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检索条件"任意字段=4th International Conference on Soft Computing in Data Science, SCDS 2018"

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4th international conference on soft computing in data science, scds 2018

4th International Conference on Soft Computing in Data Scien...

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4th international conference on soft computing in data science, scds 2018

ISBN: (纸本)9789811334405

the proceedings contain 30 papers. the special focus in this conference is on soft computing in data science. the topics include: Exploratory analysis of MNIST handwritten digit for machine learning modelling;Improved conditional value-at-risk (CVaR) based method for diversified bond portfolio optimization;ranking by fuzzy weak autocatalytic set;fortified offspring fuzzy neural networks algorithm;forecasting value at risk of foreign exchange rate by integrating geometric brownian motion;fog of search resolver for minimum remaining values strategic colouring of graph;incremental software development model for solving exam scheduling problems;visualization of frequently changed patterns based on the behaviour of dung beetles;applications of machine learning techniques for software engineering learning and early prediction of students’ performance;scalable single-source shortest path algorithms on distributed memory systems;opinion mining for skin care products on twitter;tweet hybrid recommendation based on latent dirichlet allocation;assessing structured examination question using automated keyword expansion approach;improving topical social media sentiment analysis by correcting unknown words automatically;big data security in the web-based cloud storage system using 3d-aes block cipher cryptography algorithm;an empirical study of classifier behavior in rattle tool;clutter-reduction technique of parallel coordinates plot for photovoltaic solar data;data visualization of violent crime hotspots in Malaysia;malaysia election data visualization using hexagon tile grid map;a computerized tool based on cellular automata and modified game of life for urban growth region analysis;simulation study of feature selection on survival least square support vector machines with application to health data.

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Research on partial task offloading and resource allocation strategy based on improved soft Actor-Critic in edge computing network 4

Research on partial task offloading and resource allocation ...

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4th international conference on Internet of things and Smart City, IoTSC 2024

作者： Wang, Weiping School of Information Science and Technology North China University of Technology Beijing100144 China

ISBN: (纸本)9781510681767

Traditional cloud computing is limited by bandwidth and transmission delay, and it has been unable to meet the current growing computing needs. As an extension of cloud computing, edge computing is a promising computing model that allows users to enjoy lower computing latency than cloud computing. this paper studies the partial task offloading problem in multi-user edge computing scenarios. Firstly, the problem is established as an optimization problem, and then the Markov decision process is used to describe the problem. Finally, an improved soft Actor-Critic algorithm is proposed to jointly solve the offloading rate, offloading node selection and resource allocation decision. the experimental results show that the algorithm can maintain good optimization performance compared with the baseline algorithm. © 2024 SPIE.

关键词： computing systems data transmission Instrument modeling Binary data data processing Mathematical optimization Network architectures Cloud computing data storage servers Systems modeling

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Patterns Recognition of Sentimental Patterns from the X-dataset using Deep Learning Algorithm 4

Patterns Recognition of Sentimental Patterns from the X-Data...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Konduri, Praveen S R Department of Data Science School of Computing Mohan Babu University Tirupati India

ISBN: (纸本)9798331515720

this research investigates the application of deep learning techniques to sentiment analysis, a field focused on mining online platforms for subjective assessments. By combining deep neural networks (DNNs) with particle swarm optimization (PSO), we propose a hybrid method to improve feature selection and enhance the accuracy of sentiment classification. the study employs a dataset collected from Twitter using the Ruby Twitter API and evaluates the performance of the proposed method using k-fold cross-validation. the results are compared with the previously used firefly search (FS) method to demonstrate the effectiveness of the hybrid approach. this research contributes to the advancement of sentiment analysis techniques and their application in various domains, such as social media monitoring and market research. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Multi-source Heterogeneous data Fusion Algorithm Based on Federated Learning 7th

Multi-source Heterogeneous Data Fusion Algorithm Based on Fe...

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7th international conference on soft computing in data science (scds)

作者： Zhou, Jincheng Lei, Yang Qiannan Normal Univ Nationalities Sch Comp & Informat Duyun 558000 Guizhou Peoples R China Baoji Univ Arts & Sci Sch Comp Sci Baoji 721007 Peoples R China Key Lab Complex Syst & Intelligent Optimizat Guiz Duyun 558000 Guizhou Peoples R China

ISBN: (纸本)9789819904044;9789819904051

With the rapid advancement of science and technology, the number of edge devices with computing and storage capabilities, as well as the generated data traffic, continues to increase, making it difficult for the centralized processing mode centered on cloud computing to efficiently process the data generated by edge devices. Moreover, multimodal data is abundant due to the diversity of edge network devices and the ongoing improvement of data representation techniques. To make full use of heterogeneous data on edge devices and to tackle the "data communication barrier" problem caused by data privacy in edge computing, a multi-source heterogeneous data fusion technique based on Tucker decomposition is developed. the algorithm introduces tensor Tucker decomposition theory and realizes federated learning by constructing a high-order tensorwith heterogeneous spatial dimension characteristics to capture the high-dimensional features of heterogeneous data and solve the fusion problem of heterogeneous data assuming no interaction. Combining and remembering. this method, unlike its predecessors, can efficiently integrate multi-source heterogeneous data without data transfer, therefore overcoming privacy and security-related data communication concerns. Finally, the effectiveness of the technique is confirmed using the MOSI data set, and the shortcomings of the original federated learning algorithm are solved by the updated federated weighted average algorithm. Subjective criteria are used to calculate the data quality for the improved federated average approach based on AHP. We present Influence, an improved federated weighted average method for dealing with multi-source data from a data quality perspective.

关键词： Edge computing Federated learning Deep learning Tensor theory Heterogeneous data fusion

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soft kind of computing Based Least Resource data finding which is Used for Analysis purpose 4

Soft kind of Computing Based Least Resource Data finding whi...

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4th IEEE international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Rani, L. Pattathu Surendran, S. Monica, D. Kumar, V. Senthil Rajaram, Gnanajeyaraman Dwibedi, Rajat Kumar Simats University Saveetha School of Engineering Chennai India Tagore Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Mechatronics Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Simats Saveetha School of Engineering Department of Computer Science and Engineering India Aarupadai Veedu Institute of Technology Department of Ece Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

Deep learning (DL) approaches are utilized across three distinct contexts to showcase their applicability in commercial environments with restricted data for training. this study showcases the application of unsupervised DL with autoencoders that vary & illustrates how federation learning serves as a successful collaboration approach for machine learning (ML) that safeguards confidentiality of data. © 2024 IEEE.

关键词： Federated learning

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Parallelism in Cloud Architecture using Implicit Partition Clustering Framework 4

Parallelism in Cloud Architecture using Implicit Partition C...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Sudharson, D. Diwakaran, M. Senthil Kumar, V. Sukirtha, S. Afsheen Zaahrah, A. Karthick, A. Artificial Intelligence and Data Science Kumaraguru College of Technology Tamil Nadu Coimbatore India Sri Krishna College of Eng & Tech Dept of IT Tamil Nadu Coimbatore India Kumaraguru College of Technology Dept of CSE Tamil Nadu Coimbatore India Shiv Nadar University Chennai Artificial Intelligence and Data Science Tamil Nadu Chennai India Kumaraguru College of Engineering Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Kumaraguru College of Technology Artificial Intelligence and Data Science Tamil Nadu Coimbatore India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515720

To overcome the reliability issues concerning data's exponential growth, many optimization techniques are deployed in artificial intelligence. Parallelism in a distributed environment provides an effective solution to handle this expansion efficiently. Catering to real-Time and batch-Time requirements in distributed environments, Spark and Flink are two prominent, powerful, versatile, fault-Tolerant, open-source frameworks providing a unified platform for large data processing. Primarily analyzing the efficiency optimization through implicit partitioning clustering algorithms and adaptable K-means. Combining a few methods for adaptable K value enhances the robustness of the clustering solution, which effectively yields optimized performance. © 2024 IEEE.

关键词： Cloud platforms

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IoT-based automatic road accident detection systems: A review 4

IoT-based automatic road accident detection systems: A revie...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Prabu, C.R. Namitha, Kalakunnath Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798331515720

With the increasing number of vehicles on roads, the occurrence of road accidents has become a major concern globally. Automatic road accident detection systems based on Internet of things (IoT) technologies have emerged as a promising solution to improve emergency response times and reduce the severity of accidents. the review encompasses various aspects including the underlying technologies, sensor networks, data processing algorithms, communication protocols, and system architectures used in these systems. Furthermore, the paper discusses the challenges and limitations of current approaches such as sensor accuracy, system scalability, and real-Time data processing. It also identifies future research directions including the integration of advanced sensor technologies, machine learning algorithms for accident prediction, and the development of standardized communication protocols for interoperability among different systems. To improve road safety, researchers and practitioners can use the ideas to help build IoT-based autonomous road accident detection systems that are more effective and efficient. © 2024 IEEE.

关键词： Interoperability

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Integrating Deep Learning, Machine Learning, AI, IoT and data science for Future Innovations 4

Integrating Deep Learning, Machine Learning, AI, IoT and Dat...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Gupta, Shipra Kumar, Vijay Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Hill University Department of Physics Uttarakhand Dehradun India

ISBN: (纸本)9798331515720

Machine Learning (ML), Deep Learning (DL), Internet of things (IoT), Artificial Intelligence (AI) and data science are integrated to create innovative solutions that are powering industries today like never before. this paper reviews these technologies and how they can work together to transform industries like healthcare, finance, and manufacturing, as well as the immensely important space of smart cities. With ML and DL-driven analytics for predictions, IoT-based real-Time data capture mechanisms, AI making informed decisions along data science generating insights from the data makes organizations efficient in every aspect as well as reliable and innovative. Some example applications are predictive maintenance in manufacturing, personalized medicine in healthcare, algorithmic trading in finance and the development of smart/sustainable urban environments. While these potboilers offer a hopeful advantage, they also come with some hurdles, data privacy/security, integration complexity and the need for high-end technical skills being the topmost. In this manuscript, some case studies of success are highlighted in the belonging field and discuss the challenges for incorporation today along with future directions for seamless integration aimed at promoting even greater innovation. © 2024 IEEE.

关键词： Smart manufacturing

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Roselinkiruba, R. Jude Moses Anto Devakanth, J. Sharmila, L. Vasumathy, M. Misba, M. Saranya Jothi, C. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai Avadi India Madanapalle Institute of Technology and Science Department of Computer Applications Andra Pradesh India Department of Information Technology Kingston Engineering College Vellore India

ISBN: (纸本)9798331515720

Steganography is the method that convert data into bits to make it suitable for hiding. While embedding data the factors such as stego image quality and distortion which cannot be observed by the human eyes. the aim of this study is to increase the stego image with good quality. Initially, the image pixels are separated into different bit planes then it is compared with the secret data bit planes using Fruitfly Optimization Improved Hybridized Algorithm Pixel Similarity (FOIS-PS). For that similar pixel Fitness Function (FF) is applied based on the three important criteria such as edge, entropy and correlation. Finally, the data is embedded in the LSB bits based on their Embedding Capacity (EC). Using this method, the size of the input is reduced. then, the data is embedded based on difference of pixels within each group of similar pixels. Evaluation metrics shows the quality of the stego image is boosted and also reduces the processing time. © 2024 IEEE.

关键词： Image quality

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Enhancing Automated Payments: Impact of Iris Technology's in UPI Transactions 4

Enhancing Automated Payments: Impact of Iris Technology's in...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Galhotra, Bhavna Bajaj, Shalini Bhaskar Jatain, Aman Jaglan, Vivek Amity University Computer Science Dept Haryana India Jagan Institute of Management Studies India K.R. Mangalam University Department of Computer Science SOET Gurgaon India Amity University ASET MP Gwalior India

ISBN: (纸本)9798331515720

In the era of cashless transactions, security has become a significant concern for users. this research explores the various models and technologies employed to enhance authentication and payment security in wallets and UPI payments. Biometric authentication, specifically iris recognition, emerges as a promising solution due to its high reliability and wide-ranging applications. the study investigates the working mechanism of iris recognition, comparing captured images with stored data to verify user identity. It highlights the significance of iris authentication in online transactions, presence verification, and other payment-related areas. this study also analyzes the existing models and identifies the most commonly used iris recognition techniques. By understanding the advancements in biometric authentication, this research aims to contribute to the development of more secure and efficient cashless payment systems. © 2024 IEEE.

关键词： Authentication Protocol

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