solving the problem of multiprocessor system architecture. Broadband, interposes communication, deadlock avoidance, port bandwidth, router architecture, and bespoke NoCs may all be improved by modelling and simulation of multilayer network structures and synthesis. Topology-based organization of 2D and 3D routers allows for the construction and laDyout of NoCs. The articles compare and contrast the design and analysis of two-dimensional and three-dimensional network-on-chip routers for setting up mesh topological nodes in a network. Xilinx 14.2 is used to execute the code. The most recent version of modalism, 10.0 student edition, is used to functionally mimic the program and modules. The experimental work and thorough testing on the Virtex-5 FPGA confirm the chip design.

关键词： Three-dimensional displays Organizations System recovery Reliability engineering Nonhomogeneous media Topology Telecommunications

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Remora Optimization with Machine Learning Driven Churn Prediction for Business Improvement

Remora Optimization with Machine Learning Driven Churn Predi...

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2023 International Conference on Intelligent Data Communication technologies and Internet of Things, IDCIoT 2023

作者： Kumar, Sumita Baruah, Pallavi Kirubakaran, S. Kumar, A. Sasi Singh, Kamlesh Reddy, M.V. Jagannatha Department of Engineering and Technology Navi Mumbai India Department of Computer Science and Electronics University of Science and Technology Meghalaya India Department of Cse Cmr College of Engineering and Technology Kandlakoya Telangana Hyderabad India Mentor-IT - Inurture Education Solutions Pvt Ltd Bangalore Department of Cloud Technology and Data Science Institute of Engineering and Technology Srinivas University Srinivas Nagar Mukka Surathkal Dakshina Kannada Karnataka Mangalore India Department of Computer Science and Engineering Graphic Era Hill University Dehradun Campus Dehradun India Department of Cse Gandhi Institute of Technology and Management Gitam Deemed to Be University Bengaluru Campus Karnataka Doddaballapura India

ISBN: (纸本)9781665474511

Customer Relationship management (CRM) is a complete approach to constructing, handling, and establishing loyal and long-lasting customer relationships. It is mostly acknowledged and widely executed for distinct domains, e.g., telecom, retail market, banking and insurance, and so on. A major objective is customer retention. The churn methods drive to recognize early churn signals and identify customers with an enhanced possibility to leave voluntarily. Machine learning (ML) techniques are presented for tackling the churning prediction difficult. This paper presents a Remora Optimization with Machine Learning Driven Churn Prediction for Business Improvement (ROML-CPBI) technique. The aim of the ROML-CPBI technique is to forecast the possibility of customer churns in the business sector. The working of the ROML-CPBI technique encompasses two major processes namely prediction and parameter tuning. At the initial stage, the ROML-CPBI technique utilizes multi-kernel extreme learning machine (MKELM) technique for churn prediction purposes. Secondly, the RO algorithm is applied for adjusting the parameters related to the MKELM model and thereby results in enhanced predictive outcomes. For validating the greater performance of the ROML-CPBI technique, an extensive range of experiments were performed. The experimental values signified the improved outcomes of the ROML-CPBI technique over other ones. © 2023 IEEE.

关键词： Machine learning

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Revolutionizing NPC Interaction: DQN with Huber Loss for Dynamic Behavior Modeling in Video Games

Revolutionizing NPC Interaction: DQN with Huber Loss for Dyn...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Haris, A.A. Ahamed Jeevitha, S. Das, Purushottam Munjal, Puja Bora, Ravi Kumar Kiran Kishore, O. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Chennai India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Virudhunagar India Graphic Era Hill University Graphic Era Deemed to Be University Department of CSE Uttarakhand Dehradun India Jagannath International Management School Department of Information Technology Delhi India GITAM School of Science Department of Mathematics Andhra Pradesh Visakhapatnam India N B K R Institute of Science and Technology Andhra Pradesh Vidyanagar India

ISBN: (纸本)9798350361537

This research aims to improve the interaction between non-player characters (NPCs) and players in video games. It proposes a new method that combines Deep Q- Networks (DQN) with Huber loss. Current strategies for simulating non-player character (NPC) behavior frequently encounter difficulties in successfully managing the trade-off between exploration and exploitation, leading to less than optimum user experiences. The solution we propose utilizes the advantages of DQN and Huber loss to overcome these restrictions. By utilizing Huber loss, our model attains resilience to anomalies and more gradual gradients, hence improving learning efficacy and steadiness. Moreover, the use of DQN enables flexible decision-making, resulting in more authentic NPC behavior. We have conducted thorough experiments to prove that our methodology is better to existing approaches. The average reward achieved by our system is 85, and it maintains a balanced ratio of 70% for exploration and 30% for exploitation. The learning curve reaches a point of convergence, suggesting consistent learning, while the value of the loss function lowers by 60%, exhibiting enhanced error reduction during the training process. The results highlight the efficacy of our suggested strategy in transforming NPC interaction in video games. © 2024 IEEE.

关键词： Decision making

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Imputation with Inter-Series Information from Prototypes for Irregular Sampled Time Series

arXiv

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arXiv 2024年

作者： Yu, Zhihao Chu, Xu Ma, Liantao Wang, Yasha Zhu, Wenwu Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China School of Computer Science Peking University Beijing China Department of Computer Science Tsinghua University Beijing China National Engineering Research Center of Software Engineering Peking University Beijing China

Irregularly sampled time series are ubiquitous, presenting significant challenges for analysis due to missing values. Despite existing methods address imputation, they predominantly focus on leveraging intra-series information, neglecting the potential benefits that inter-series information could provide, such as reducing uncertainty and memorization effect. To bridge this gap, we propose PRIME, a Prototype Recurrent Imputation ModEl, which integrates both intra-series and inter-series information for imputing missing values in irregularly sampled time series. Our framework comprises a prototype memory module for learning inter-series information, a bidirectional gated recurrent unit utilizing prototype information for imputation, and an attentive prototypical refinement module for adjusting imputations. We conducted extensive experiments on three datasets, and the results underscore PRIME’s superiority over the state-of-the-art models by up to 26% relative improvement on mean square error. Our code is available at https://***/yzhHoward/PRIME. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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Empowering Women: A Creative Approach to Integrated Safety with Machine Learning Algorithms

Empowering Women: A Creative Approach to Integrated Safety w...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Kiran, Ajmeera Sundaram, Ajith Varghese, Issac K Dhanasekaran, S. Ayyasamy, Ramesh Kumar Krisnan, Saraswathy MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Amrita School of Business Kochi Amrita Vishwa Vidyapeetham Amrita Nagar Edappally Kerala Ernakulam682041 India T A Pai Management Institute Manipal Academy of Higher Education Manipal India Kalasalingam Academy of Research and Education Deemed to be University Department of Information Technology Tamilnadu Srivilliputtur India Faculty of Information and Communication Technology Kampar Malaysia Kampar Kampar Malaysia

ISBN: (纸本)9798331540685

An extensive platform created to provide women with a sense of security and empowerment in their everyday lives is the Integrated Women's Safety Application. Users can use apps with assurance that their data is safe thanks to secure login functionality. The software offers users messaging warnings alerting them to potential hazards in their neighbourhood, as well as a panic button for emergency assistance in case of an emergency. In addition to offering directions, Route Navigation also indicates the locations of neighbouring police stations for an extra layer of security. A smooth and effective method for requesting assistance and recording hazardous circumstances is created by enabling users to report issues straight from the app. In addition, the heatmap visualization tool helps users make educated judgments about their travels by providing a visual depiction of safety ratings in various locations. The Integrated Women's Safety Application attempts to equip women with the tools to navigate the world confidently and safely by combining these essential aspects into a single platform. © 2024 IEEE.

关键词： Hazards

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Securing the Internet of Things with Block chain-based Network Security Protocols

Securing the Internet of Things with Block chain-based Netwo...

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International Conference on Computing and Networking Technology (ICCNT)

作者： T. Marimuthu Saumitra Chattopadhyay H. Summia Parveen Rishi Prakash Shukla Manika Manwal Vilas Nair Department of CSE School of Computing Kalasalingam Academy of Research and Education (Deemed to be University) Krishnankoil Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Computer Science Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Jaipuriya Institute of Management Jaipur India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India SCMS School of Technology and Management Cochin

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The Iot refers back to the network of physical gadgets, cars, and home equipment, which might be embedded with sensors, software program, and connectivity, allowing them to accumulate and trade facts. With the growing adoption of these gadgets, there is a growing subject for the safety and privacy of the information being transmitted thru them. Conventional community safety protocols are insufficient to shield massive-scale Iota deployments due to their centralization and vulnerability to cyber-attacks. Block chain generation gives an ability technique to the protection stressful situations of Iota. It is a dispensed ledger device that lets in relaxed and transparent transactions without the need for a government. In this summary, we suggest using block chain-based network safety protocols to comfy the Iota. Those protocols make use of the cryptographic competencies of block chain to ensure cozy device authentication and records integrity, and decentralized consensus mechanisms to save you the single thing of failure.

关键词： Privacy Scalability Government Network security Software Sensors Internet of Things Protection Cyberattack Contracts

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A new online field feature selection algorithm based on streaming data

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1365-1377页

作者： Zhang, Zhenjiang Song, Fuxing Zhang, Peng Chao, Han-Chieh Zhao, Yingsi School of Software Engineering Beijing Jiaotong University Beijing100044 China Department of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China Department of Electrical Engineering National Dong Hwa University Hualien97401 Taiwan School of Economics and Management Beijing Jiaotong University Beijing100044 China

The rapid development of Internet technology derived out a massive network text data. Therefore, how to classify the massive text data efficiently has important theoretical significance and application value. In order to acquire accurate classification results, the process has been divided into two parts. In terms of text representation, this paper proposes an online field feature selection algorithm (OFFS algorithm) based on streaming data, which solves the problems of low efficiency and memory consumption of traditional feature selection algorithms. With improvements in the vector space model, the new algorithm can select the real-time feature of the data and quickly generate text vector. In the aspect of classifier design, an OFFS-BP neural network text classifier based on BP neutral network and OFFS algorithm is designed. It adapts to the distributed parallel computing, reduces the training time and balances the computation efficiency and classification accuracy. Finally based on the Spark platform, the OFFS-BP neural network classifier is implemented. The experimental results show that the OFFS-BP neural network classifier is more suitable for big data environment with less computation time and higher classification efficiency. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Neural networks

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Spectral-Spatial Fusion Hypergraph Convolutional Network for Multispectral Point Cloud Classification 11

Spectral-Spatial Fusion Hypergraph Convolutional Network for...

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11th International Conference on Information Systems and Computing Technology, ISCTech 2023

作者： Huang, Jiangbo Wang, Qingwang Zhang, Zifeng Liu, Sivang Wang, Zhifeng Shen, Tao Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming China Kunming University of Science and Technology School of Information Engineering and Automation Kunming China Real Estate Resource Management Center General Logistics Department of Pla Beijing China Beijing Anlu International Technology Co Beijing China

ISBN: (纸本)9798350342406

Graph Neural Networks (GNNs) based on simple graph can directly model 3D multispectral point clouds, thus avoiding the problem of 3D spatial information loss when converting 3D point clouds to 2D feature images. However, considering the dense distribution of objects in remote sensing scenes, GNNs only consider the association between pairs of vertices, which is insufficient for exploring high-order correlations and long-term dependence correlations. Hypergraph neural network (HGNN) is a more generalized framework that overcomes the limitations of traditional simple graph. In this paper, we propose a spectral-spatial fusion hypergraph convolutional network (S2FHGCN) for 3D multispectral point cloud classification. S2FHGCN first generates two hypergraphs from multidimensional features including spectral feature and spatial feature, respectively. The hypergraph structure can better handle complex high-order correlations for representation learning. Then the two hypergraphs are fused to obtain global representation information. Finally, the multidimensional features and the fused hypergraph are input to the hypergraph convolution network. The experimental results on real multispectral point clouds demonstrate the effectiveness of our proposed S2FHGCN for multispectral point cloud classification. © 2023 IEEE.

关键词： Remote sensing

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Retraction Note: Algorithms for high mobility environment in 5G radio access networks with millimeter wave communications

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Goswami, Chandrashekhar Sharma, Priti Bharati, Rakesh Rajheshwari, K. C. Maguluri, Lakshmana Phaneendra Elangovan, Muniyandy Department of CSE School of Computing MIT ADT University Pune India Department of Information Technology Jagannath International Management School New Delhi India Department of Computer Science & Engineering BIT Gorakhpur India Department of Computer Science Engineering Sona College of Technology Salem India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of R & D Bond Marine Consultancy London UK

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Programming by Example Made Easy

arXiv

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arXiv 2023年

作者： Wu, Jiarong Wei, Lili Jiang, Yanyan Cheung, Shing-Chi Ren, Luyao Xu, Chang Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Electrical and Computer Engineering McGill University Montreal Canada State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Key Lab of High Confidence Software Technologies Ministry of Education Department of Computer Science and Technology EECS Peking University Beijing China

Programming by example (PBE) is an emerging programming paradigm that automatically synthesizes programs specified by user-provided input-output examples. Despite the convenience for end-users, implementing PBE tools often requires strong expertise in programming language and synthesis algorithms. Such a level of knowledge is uncommon among software developers. It greatly limits the broad adoption of PBE by the industry. To facilitate the adoption of PBE techniques, we propose a PBE framework called Bee, which leverages an "entity-action" model based on relational tables to ease PBE development for a wide but restrained range of domains. Implementing PBE tools with Bee only requires adapting domain-specific data entities and user actions to tables, with no need to design a domain-specific language or an efficient synthesis algorithm. The synthesis algorithm of Bee exploits bidirectional searching and constraint-solving techniques to address the challenge of value computation nested in table transformation. We evaluated Bee’s effectiveness on 64 PBE tasks from three different domains and usability with a human study of 12 participants. Evaluation results show that Bee is easier to learn and use than the state-of-the-art PBE framework, and the bidirectional algorithm achieves comparable performance to domain-specifically optimized synthesizers. Copyright © 2023, The Authors. All rights reserved.

关键词： Problem oriented languages

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