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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Tyagi, Shivangi Singh, Pushpendra Khan, Kunwar Laiq Ahmad Srm Institute of Science and Technology Department of Computer Science Delhi NCR Campus Modinagar UP Ghaziabad201204 India Meerut Institute of Technology Department of Mechanical Engineering Ghaziabad India

ISBN: (纸本)9798331527495

Composite materials are heavily implemented throughout many industries because they offer immense strength in combination with the ability to be lightweight without compromising their flexibility in a many-faceted environment. However, these are nonlinear and multi-dimensional materials;that require sophisticated models to predict the optimal properties and performance. Currently, an impressive method making predictions, optimizing, detecting defects in composites is ML as compared with considerable experimental tests. This paper reviews the current state of the art of advanced ML in the field of composite materials for different aspects such as property prediction, defect identification, design optimization and additive manufacturing. A comparative assessment also shows the challenges and the methods used and final results in various types of composites. The study also recognises the ML's capability to open a new path in composites material design and test with existing scales, interpretability, and integration issues. © 2025 IEEE.

关键词： Metamaterials

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Fractional Linear Matroid Matching Is in Quasi-NC 32

Fractional Linear Matroid Matching Is in Quasi-NC

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32nd Annual European Symposium on Algorithms, ESA 2024

作者： Gurjar, Rohit Oki, Taihei Raj, Roshan Department of Computer Science and Engineering Indian Institute of Technology Bombay Mumbai India Department of Mathematical Informatics Graduate School of Information Science and Technology The University of Tokyo Japan

ISBN: (纸本)9783959773386

The matching and linear matroid intersection problems are solvable in quasi-NC, meaning that there exist deterministic algorithms that run in polylogarithmic time and use quasi-polynomially many parallel processors. However, such a parallel algorithm is unknown for linear matroid matching, which generalizes both of these problems. In this work, we propose a quasi-NC algorithm for fractional linear matroid matching, which is a relaxation of linear matroid matching and commonly generalizes fractional matching and linear matroid intersection. Our algorithm builds upon the connection of fractional matroid matching to non-commutative Edmonds' problem recently revealed by Oki and Soma (2023). As a corollary, we also solve black-box non-commutative Edmonds' problem with rank-two skew-symmetric coefficients. © Rohit Gurjar, Taihei Oki, and Roshan Raj;licensed under Creative Commons License CC-BY 4.0.

关键词： Parallel algorithms

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Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 83-109页

作者： Anbazhagu, U.V. Koti, Manjula Sanjay Muthukumaran, V. Geetha, V. Munrathnam, Meram Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of MCA Dayananda Sagar Academy of Technology and Management Bangalore560082 India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur Campus 603 203 India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.k.Valley Idupulapaya Andhra Pradesh Kadapa516330 India

The necessity for smart energy oversight solutions has arisen in response to the rising popularity of energy-efficient home automation and other energy-saving technologies. Optimizing smart home energy use using multi-criteria decision-making (MCDM) is a proven methodology. However, the procedure for making decisions and MCDM’s capacity to handle various criteria are typically limiting factors. Hybrid methods, which integrate multiple decision-making approaches like Fuzzy Logic (FL) and Modular Neural Networks (MNN), could potentially be able to circumvent these restrictions and boost energy management systems’ efficacy and precision. This investigation presents a hybrid Neuro-Fuzzy (H-NF) method for MCDM in regulating energy for smart homes by combining FL with an MNN. The suggested approach would optimize energy use in smart homes by considering several parameters, notably cost, ease of use, and environmental effects. In addition, this study aims to examine how the H-NF model fares in comparison to other methods of making important decisions in terms of several performance metrics. The suggested hybridized approach has the potential to deliver more precise and effective decision-making processes for energy management in smart homes, allowing users to optimize their energy consumption while preserving comfort and lowering environmental impact. © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Decision making

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A New Approach Based on an Intelligent Method to Classify Quality of Service

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Informatica (Slovenia) 2022年第9期46卷 7-16页

作者： Altmemi, Dhuha Kh. Abdulzahra, Abdulmalik Adil Alshawi, Imad S. Department of Computer Science Shatt Al-Arab University College Basra Iraq Department of Computer Technology Engineering Al-Kunooze University College Basrah Iraq Department of Computer Science College of Computer Science and Information Technology University of Basrah Basrah Iraq

computer networks are used more frequently for time-sensitive applications like voice over internet protocol and other communications. In computer networks, quality of service (QoS) can be crucial since it makes it easier to assess a network's performance and offers mechanisms for enhancing its performance. As a result, understanding the QoS provided by networks is essential for both network users and service providers to assess how well the transmission requirements of different applications are satisfied and to implement improvements to network performance. Next-generation monitoring systems must not only detect network performance deterioration instantly but also pinpoint the underlying cause of quality of service problems to achieve strict network standards. A brand-new fuzzy logic-based algorithm is suggested as a solution to this issue. Thus, the proposed approach was evaluated and compared with probabilistic neural networks (PNN) and Bayesian classification, as well as network performance measurement, latency, jitter, and packet loss. All approaches correctly classified the QoS categories, although generally, the fuzzy approach outperformed PNN and Bayesian. An improved comprehension of the network performance is acquired by precisely determining its QoS. © 2022 Authors. All rights reserved.

关键词： Fuzzy logic

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Feature Extraction and Similarity-Based Movie Recommendation Using Bag-of-Words Model

Feature Extraction and Similarity-Based Movie Recommendation...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Raj, Swati Dutta, Jhalak Bandopadhyay, Saikat Prasad, Rajesh Department of Computer Science and Engineering Christ University Bengaluru India Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Artificial Intelligence and Machine Learning Netaji Subhash Engineering College Kolkata India Department of Computer Science and Engineering MIT Art Design and Technology University Pune India

ISBN: (纸本)9789819613472

Recommender systems have become pervasive in guiding users through a lot of choices available in today’s digital landscape. This paper presents a content-based recommender system focused specifically for movie recommendations, offering a viable alternative to collaborative filtering methods. Unlike collaborative filtering, which relies on user ratings and demographic data, the proposed system operates without such prerequisites, making it particularly advantageous in scenarios with limited user engagement. By leveraging content attributes such as title, genre, cast, and crew, the system identifies similarities between movies and recommends those akin to a user’s preferences. Through innovative preprocessing techniques and feature extraction methodologies, the system transforms textual descriptions into numerical vectors, enabling efficient recommendation generation. The proposed model contributes to addressing critical challenges inherent in collaborative filtering, including the cold start problem, data sparsity, and popularity bias. Furthermore, it underscores the broad applicability of recommender systems across various domains and emphasizes the continuous evolution of interface designs and algorithms within the research community. Overall, the proposed content-based recommender system offers a promising solution for delivering personalized movie recommendations while advancing the state of the art in recommendation technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bag-of-words Cosine similarity Filtering system Recommender system TF-IDF

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SE-GCL: an event-based simple and effective graph contrastive learning for text representation

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Neural Computing and Applications 2025年第8期37卷 5913-5926页

作者： Meng, Tao Ai, Wei Li, Jianbin Wang, Ze Li, Keqin College of Computer and Mathematics Central South University of Forestry and Technology Changsha Hunan410004 China Department of Computer Science State University of New York New PaltzNY12561 United States

Text representation learning is significant as the cornerstone of natural language processing. In recent years, graph contrastive learning (GCL) has been widely used in text representation learning due to its ability to represent and capture complex text information in a self-supervised setting. However, the current mainstream graph contrastive learning methods often require the incorporation of domain knowledge or cumbersome computations to guide the data augmentation process, which significantly limits the application efficiency and scope of GCL. Additionally, many methods learn text representations only by constructing word-document relationships, which overlooks the rich contextual semantic information in the text. To address these issues and exploit representative textual semantics, we present an event-based, simple, and effective graph contrastive learning (SE-GCL) for text representation. Precisely, we extract event blocks from text and construct internal relation graphs to represent inter-semantic interconnections, which can ensure that the most critical semantic information is preserved. Then, we devise a streamlined, unsupervised graph contrastive learning framework to leverage the complementary nature of the event semantic and structural information for intricate feature data capture. In particular, we introduce the concept of an event skeleton for core representation semantics and simplify the typically complex data augmentation techniques found in existing graph contrastive learning to boost algorithmic efficiency. We employ multiple loss functions to prompt diverse embeddings to converge or diverge within a confined distance in the vector space, ultimately achieving a harmonious equilibrium. We conducted experiments on the proposed SE-GCL on four standard data sets (AG News, 20NG, SougouNews, and THUCNews) to verify its effectiveness in text representation learning. The accuracy achieved on the respective datasets is 91.56, 86.76, 98.03, and 97.79%, de

关键词： Contrastive Learning

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Hybrid Soft Computing for CBIR System by Integration of Edge Detection and Compression Mechanism 5th

Hybrid Soft Computing for CBIR System by Integration of Edge...

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5th International Conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

作者： Diwanshi Kumar, Dharminder Dhingra, Sakshi Department of Computer Science and Engineering Guru Jambheshwar University of Science and Technology Haryana Hisar125001 India

ISBN: (纸本)9789819716814

Demand of CBIR is growing day by day, thus there are several soft computing techniques that are used during image processing. Edge detection and compression techniques are capable to reduce the processing time of CBIR operations. But, the issue with compression mechanism is quality reduction. The goal of current investigation is to maintain picture quality after compression has been applied. The impact of processing on final picture quality is a major topic of study in field of image analysis. In addition, there is a need to explore lossless compression methods that preserve picture quality. Present study is examining distinct approaches utilized by current research article along with its operating mechanism, pros and drawback. The goal of previous studies were to improve image processing speed, but image quality was ignored. There remains need to maintain image quality throughout CBIR processes. Thus integration of edge detection and compression technique has been made to introduce hybrid soft computing approach in order to improve efficiency of CBIR system. Canny-based edge detection has been used with loss less image compression mechanism. Huffman is the lossless compression mechanism that is used to maintain the quality of image after compression. Integration of these compression and edge detection mechanism has been made with DWT in proposed study, thus hybrid approach has been used to resolve the issue of performance of conventional CBIR systems. The proposed approach prioritizes both picture quality and compression. In order to measure the improvement in picture quality due to the elimination of noise, PSNR has been implemented. The suggested work is superior to the status quo since it takes into account both quality and performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Edge detection

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Performance Analysis of Resource Allocation Algorithms for Vehicle Platoons over 5G eV2X Communication 17

Performance Analysis of Resource Allocation Algorithms for V...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Mandal, Gulabi Roy, Anik Palit, Basabdatta Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India Indian Institute of Technology Advanced Technology Development Center Kharagpur India National Institute of Technology Department of Electronics and Communication Engineering Rourkela India

ISBN: (纸本)9798331531195

Vehicle platooning is a cooperative driving technology that can be supported by 5G enhanced Vehicle-to-Everything (eV2X) communication to improve road safety, traffic efficiency, and reduce fuel consumption. eV2X communication among the platoon vehicles involves the periodic transmission of Cooperative Awareness Messages (CAMs) containing vehicle information under strict latency and reliability requirements. These requirements can be maintained by administering the assignment of resources, in terms of time slots and frequency bands, for CAM transmission in a platoon, with the help of a resource allocation mechanism. State-of-the-art on control and communication design for vehicle platoons either consider a simplified platoon model with a detailed communication architecture or consider a simplified communication delay model with a detailed platoon control system. Departing from existing works, we have developed a comprehensive vehicle platoon communication and control framework using OMNET + +, the benchmarking network simulation tool. We have carried out an inclusive and comparative study of three different platoon Information Flow Topologies (IFTs), namely Car-to-Server, Multi-Hop, and One-Hop over 5G using the Predecessor-Leader Following platoon control law to arrive at the best-suited IFT for platooning. Secondly, for the best-suited 5G eV2X IFT, we have analyzed the performance of three different resource allocation algorithms, namely Maximum of Carrier to Interference Ratio (MaxC/I), Proportional Fair (PF), and Deficit Round Robin (DRR). Exhaustive system-level simulations show that the One-Hop information flow strategy along with the MaxC/I resource allocation yields the best Quality of Service (QoS) performance, in terms of latency, reliability, Age of Information (AoI), and throughput. © 2025 IEEE.

关键词： Vehicle to vehicle communications

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Twin attention based multi-task convolutional bidirectional long short term memory for facial expression recognition

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Multimedia Tools and Applications 2025年第10期84卷 8037-8070页

作者： Sreenivas, Velagapudi Sivaneasan, B. Vani, K. Suvarna Chakrabarti, Prasun Singapore Institute of Technology Singapore138683 Singapore Engineering Cluster Singapore Institute of Technology Singapore138683 Singapore Department of Computer Science and Engineering AI/ML Research Group Head VR Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur313601 India

Facial Expression Recognition (FER) aims to detect the emotional state of facial images. It is playing an increasingly important role in several application areas, including human–computer interaction (HCI), video transcriptions, and social communications. This article provides an adequate attention-based multi-task deep learning method for facial expression recognition. First, the input videos are collected from the RAVDESS and MELD datasets. Then, the input videos are converted using a threshold-based keyframe extraction algorithm. Next, the input data is pre-processed using the Adaptive Pixel Density Median Filtering (A-PDMF) method. Key features such as shape, color and texture are extracted from the pre-processed images. Finally, the facial expressions are recognized by proposing a novel twin attention-based multi-task convolutional bidirectional long-short-term memory method (TA-MC-BiLSTM). In addition, the classification parameters are optimally tuned using the EX-AHA method (Extended Artificial Hummingbird Algorithm). The proposed model reduces the size of facial features while accurately identifying the wide range of facial expressions. For simulation, the proposed method prefers python tool and results are analyzed using RAVDESS and MELD datasets. The simulation results show that the proposed model provides better results than other existing models in terms of accuracy of 99.6% for RAVEDESS and 99.4% for MELD. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

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A Survey on Online Courses: State of the Art and Current Challenges

A Survey on Online Courses: State of the Art and Current Cha...

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2023 Al-Sadiq International Conference on Communication and Information technology, AICCIT 2023

作者： Saeed, Zainab Ahmed Hassoun, Mohammed Mohsoun University of Kerbala College of Computer Science and Information Technology Computer Science Department Kerbala Iraq

ISBN: (纸本)9798350341881

The COVID-19 pandemic has driven a transformative shift in education, leading to the widespread adoption of online learning as the primary mode of education. This article provides an overview of the current state of online courses, exploring their potential as a mainstream learning approach. Motivated by the pandemic's impact on education, this research aims to identify the challenges and opportunities associated with online learning. The research conducted a comprehensive review of existing literature and analyzed studies, reports, and surveys related to online courses. The results highlight the advantages of online learning, such as flexibility and accessibility, while acknowledging challenges like the digital divide and limited social interactions. Ultimately, this study emphasizes the potential of online courses to enhance education and calls for addressing challenges to optimize their effectiveness. By leveraging the strengths of online learning, educators and policymakers can shape a more inclusive and accessible future of education. © 2023 IEEE.

关键词： COVID-19

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