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28th International Conference on Database Systems for Advanced applications, DASFAA 2023

作者： Xuan, Hongrui Li, Bohan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Nanjing China National Engineering Laboratory for Integrated Aero-Space-GroundOcean Big Data Application Technology Nanjing China

ISBN: (纸本)9783031306716

Modeling various types of users’ interactions and jointly considering individual preferences from multiple perspectives, multi-behavior recommendation has attracted increasing attention recently. However, most existing multi-behavior recommendations only focus on the behavioral interaction itself, attempting to extract user preferences merely by modeling behaviors, while ignoring the properties of the interaction (e.g., the temporal information). Meanwhile, the introduction of multi-behavior information is bound to generate additional noise signals, which further affects the accuracy of recommendation. Thus, to tackle the above challenges, two aspects need to be explored: (1) How to fuse interaction attributes to construct unified cross-behavior dependencies while distinguishing subtle differences between individuals’ behaviors. (2) How to mitigate the effects of noise to a certain extent while preserving multi-behavioral information. In this work, we propose a Temporal-Aware Multi-Behavior Contrastive Learning (TMCL) framework to explore the patterns of multiple behaviors of individuals through temporal information, jointly capture the correlation of users’ preference evolution, and alleviate the impact of multi-behavior noise. Extensive experiments and ablation tests on the three datasets indicate our TMCL outperforms various state-of-the-art methods and verify the effectiveness of our model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： User profile

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A novel borda count based feature ranking and feature fusion strategy to attain effective climatic features for rice yield prediction

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Informatica (Slovenia) 2021年第1期45卷 13-31页

作者： Mishra, Subhadra Mishra, Debahuti Mallick, Pradeep Kumar Santra, Gour Hari Kumar, Sachin Department of Computer Science and Application CPGS Odisha University of Agriculture and Technology Bhubaneswar Odisha India Department of Computer Science and Engineering Siksha’O’ Anusandhan Deemed to be University Bhubaneswar Odisha India School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha India Department of Soil Science and Agricultural Chemistry IAS Siksha’O’ Anusandhan Deemed to be University Bhubaneswar Odisha India Department of Computer Science South Ural State University Chelyabinsk Russia

An attempt has been made in the agricultural field to predict the effect of climatic variability based on rice crop production and climatic features of three coastal regions of Odisha, a state of India. The novelty of this work is Borda Count based fusion strategy on the ranked features obtained from various ranking methodologies. Proposed prediction model works in three phases;in the first phase, three feature ranking approaches such as;Random Forest, Support Vector Regression-Recursive Feature Elimination (SVR-RFE) and F-Test are applied individually on the two datasets of three coastal areas and features are ranked as per the their algorithm. In the second phase;Borda Count as a fusion method has been implemented on those ranked features from the above phase to obtain top five best features. The multiquadratic activation function based Extreme Learning Machine (ELM) has been used to predict the rice crop yield using those ranked features obtained from fusion based raking strategy and the number of varying features are obtained which gives prediction accuracy above 99% in the third phase of experimentation. Finally, the statistical paired T-test has been used to evaluate and validate the significance of proposed fusion based ranking prediction model. This prediction model not only predicts the rice yield per hector but also able to obtain the significant or most affecting features during Rabi and Kharif seasons. From the observations made during experimentation, it has been found that;relative humidity is playing a vital role along with minimum and maximum temperature for rice crop yield during Rabi and Kharif seasons. © 2021 Slovene Society Informatika. All rights reserved.

关键词： Decision trees

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Two-Stream Holographic Convolutional Network for Speech Emotion Recognition Using Korean Audio 4

Two-Stream Holographic Convolutional Network for Speech Emot...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Venkateswaran, N. Mudadla, Balaji. Jasmine, R. Rajitha Nalini, J. Rajasekar, M. Jayasudha, T. Panimalar Engineering College Department of Master of Business Administration Tamil Nadu Chennai600123 India CMR Technical Campus KandlaKoya Medchal Telangana Hyderabad501401 India R.M.K. Engineering College Department of Information Technology Tamil Nadu Kavaraipettai601206 India Vignan's Institute of Engineering for Women Department of Computer Science Andhra Pradesh Visakhapatnam530049 India Saveetha College of Liberal arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Computer Science And Application Tamil Nadu Chennai602105 India Kpr Institute of Engineering and Technology Department of English Tamil Nadu Coimbatore641407 India

ISBN: (纸本)9798331529635

Speech Emotion Recognition (SER) has become an important area of research in the AI domain, helping systems comprehend people's feelings with speech, in such areas as healthcare, automated customer support, and virtual companionship. Emotion detection from Korean speech raises learning issues and demand an understanding of all high-frequency spectrum and time selective features. Despite that these layers work effectively, typical convolutional networks may lose phase information which is so important for emotion distinction especially in languages with tone and phonetics. For this research, the AI-Hub Speech Emotional Database, a large scale Korean speech dataset, is employed to create a Two-Stream Holographic Convolutional Network (TSHCN) for SER. However, the TSHCN architecture employed a two-path structure in which one branch encoded the spectral feature and the other branch encoded the temporal dynamics in sequence, while the phase information was retained using holodynamic convolutions for better classification of emotions. In order to increase the efficiency of the model, the Kookaburra Optimization Algorithm (KOA) is used on the parameters of the weight network, reducing the number of mistakes and the load of calculations as well as increasing the accuracy of the model. As more evident from the previously discussed results, SER performance is notably enhanced by the use of TSHCN with KOA;specifically, the accurate representation of Korean speakers' speech emotions will help pave the way for the development of SER for the Korean language and other complex languages as well. This study thus takes up the shortcoming in the conventional approaches to the task and proposes an optimal and language-independent SER model. The introduced approach attains higher accuracy as 99%. © 2024 IEEE.

关键词： Emotion Recognition

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Internet of things-based automation design and organizational innovation of manufacturing enterprises

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Materials Today: Proceedings 2022年 56卷 1769-1775页

作者： Babu, T. Roopa, H. Shukla, Arvind Kumar Stalin David, D. Jayadatta, S. Rajesh, A.S. Department of Instrumentation and Control Engineering St. Joseph's College of Engineering Chennai India Department of Information Science and Engineering Bangalore Institute of Technology Bangalore India Department of Computer Application IFTM University Moradabad India Department of CSE IFET College of Engineering Villupuram India Department of Management Studies KLE's IMSR BVB Campus Hubli India Department of Mechanical Engineering JSS S&T University Mysore India

The Internet of Things (IoT) foresaw a transparent network between the real and virtual worlds. IoT offers a promising potential to develop strong market and industrial applications. The export-oriented economy of industrial manufacturers relies on product and service creativity to retain a competitive advantage. The IoT-based applications of innovative manufacturing companies (IOTs), where most companies are involved and engaged in many of the case IoT-based applications, is motivated by computing capacity, networking, cost reduction and electronic components. A common motive is the Internet of Things applications and the improvement of after-sales service activities. The suggested Linear Product Design Regression (LPDR) algorithm classifies business product innovation design based on consumer input to support business development. Businesses, companies and the technology industry: the inhibitors hinder the development of the four IT applications. IoT offers one of the latest innovations in manufacturing to allow us to meet key challenges. The product design and development engineering are engaged in utilizing the Internet of Things (IoT) to create comprehensive policies and policy creation for manufacturing *** support business development. Businesses, companies and the technology industry: the inhibitors hinder the development of the four IT applications. To allow us to meet key challenges and to create comprehensive policies and policy creation for manufacturing companies. © 2021

关键词： Product design

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Healthcare management in Bigdata with Blockchain and IOT integrated System

Healthcare management in Bigdata with Blockchain and IOT int...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Shalini Kaliyahan Prachi Bhatnagar Shilpi Gupta Archana Jain Suraj Malik Saikat Gochhait Aditi Sharma Mahima Shankar Pandey Department of Computer Science and Application IIMT University Meerut Uttar Pradesh India Department of Computer Science and Engineering Meerut Institute of Technology Meerut Uttar Pradesh India Department of Computer Science and Engineering IIMT University Meerut Uttar Pradesh India Symbiosis Institute of Digital and Telecom Management Constituent of Symbiosis International Deemed University Pune India Department of Computer Science and Engineering Galgotia College of Engineering and Technology Greater Noida Uttar Pradesh India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

In 21 st decade, healthcare is one of the biggest challenges, and the current scenario very flexible to adapt the effective execution technology solutions like internet of things (IoT). Through this a patient may access the treatment remotely and rapidly. Most suitable and effective applications need complex data of personal care, and reliability among the entire participated component or parties. Blockchain technology has been captured the industry domain with its computation and trusted model among participated parties. This paper presents a data storage decision framework for managing blockchain storage using on-chain and off-chain methods. There is a need for making balanced decisions between the very expensive on-chain storage methods and the less secure off-chain storage, towards optimizing blockchain storage costs.

关键词： Industries Runtime Memory Medical services Time measurement Real-time systems Blockchains Internet of Things Reliability Secure storage

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-Net: Adaptively Aligned Multi-scale Moment for Few-Shot Action Recognition

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International Journal of computer Vision 2025年 1-16页

作者： Gao, Zilin Wang, Qilong Zhang, Bingbing Hu, Qinghua Li, Peihua School of Information and Communication Engineering Dalian University of Technology Dalian China College of Intelligence and Computing Tianjin University Tianjin China Haihe Laboratory of Information Technology Application Innovation Tianjin China School of Computer Science and Engineering Dalian Minzu University Dalian China

Thanks to capability to alleviate the cost of large-scale annotation, few-shot action recognition (FSAR) has attracted increased attention of researchers in recent years. Existing FSAR approaches typically neglect the role of individual motion pattern in comparison, and under-explore the feature statistics for video dynamics. Thereby, they struggle to handle the challenging temporal misalignment in video dynamics, particularly by using 2D backbones. To overcome these limitations, this work proposes an adaptively aligned multi-scale second-order moment network, namely $$\text {A}^2\text {M}^2$$ -Net, to describe the latent video dynamics with a collection of powerful representation candidates and adaptively align them in an instance-guided manner. To this end, our $$\text {A}^2\text {M}^2$$ -Net involves two core components, namely, adaptive alignment ( $$\text {A}^2$$ module) for matching, and multi-scale second-order moment ( $$\text {M}^2$$ block) for strong representation. Specifically, $$\text {M}^2$$ block develops a collection of semantic second-order descriptors at multiple spatio-temporal scales. Furthermore, $$\text {A}^2$$ module aims to adaptively select informative candidate descriptors while considering the individual motion pattern. By such means, our $$\text {A}^2\text {M}^2$$ -Net is able to handle the challenging temporal misalignment problem by establishing an adaptive alignment protocol for strong representation. Notably, our proposed method generalizes well to various few-shot settings and diverse metrics. The experiments are conducted on five widely used FSAR benchmarks, and the results show our $$\text {A}^2\text {M}^2$$ -Net achieves very competitive performance compared to state-of-the-arts, demonstrating its effectiveness and generalization.

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Timetable Generator for Educational Institute Using Genetic Algorithm

Timetable Generator for Educational Institute Using Genetic ...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Chandradeep Bhatt Divyanshu Chaurasiya Rahul Chauhan Teekam Singh Sanjay Sharma Dev Baloni CSE Department Graphic Era Hill University Dehradun India Computer Science and Engineering Graphic Era Hill University Dehradun India Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Computer Application Department SGRR Univeristy Dehradun India Computer Science and Engineering Quantum University Dehradun India

Genetic algorithms are effectively capable to find out solution of a variety of challenging actual situations. They are heuristic search and optimization methods influenced by natural evolution. The process of making a schedule has been quite time- and labor-intensive. To create a schedule, a lot of time and effort must be invested. Timetables are produced for a wide range of reasons, including class scheduling at educational institutions, developing timing charts for buses and trains, and many more. A schedule requires a lot of effort and time to create. In our research, we tried to reduce the difficulties of generating a schedule using a genetic approach. We may create timetables that are quicker to create while still being more precise, exact, and error-free by using genetic algorithms.

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Prediction of Fetal Brain Anomalies Using Deep Learning Models

Prediction of Fetal Brain Anomalies Using Deep Learning Mode...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Venkateshwari Subburaj Pandiselvam Pandiyarajan Department of Computer Application School of Computing Kalasalingam Academy of Research and Education Krishnankoil TamilNadu India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil TamilNadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Fetal brain anomaly prediction is important for fetal medicine, as well as for prenatal health care. Fetal anomalies are classified into two types, anomalies in the fetus' body parts including heart, lung, and kidney anomalies in facial features. Thus, to predict the anomalies in the fetal brain, various deep-learning models are used in this paper. The LeNet, AlexNet, ResNet, VGGNet, GoogLeNet and ZFNet were used. From the results obtained, the ResNet method achieved 99% accuracy with the given 3D ultrasound images of the fetal brain compared with other models and benchmark algorithms like SVM, Logistic regression, and KNN. The results of this paper are useful to identify fetal anomalies. Hence, this approach will be useful for improving outcomes for individuals and society as much as possible.

关键词： Deep learning Support vector machines Solid modeling Pediatrics Three-dimensional displays Accuracy Ultrasonic imaging Predictive models Brain modeling Prediction algorithms

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A Reinforcement Learning-Based Image Mapping-Topology Optimization for Antenna Design

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Dou, Jiangling Gong, Hao Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Kunming University of Science and Technology Faculty of Information Engineering and Automation Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming Yunnan650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College China

A Reinforcement Learning (RL)-based Image Mapping Topology (IMT) optimization approach is proposed. To enable RL to optimize antenna topology and increase the efficiency of the optimization process. Two crucial components, Stratified Sampling Balance (SSB) and IMT are introduced. SSB is utilized to balance different types of data samples. IMT is utilized to facilitate the interaction between RL and simulation software, it also pretrains the actor network within the RL algorithm. This methodology allows the RL agent to acquire knowledge from the interaction of the antenna topology with the environment. Subsequently, the agent outputs antenna topology with the maximum reward signal. Compared with other machine learning-based optimization methods, the proposed method significantly alleviates the issue of learning inefficiency and reduces the exploration time for feasible solutions. To validate the effectiveness of the optimization method, a microstrip antenna is optimized. The measurement results show that the impedance bandwidth is doubled while maintaining the realized gain with fewer simulation samples and less optimization time. © 2025 IEEE.

关键词： Reinforcement learning

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Timeliness-Selective Incentive Federated Crowdsourcing

Timeliness-Selective Incentive Federated Crowdsourcing

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IEEE International Conference on Web Services (ICWS)

作者： Xiaoqian Jiang Haiyang Diao Cangqi Zhou Jing Zhang School of Cyber Science and Engineering Southeast University China Siemens (China) Co. Ltd. School of Computer Science and Engineering Nanjing University of Science and Technology China Engineering Research Center of Blockchain Application Supervision and Management (Southeast University) Ministry of Education China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

Crowdsourcing offers an efficient means of gathering labeled data and training machine learning models. However, the exposure of crowd workers to potential privacy breaches during data collection diminishes their willingness to participate. This paper proposes TsIFedCrowd, a novel incentive mechanism within the federated learning paradigm, aimed at mitigating privacy risks for crowd workers while achieving high-quality crowdsourced data and learning models at minimal expense. TsIFedCrowd enables clients to retain privacy-sensitive data locally, requiring only the upload of trained models to the server, which aggregates them into a global model. By modeling the federated crowdsourcing process as a two-stage Stackelberg game, TsIFedCrowd incentivizes workers to complete tasks with heightened quality and efficiency. It also provides a timeliness selection mechanism, which makes TsIFedCrowd suitable for both real-time crowdsourcing, where the generation time of task results (data) needs to meet the established requirements of the application, and non-real-time crowdsourcing. TsIFedCrowd mandates clients to complete federated crowdsourcing tasks while maximizing utility for both clients and the server, achieved through Nash equilibrium resolution. TsIFedCrowd is also extended to the multiple heterogeneous federated crowdsourcing scenario. Extensive experiments demonstrate TsIFedCrowd’s effectiveness in ensuring incentive fairness and universality, accelerating convergence speed, and budget savings. Real-world crowdsourcing tasks further validate its efficacy.

关键词： Crowdsourcing Training Web services Scalability Games Nash equilibrium Privacy breach Data models Real-time systems Servers

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