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检索条件"任意字段=3rd International Conference on Soft Computing in Data Science, SCDS 2017"

共 693 条记录，以下是1-10 订阅

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3rd international conference on soft computing in data science, scds 2017

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3rd international conference on soft computing in data science, scds 2017

ISBN: (纸本)9789811072413

The proceedings contain 26 papers. The special focus in this conference is on soft computing in data science. The topics include: Time series machine learning: Implementing ARIMA and hybrid ARIMA-ANN for electricity forecasting modeling;a design of a reminiscence system for tacit knowledge recall and transfer;pattern search based on particle swarm optimization technique for block matching motion estimation algorithm;enhancing parallel self-organizing map on heterogeneous system architecture;geovisualization using hexagonal tessellation for spatiotemporal earthquake data analysis in Indonesia;Incremental filtering visualization of jobstreet Malaysia ICT jobs;software application for analyzing photovoltaic module panel temperature in relation to climate factors;budget visual: Malaysia budget visualization;fuzzy arithmetical modeling of pressurizer in a nuclear power plant;road lane segmentation using deconvolutional neural network;nitrogen fertilizer recommender for paddy fields;Ranking performance of modified fuzzy TOPSIS variants based on different similarity measures;prediction of new bioactive molecules of chemical compound using boosting ensemble methods;Pragmatic miner to risk analysis for intrusion detection (PMRA-ID);intelligent system E-learning modeling according to learning styles and level of ability of students;mining textual terms for stock market prediction analysis using financial news;content quality of latent dirichlet allocation summaries constituted using unique significant words;rare event prediction using similarity majority under-sampling technique;Pattern recognition of balinese carving motif using learning vector quantization (LVQ);Feature extraction for image content retrieval in thai traditional painting with SIFT algorithms;modeling of the gaussian-based component analysis on the kernel space to extract face image.

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Recent Trends in data science and soft computing: Proceedings of the 3rd international conference of Reliable Information and Communication Technology

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2018年

作者： Faisal Saeed Nadhmi Gazem Fathey Mohammed Abdelsalam H Busalim

ISBN: (纸本)9783319990064

This book presents the proceedings of the 3rd international conference of Reliable Information and Communication Technology 2018 (IRICT 2018), which was held in Kuala Lumpur, Malaysia, on July 2324, 2018. The main theme of the conference was data science, AI and IoT Trends for the Fourth Industrial Revolution. A total of 158 papers were submitted to the conference, of which 103 were accepted and considered for publication in this book. Several hot research topics are covered, including Advances in data science and Big data Analytics, Artificial Intelligence and soft computing, Business Intelligence, Internet of Things (IoT) Technologies and Applications, Intelligent Communication Systems, Advances in Computer Vision, Health Informatics, Reliable Cloud computing Environments, Recent Trends in Knowledge Management, Security Issues in the Cyber World, and Advances in Information Systems Research, Theories and Methods.

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Iterative Turbo Receiver for FTN Based on Semi-Definite Relaxation 3

Iterative Turbo Receiver for FTN Based on Semi-Definite Rela...

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3rd international conference on Cloud computing, Big data Application and software Engineering, CBASE 2024

作者： Jiang, YuTao Gong, WenBin Chen, Honghao Gu, Yu Yuan, Tangju Su, Yu School of Electronic and Information Engineering Chongqing Three Gorges University Chongqing Wanzhou China School of Computer Science and Engineering Chongqing Three Gorges University Chongqing Wanzhou China

ISBN: (纸本)9798331506582

Faster-than-Nyquist (FTN) technology, as a transmission technique that can improve spectrum efficiency, has received widespread attention. However, it introduces severe Inter-Symbol Interference (ISI), leading to increased complexity in symbol detection at the receiver end. An FTN detector based on Semidefinite Relaxation (SDR) exhibits good performance and low complexity. In this paper, an iterative FTN detector based on soft-output SDR is proposed. In traditional turbo receivers, forward error correction codes are only used at the decoder. However, the SDR detector proposed in this paper outputs external information to the decoder with higher reliability. By simplifying the SDR Turbo receiver, this approach solves only one SDR problem for each codeword instead of multiple SDR problems in the iterative turbo processing, thus reducing the time complexity of the SDR turbo receiver while maintaining the original error performance. © 2024 IEEE.

关键词： Forward error correction

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AI-enabled digital twins: soft computing methods for industrial data mining 3

AI-enabled digital twins: soft computing methods for industr...

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3rd international conference on Intelligent computing and Next Generation Networks, ICNGN 2024

作者： Kunelbayev, Murat Amirkhanov, Bauyrzhan Zauranbek, Madiyar Abdulkhamit, Nazargozha Adilzhanova, Saltanat Nurgazy, Tomiris Artificial Intelligent and Big Data department Al-Farabi Kazakh National University Almaty Kazakhstan Cybersecurity and cryptology department Al-Farabi Kazakh National University Almaty Kazakhstan Artificial Intelligence and Big Data department Al-Farabi Kazakh National University Almaty Kazakhstan

ISBN: (纸本)9798331529222

Digital twins (DTs) have become a critical constituent in the continuous digital evolution of industrial systems, enabling instantaneous insight, anticipatory analytics, and tactical enhancement across various sectors. This paper provides an extensive examination of DT principles, historical progressions, market trends, enabling technologies, fields of application, economic impacts, modeling methodologies, challenges, and prospective developments. Special emphasis is placed on the food industry, which exemplifies how DTs can enhance traceability, ensure quality assurance, and build resilience in complex global supply chains. Based on a wide-ranging review of the scholarly literature and industrial analysis, this work highlights how DTs integrate IoT data, AI-driven models, cloud/edge computing, and robust engineering data management systems into cohesive decision-support frameworks. The result is a powerful skill that stimulates continuous improvement, goals toward sustainability, and strategic visioning in dynamic industrial settings. ©2024 IEEE.

关键词： Supply chains

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Segmenting the Brain Tumor using Deep Learning Techniques 3

Segmenting the Brain Tumor using Deep Learning Techniques

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pranitha, Ch. Lakshmi Sumathi, R. Harshitha, V. Kavya, N. Soni, A. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331509675

This study presents a U-Net architecture with EfficientNet-B7 as the feature extraction backbone is used to propose a robust brain tumor segmentation method. Using cutting-edge deep learning techniques, the suggested model precisely segments MRI images with an emphasis on identifying and defining tumor anomalies. A thorough preprocessing pipeline that handles missing values, augments data with albumentations, and normalizes data guarantees the consistency and resilience of the incoming data. The segmentation procedure uses a hybrid loss function that combines soft Dice Loss and Binary Cross-Entropy (BCE), improving the model's ability to strike a compromise between segmentation consistency and pixel-level precision. To ensure efficiency and generalizability, the model training is optimized using the Adam optimizer in conjunction with a learning rate scheduler. Additionally, an early halting mechanism is incorporated to minimize overfitting. Using a standardized dataset, performance evaluation is carried out with 70%, 15%, and 15% splits for training, validation, and testing, respectively. To evaluate the quality of segmentation, important metrics are employed, such as the Dice Coefficient and the Intersection over Union (IoU). The accuracy of the model in identifying tumor boundaries is illustrated by visualizations of the anticipated segmentation results. The potential of combining U-Net with cutting-edge feature extractors such as EfficientNet-B7 to produce dependable, effective, and clinically significant segmentation results in medical imaging is demonstrated by this approach. © 2025 IEEE.

关键词： Deep learning

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Network Intrusion Detection System Using Decision Tree and KNN Algorithm 3

Network Intrusion Detection System Using Decision Tree and K...

引用

3rd international conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Singh, Km Shivani Singh, Khundrapam Johnson National Institute of Technology Dept. of Computer Science and Engineering Manipur Imphal India

ISBN: (纸本)9798350309409

The complexity of attacks is increasing, making it increasingly difficult to effectively discover breaches. A network intrusion detection system is needed to handle the aforementioned problem. An automated software program called a network intrusion detection system alerts administrators when someone attempts to compromise the system by participating in hazardous behaviors. A system or network is protected from harmful invasions by hardware and software firewalls. They function like filters, removing any data that could put the system or network at risk. We aimed to design the best intrusion detection system possible so that it could distinguish between 'normal' and 'attacked' categories of network data with high accuracy. The best accuracy can be obtained by using soft computing techniques like Decision Trees and KNN. We tested a variety of techniques, such as preprocessing the data, feature selection, principal component analysis (PCA) reduction, standardization, and normalization, in order to increase our model's accuracy scores. We also assess the two approaches' results according to their accuracy in order to decide which way is more effective. © 2024 IEEE.

关键词： Decision trees

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Encrypted video identification - Normalization and activation function perspective 3

Encrypted video identification - Normalization and activatio...

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3rd international conference on Communication, computing and Digital Systems, C-CODE 2023

作者： Bukhari, Syed M. A. H. Afandi, Waleed Maqsood, Tahir Khan, Muhammad U. S. Comsats University Islamabad Department of Computer Science Abbottabad Pakistan Comsats University Islamabad Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350332391

Video identification in encrypted network traffic has become a challenge for researchers in the past few years. However, the accuracy of the identification depends on various factors. This paper highlight the impact of data normalization techniques and the activation function of the hidden and dense layer on accuracy. For this purpose, we tried different normalization techniques for data normalization. Upon selecting the best data normalization technique, we first modified the activation functions of hidden layers. After selecting the most suitable activation function for hidden layers, we modified the activation function of the dense layer. The results show that the Min-Max data normalization technique, the ReLU function on hidden layers, and the soft-max activation function on dense layer is the most suitable combination for video identification in network traffic. The configuration achieves an accuracy of 96.38%. © 2023 IEEE.

关键词： Convolutional neural networks

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Adaptive SOM neural network algorithm 3

Adaptive SOM neural network algorithm

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3rd international conference on Artificial Intelligence, Automation, and High-Performance computing, AIAHPC 2023

作者： Wang, Xin Yan, Taishan School of Information Science and Engineering Hunan Institute of Science and Technology Hunan Yueyang China Hunan Institute of Science and Technology School of Information Science and Engineering China Key Laboratory of Hunan Province on Intelligent Control and Optimization of Complex Industrial Logistics System Hunan Yueyang China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

In order to improve the performance of traditional SOM model, an adaptive SOM neural network algorithm is proposed. By introducing the error square sum solution formula, the clustering error of the model sub-data set is calculated by using the method of cutting the data set, and the change trend of the clustering error is quickly obtained. Then the learning rate of the SOM network is adaptively adjusted according to this, and the convergence speed is accelerated on the basis of ensuring that the network is fully trained, thereby improving the performance of the SOM network. © 2023 SPIE.

关键词： Errors

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IoT Security Detection and Evaluation for Smart Cyber Infrastructures Using LSTMs with Attention Mechanism 3

IoT Security Detection and Evaluation for Smart Cyber Infras...

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3rd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Pillai, Sanjaikanth E Vadakkethil Somanathan Polimetla, Kiran Prakash, Chidurala Sai Pareek, Piyush Kumar Pawar, Priyanka Pramod School Of Electrical Engineering And Computer Science University Of North Dakota Grand Forks United States Adobe Inc. Dept. Of Information Technology AustinTX United States Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Aiml And Ipr Cell Bengaluru India University Of The Cumberlands Department Of Information Technology WilliamsburgKY United States

ISBN: (纸本)9798350318609

A computer network may be impacted by malware, computer viruses, and other hostile attacks. One of the most important elements of network security as an active defence technology is Intrusion Detection System (IDS). Issues with traditional intrusion detection systems include low detection effectiveness, inaccurate results, and an inability to handle new types of intrusions. This paper implemented a deep LSTMs with attention mechanism approach for IDS. The proposed technique is determined on Bot-IoT dataset, and z-score normalization is performed to normalize data. Information Gain (IG) is utilized to choose the features. A deep LSTMs with attention mechanism is used to effectively identify and categorize IDS effectively. The IG-deep LSTMs with attention mechanism achieves better accuracy using of 99.70% and 99.60% in NSL-KDD and CIC-IDS2017 datasets respectively. © 2024 IEEE.

关键词： Internet of things

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Blockchain and Machine Learning Security for the Internet of Things: A Bibliometric Analysis 3

Blockchain and Machine Learning Security for the Internet of...

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3rd international conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Mudhar, Jaideep Kaur Malhotra, Jyoteesh Singh, Ajay Kumar Anand, Monika Guru Nanak Dev University Computer Engineering Amritsar India Guru Nanak Dev University Electronics Engineering Regional Campus Jalandhar India Chandigarh University Computer Science and Engineering Mohali India

ISBN: (纸本)9798350322842

The Internet of Things has developed as a catalyst for innovation across numerous industries, from home automation to smart cities to 4IR primarily Industry 4.0. Through the internet, IoT devices interact and exchange information. Beyond its potential for development, IoT devices are divergent, ubiquitous, memory-and computational-power-constrained, making them vulnerable to a range of security and privacy risks. Blockchain, a decentralised and tamper-proof technology, has recently been introduced in the BIoT domain for securing data handling activities and inculcating trusted transactions. In anticipation of IoT System vulnerabilities, Machine Learning (ML) algorithms yield accurate findings for large and complicated databases. This study conducts a bibliometric study from the Web of science (WoS) database from 2017 to 2022 in the area of ML-based BIoT security using Prisma guidelines. Rankings of the most productive and influential writers, articles, and sources are based on total publications and citations. Text data content analysis, Author keywords, index keywords are utilized to highlight research hotspots and future trends in the ML-Based BIoT security domain. © 2023 IEEE.

关键词： Blockchain

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