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International Conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Raja Marappan Saraswatikaniga R School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Chennai M.Tech Computer Science and Engineering (Business Analytics) School of Computer Science and Engineering Vellore Institute of Technology Chennai

Recommendation systems have become increasingly important in various online applications. One of the significant types of research in machine learning (ML) is recommendation systems. A recommender system, alternatively referred to as a recommendation system, is an information filtering system that supports users in identifying their “rating” or “preference” for an object and makes predictions based on that rating or preference. This research analyzes current recommendation systems applied in ML public instances to explore knowledge discovery. The suggestions systems are developed for musical, online dating, and restaurant applications. The recommendation system serves as a tool to assist users in discovering what they are interested in by providing them with appropriate ideas. To provide personalized recommendations to users, primarily employ collaborative, content-based, session-based, demographic, and hybrid filtering. Classification, clustering, and association rule discovery are the critical data mining and ML techniques most widely employed in recommendation systems. In general, the accuracy of the recent models lies at (75%, 99 % ) and the error rate occurs at (5%, 25 % ) for the ML public instances.

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Multiple Sclerosis Detection with Reinforcement Learning and Differential Evolution

Multiple Sclerosis Detection with Reinforcement Learning and...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yang, Jing Yang, Jin Wu, Chenwei Jiang, Gaozhe Lv, Yaning Liu, Bingyan Xu, Feng Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia Mallinckrodt Institute of Radiology Washington University School of Medicine St. Louis United States Evident Battery Inc. Massachusetts United States Institute of Operations Research and Analytics National University of Singapore Singapore School of Mathematics and Statistics Xi'an University of Electronic Science and Technology Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Engineering and Design Technical University of Munich Munich Germany

ISBN: (纸本)9798350368741

Multiple Sclerosis (MS) disrupts nerve communication, potentially leading to permanent damage. Convolutional Neural Networks (CNNs) are commonly recommended to accelerate magnetic resonance imaging (MRI) analysis for MS. Traditional CNN-based methods often face challenges with feature selection, imbalanced classification, and hyperparameter optimization, which can reduce their effectiveness. To address these challenges, this paper introduces a reinforcement learning (RL) strategy for tackling feature selection and class imbalance, complemented by an advanced Differential Evolution (DE) algorithm for hyperparameter tuning. The RL algorithm dynamically adapts, enhancing feature selection and class balance, and consistently outperforms traditional static methods by continually optimizing its response to the complex patterns in MS detection. Furthermore, the DE is enhanced with a novel mutation mechanism that utilizes k-means clustering to identify significant clusters. Tested on the SNAC-MS and ISBI datasets, our model demonstrates robust accuracy with average F-measures of 92.873% and 93.783%, respectively, highlighting its effectiveness and adaptability in diagnosing MS. © 2025 IEEE.

关键词： feature selection hyperparameter optimization imbalanced classification Multiple sclerosis reinforcement learning

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Finding Simplex Items in data Streams 39

Finding Simplex Items in Data Streams

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Fan, Zhuochen Guo, Jiarui Li, Xiaodong Yang, Tong Zhao, Yikai Wu, Yuhan Cui, Bin Xu, Yanwei Uhlig, Steve Zhang, Gong Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Central Research Institute 2012 Labs Huawei Technologies Co. Ltd. Theory Lab Hong Kong Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom

ISBN: (纸本)9798350322279

In this paper, we propose a new type of item in data streams, called simplex items. Simplex items have frequencies in consecutive p windows that can be approximated by a polynomial of degree at most k, where k = 0, 1, 2. These low-order representable simplex items have a wide range of potential applications. For example, when k = 1, we can leverage these items whose frequency has obvious linear increase or decrease to speed up the running time of a class of machine learning models and detect network attacks such as distributed denial-of-service (DDoS), etc. To find k-degree simplex items in real time, we propose a novel sketch, namely X-Sketch, to accurately record simplex items in a compact space. The key idea of X-Sketch is to effectively filter out non-simplex items with less memory overhead, and then monitor the remaining potential simplex items and keep those items with more consecutive windows. We conduct extensive experiments, and the experimental results show that the F1 Score of X-Sketch is on average 68.6%, 57.9%, and 42.2% higher than the baseline solution for k = 0, 1, 2, respectively. Finally, we also provide a case study that applies X-Sketch to "accelerate"the two machine learning models through end-to-end experiments. We have released our source code at GitHub. © 2023 IEEE.

关键词： Denial-of-service attack

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Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第6期34卷 4942-4956页

作者： Huang, Aiping Li, Lijian Zhang, Le Niu, Yuzhen Zhao, Tiesong Lin, Chia-Wen Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou350108 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering The Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China Institute of Communications Engineering National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group. © 1991-2012 IEEE.

关键词： Semantics

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MAAS-Net: Efficient Multi-Scale Attention Aggregation Supervision Network for Tire Tread Segmentation 4

MAAS-Net: Efficient Multi-Scale Attention Aggregation Superv...

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4th International Conference on Industrial Automation, Robotics and Control engineering, IARCE 2024

作者： Ji, Xu Tian, Yongqin Xu, Yibo Liu, Dehua Zhao, Wenyi Zhao, Liang Yang, Mu Yang, Huihua School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Information Engineering Henan Institute of Science and Technology Xinxiang China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Industrial Technology Co. Ltd. Beijing China

ISBN: (纸本)9798350380323

Overlap detection plays a critical role in ensuring tire safety performance, as defects like overlapping anomalies due to mold aging and wear elevate the risk of tire blowouts. However, traditional edge detection algorithms entail a cumbersome manual parameter adjustment process and are limited to fixed hardware equipment scenarios, thereby compromising the flexibility and robustness of surface defect detection. Moreover, conventional image segmentation methods often fall short in achieving high-precision segmentation of the tread area. In this paper, we introduce MAAS-Net, a network employing a U-shaped encoder-decoder architecture. Integrated within are Convolutional Block Attention Modules (CBAM) after each decoding stage to generate multi-resolution attention feature maps. These maps are progressively fused into Multi-scale Aggregated Supervision (MAS) to enhance the model's capacity to discern features across various levels and scales. Additionally, to enhance the model's accuracy near the tread edge area, we propose a post-processing method named ContourFix. Leveraging data augmentation and multi-task learning, ContourFix aims to replace unstable prediction values near the edges with high-confidence prediction values inside the target area. The efficacy of these techniques is evaluated using a dataset of 3D tire tread. Experimental results demonstrate that MAAS-Net significantly outperforms conventional semantic segmentation models in segmenting tire treads, and ContourFix effectively enhances the accuracy of the segmentation model near the regional boundary. © 2024 IEEE.

关键词： Semantic Segmentation

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Adaptive Fusion Attention Network for Facial Expression Recognition 23

Adaptive Fusion Attention Network for Facial Expression Reco...

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Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition

作者： Guodong Li Cheng Peng Kun Zou Bowen Zhang School of Computer Science and Engineering(School of Cybersecurity) University of Electronic Science and Technology of China China School of Computing University of Electronic Science and Technology of China Zhongshan Institute China College of Big Data and Internet Shenzhen Technology University China

ISBN: (纸本)9798400707988

In this paper, we propose a novel facial expression recognition network, called Adaptive Fusion Attention Network (AFAN). Facial expression recognition is different from traditional image classification tasks. There are potentially similar features between different categories, and the differences between them may be subtle and difficult to distinguish. To solve this problem, we propose AFAN, which not only can get multi-dimensional features, but also can generate an attention map with stronger representation ability. The comparative experiments on AffectNet-7 dataset verify the excellent performance of the proposed method on facial expression recognition tasks.

关键词： Adaptive convolutional blocks Facial expression recognition Fine-grained classification Multi-head attention networks

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Identification and Visualization of Spatial and Temporal Trends in Textile Industry

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computers, Materials & Continua 2023年第2期74卷 4165-4181页

作者： Umair Yousaf Muhammad Asif Shahbaz Ahmed Noman Tahir Azeem Irshad Akber Abid Gardezi Muhammad Shafiq Jin-Ghoo Choi Habib Hamam Department of Computer Science National Textile UniversityFaisalabad38000Pakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Faculty of Engineering Uni de MonctonMonctonNB E1A3E9Canada International Institute of Technology and Management Commune d’AkandaBPLibreville1989Gabon School of Electrical Engineering Department of Electrical and Electronic Eng.ScienceUniversity of JohannesburgJohannesburg2006South Africa Spectrum of Knowledge Production&Skills Development Sfax3027Tunisia

The research volume increases at the study rate,causing massive text *** to these enormous text corpora,we are drowning in data and starving for ***,recent research employed different text mining approaches to extract information from this text *** proposed approaches extract meaningful and precise phrases that effectively describe the text’s *** extracted phrases are commonly termed ***,these key phrases are employed to determine the different fields of study ***,these key phrases can also be used to determine the spatiotemporal trends in the various research *** this research,the progress of a research field can be better revealed through spatiotemporal bibliographic trend ***,an effective spatiotemporal trend extraction mechanism is required to disclose textile research trends of particular regions during a specific *** study collected a diversified dataset of textile research from 2011–2019 and different countries to determine the research *** data was collected from various open access ***,this research determined the spatiotemporal trends using quality *** research also focused on finding the research collaboration of different countries in a particular research *** research collaborations of other countries’researchers show the impact on import and export of those *** visualization approach is also incorporated to understand the results better.

关键词： Text mining spatiotemporal trend research collaboration

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Early Detection of Activity in Untrimmed Videos Using Dense Optical Flow and Deep Neural Network 5

Early Detection of Activity in Untrimmed Videos Using Dense ...

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5th International Conference on electronics, Communication and Aerospace Technology, ICECA 2021

作者： Poonkodi, M. Vadivu, G. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India Big Data Analytics School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9781665435246

computer Vision is playing aremarkable role right from essentials to entertainment and thus trying to turn computer as a 'seeing' machine. Having widespread applications in most of the real world domain like healthcare, surveillance., space and social media, yet it is still challenging to match human intelligence in understanding the underlying context. Recognition of a task performed by human is important to learn the happenings, but predicting it, from the activities' initial subset becomes an aid to overcome adversities in near future. Numerous algorithms and techniques are present, yet a gap exists due to the environment in which it is recorded, illumination condition,overlapping of men and materials on captured scenes *** lead to the requirement of high power computing devices for recognition, which is considerably *** this work, Dense OPnet (Dense optical flow and Convolution neural Net) is proposed which takes in the RGB input video, converted to dense optical flow frames and then fed to three layered CNN for recognition and early detection of an activity using N/2 frames of the total input video. This method is evaluated on two benchmark datasetlike J-HMDB21 and UCF50 giving an accuracy of 93.4% and 74.3% on recognition, 68.2% and 50.24% on early action detection respectively. © 2021 IEEE.

关键词： computer vision

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An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

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Trajectory Generation by Sparse Demonstration Learning and Minimum Snap-based Optimization

Trajectory Generation by Sparse Demonstration Learning and M...

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International Conference on Automation and Computing (ICAC)

作者： Taoying Xu Haoping She Weiyong Si Chuanjun Li School of Aerospace Engineering Beijing Institute of Technology School of Computer Science and Electronic Engineering University of Essex

ISBN: (数字)9798350360882

ISBN: (纸本)9798350360899

In this paper, dynamic time warping function is used to establish an optimal control system for four-rotor unmanned aerial vehicle (UAV) to learn how to optimize trajectory planning from sparse demonstration. By continuous Pontryagin Differentiable Programming, UAV learns the objective function based on sparse waypoints demonstration. However, due to the small sample data of sparse demonstration learning, there is a problem of low precision, and Pontryagin’s Minimum Principle itself has the limitation of easily falling into the local optimal solution. So, this paper adopts the Minimum Snap trajectory algorithm that meets the dynamic constraints of the agent to generate a planned trajectory, to weighted combination with learning trajectory solved based on continuous Pontryagin Differentiable Programming, and the resulting optimized trajectory has the advantages of small demonstration learning difference loss, reasonable time allocation and reasonable planning, so that UAV can have certain generalization capability and optimize a reasonable trajectory with less energy loss. Finally, the feasibility of the proposed method is verified by the simulation experiment of the four-rotor UAV.

关键词： Trajectory planning Heuristic algorithms Optimal control Programming Autonomous aerial vehicles Linear programming Trajectory Planning Resource management Vehicle dynamics

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