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A Novel Smartphone Recommendation System Using Ensemble Machine Learning

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A Novel Smartphone Recommendation System Using Ensemble Mach...

computer Science and Data engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Ahmad Almadhor Sidra Abbas Gabriel Avelino Sampedro Mideth Abisado Thippa Reddy Gadekallu College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia Department of Computer Science COMSATS University Islamabad Islamabad Pakistan Faculty of Information and Communication Studies University of the Philippines Open University Los Baños Philippines Center for Computational Imaging and Visual Innovations De La Salle University Manila Philippines College of Computing and Information Technologies National University Manila Philippines Zhongda Group Jiaxing City Zhejiang Province China Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu India College of Information Science and Engineering Jiaxing University Jiaxing China Division of Research and Development Lovely Professional University Phagwara India

Due to the proliferation of internet evaluations brought on by the rising demand for smartphones, consumers find it challenging to make accurate selections when purchasing. In this paper, we offer ensemble voting methods based on TF-IDF (Term Frequency-Inverse Document Frequency) features for clas-sifying mobile phone ratings. We use a recently assembled dataset comprising over 13,000 smartphone reviews from the Flipkart website. The suggested approach includes feature extraction using the TF-IDF, data cleaning, balancing, and voting-based model prediction. To identify the recently created Flipkart dataset, the suggested method created an ensemble voting mechanism based on machine learning techniques. According to the experimental findings, the suggested method performs more accurately and efficiently than conventional machine learning techniques. At 98.0 %, the model achieved the greatest accuracy. The suggested method can be expanded to additional e-commerce platforms with sizable datasets of online evaluations and assist customers in making updated purchase preferences.

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ScanMix: Learning from Severe label Noise via Semantic Clustering and Semi-Supervised Learning

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arXiv 2021年

作者： Sachdeva, Ragav Cordeiro, Filipe Rolim Belagiannis, Vasileios Reid, Ian Carneiro, Gustavo Visual Geometry Group Department of Engineering Science University of Oxford United Kingdom School of Computer Science Australian Institute for Machine Learning Australia Visual Computing Lab Department of Computing Universidade Federal Rural de Pernambuco Brazil Otto-von-Guericke-Universität Magdeburg Germany Centre for Vision Speech and Signal Processing University of Surrey United Kingdom

We propose a new training algorithm, ScanMix, that explores semantic clustering and semi-supervised learning (SSL) to allow superior robustness to severe label noise and competitive robustness to non-severe label noise problems, in comparison to the state of the art (SOTA) methods. ScanMix is based on the expectation maximisation framework, where the E-step estimates the latent variable to cluster the training images based on their appearance and classification results, and the M-step optimises the SSL classification and learns effective feature representations via semantic clustering. We present a theoretical result that shows the correctness and convergence of ScanMix, and an empirical result that shows that ScanMix has SOTA results on CIFAR-10/-100 (with symmetric, asymmetric and semantic label noise), Red Mini-ImageNet (from the Controlled Noisy Web labels), Clothing1M and WebVision. In all benchmarks with severe label noise, our results are competitive to the current SOTA. © 2021, CC BY.

关键词： Semantics

MAPA: Multi-Accelerator Pattern Allocation Policy for Multi-Tenant GPU Servers

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MAPA: Multi-Accelerator Pattern Allocation Policy for Multi-...

Supercomputing Conference

作者： Kiran Ranganath Joshua D. Suetterlein Joseph B. Manzano Shuaiwen Leon Song Daniel Wong Department of Electrical and Computer Engineering University of California Riverside CA USA High-Performance Computing Group Pacific Northwest National Lab WA USA Future System Architecture Lab School of Computer Science University of Sydney Sydney Australia

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

Multi-accelerator servers are increasingly being deployed in shared multi-tenant environments (such as in cloud data centers) in order to meet the demands of large-scale compute-intensive workloads. In addition, these accelerators are increasingly being inter-connected in complex topologies and workloads are exhibiting a wider variety of inter-accelerator communication patterns. However, existing allocation policies are ill-suited for these emerging use-cases. Specifically, this work identifies that multi-accelerator workloads are commonly fragmented leading to reduced bandwidth and increased latency for inter-accelerator communication. We propose Multi-Accelerator Pattern Allocation (MAPA), a graph pattern mining approach towards providing generalized allocation support for allocating multi-accelerator workloads on multi-accelerator servers. We demonstrate that MAPA is able to improve the execution time of multi-accelerator workloads and that MAPA is able to provide generalized benefits across various accelerator topologies. Finally, we demonstrate a speedup of 12.4% for 75th percentile of jobs with the worst case execution time reduced by up to 35% against baseline policy using MAPA.

关键词： Network topology Processor scheduling High performance computing Graphics processing units Throughput Hardware Topology

SpEx+: A complete time domain speaker extraction network 21

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SpEx+: A complete time domain speaker extraction network

21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020

作者： Ge, Meng Xu, Chenglin Wang, Longbiao Chng, Eng Siong Dang, Jianwu Li, Haizhou Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Engineering Nanyang Technological University Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan Department of Electrical and Computer Engineering National University of Singapore Singapore Machine Listening Lab University of Bremen Germany

ISBN: (纸本)9781713820697

Speaker extraction aims to extract the target speech signal from a multi-talker environment given a target speaker's reference speech. We recently proposed a time-domain solution, SpEx, that avoids the phase estimation in frequency-domain approaches. Unfortunately, SpEx is not fully a time-domain solution since it performs time-domain speech encoding for speaker extraction, while taking frequency-domain speaker embedding as the reference. The size of the analysis window for time-domain and the size for frequency-domain input are also different. Such mismatch has an adverse effect on the system performance. To eliminate such mismatch, we propose a complete time-domain speaker extraction solution, that is called SpEx+. Specifically, we tie the weights of two identical speech encoder networks, one for the encoder-extractor-decoder pipeline, another as part of the speaker encoder. Experiments show that the SpEx+ achieves 0.8dB and 2.1dB SDR improvement over the state-of-the-art SpEx baseline, under different and same gender conditions on WSJ0-2mix-extr database respectively. Copyright © 2020 ISCA

关键词： Extraction

Unsupervised partial point set registration via joint shape completion and registration

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arXiv 2020年

作者： Li, Xiang Wang, Lingjing Fang, Yi NYU Multimedia and Visual Computing Lab NYU Tandon Abu Dhabi United Arab Emirates Department of Electrical and Computer Engineering NYU Tandon United States Department of Electrical and Computer Engineering NYU Abu Dhabi United Arab Emirates

We propose a self-supervised method for partial point set registration. While recent proposed learning-based methods have achieved impressive registration performance on the full shape observations, these methods mostly suffer from performance degradation when dealing with partial shapes. To bridge the performance gaps between partial point set registration with full point set registration, we proposed to incorporate a shape completion network to benefit the registration process. To achieve this, we design a latent code for each pair of shapes, which can be regarded as a geometric encoding of the target shape. By doing so, our model does need an explicit feature embedding network to learn the feature encodings. More importantly, both our shape completion network and the point set registration network take the shared latent codes as input, which are optimized along with the parameters of two decoder networks in the training process. Therefore, the point set registration process can thus benefit from the joint optimization process of latent codes, which are enforced to represent the information of full shape instead of partial ones. In the inference stage, we fix the network parameter and optimize the latent codes to get the optimal shape completion and registration results. Our proposed method is pure unsupervised and does not need any ground truth supervision. Experiments on the ModelNet40 dataset demonstrate the effectiveness of our model for partial point set registration. Copyright © 2020, The Authors. All rights reserved.

关键词： Encoding (symbols)

LongReMix: Robust Learning with High Confidence Samples in a Noisy label Environment

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arXiv 2021年

作者： Cordeiro, Filipe R. Sachdeva, Ragav Belagiannis, Vasileios Reid, Ian Carneiro, Gustavo School of Computer Science Australian Institute for Machine Learning Australia Visual Geometry Group Department of Engineering Science University of Oxford United Kingdom Visual Computing Lab Department of Computing Universidade Federal Rural de Pernambuco Brazil Otto-von-Guericke-Universität Magdeburg Germany Centre for Vision Speech and Signal Processing University of Surrey United Kingdom

State-of-the-art noisy-label learning algorithms rely on an unsupervised learning to classify training samples as clean or noisy, followed by a semi-supervised learning (SSL) that minimises the empirical vicinal risk using a labelled set formed by samples classified as clean, and an unlabelled set with samples classified as noisy. The classification accuracy of such noisy-label learning methods depends on the precision of the unsupervised classification of clean and noisy samples, and the robustness of SSL to small clean sets. We address these points with a new noisy-label training algorithm, called LongReMix, which improves the precision of the unsupervised classification of clean and noisy samples and the robustness of SSL to small clean sets with a two-stage learning process. The stage one of LongReMix finds a small but precise high-confidence clean set, and stage two augments this high-confidence clean set with new clean samples and oversamples the clean data to increase the robustness of SSL to small clean sets. We test LongReMix on CIFAR-10 and CIFAR-100 with introduced synthetic noisy labels, and the real-world noisy-label benchmarks CNWL (Red Mini-ImageNet), WebVision, Clothing1M, and Food101-N. The results show that our LongReMix produces significantly better classification accuracy than competing approaches, particularly in high noise rate problems. Furthermore, our approach achieves state-of-the-art performance in most datasets. The code is available at https://***/filipe-research/LongReMix. © 2021, CC BY.

关键词： Deep learning

Microscopic-based analysis of nuclei in spheroids via SUNSHINE: An on-chip workflow integrating optical clearing, fluorescence calibration and supervoxel segmentation

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computers in Biology and Medicine 2025年 187卷 109761-109761页

作者： Lin, Chia-Hsiang Leng, Zi-Chao Yu, Chien-Hsin Deori Bharali, Lui Kirtan Lin, Cheng-Li Mao, Bin-Hsu Tu, Ting-Yuan Department of Electrical Engineering National Cheng Kung University Tainan City70101 Taiwan Institute of Computer and Communication Engineering Department of Electrical Engineering National Cheng Kung University Tainan City70101 Taiwan Department of Biomedical Engineering College of Engineering National Cheng Kung University Tainan City70101 Taiwan Department of Biotechnology and Medical Engineering National Institute of Technology Odisha Rourkela769008 India Department of Orthopedic Surgery National Cheng Kung University Hospital Tainan City704 Taiwan Skeleton Materials and Biocompatibility Core Lab Research Center of Clinical Medicine National Cheng Kung University Hospital Tainan City704 Taiwan National Cheng Kung University Hospital Tainan City704 Taiwan Musculoskeletal Research Center Innovative Headquarter National Cheng Kung University Tainan City70101 Taiwan Miin Wu School of Computing National Cheng Kung University Tainan City70101 Taiwan Meta-nanoPhotonics Center National Cheng Kung University Tainan City70101 Taiwan Medical Device Innovation Center National Cheng Kung University Tainan City70101 Taiwan

Multicellular spheroids (MCSs) are increasingly employed as 3D cell culture models in biomedical research due to their ability to effectively replicate in vivo cell interactions, making them suitable for high-throughput drug screening. Accurate cell counting is critical for data normalization, therapeutic evaluation, and exploration of culture conditions;however, affordable software solutions for 3D cell counting using microscopic images are limited. To fill this gap, we created SUNSHINE, an innovative on-chip analytical workflow that uniquely merges optical clearing, histogram matching (HM)-assisted fluorescence calibration, and simple linear iterative clustering (SLIC) supervoxel segmentation. This tool offers an efficient method for analyzing the characteristics and counts of fluorescence-labeled nuclei within MCSs. While optical clearing improves the penetration depth of microscopic imaging, deeper regions of thicker samples often yield faint fluorescence signals. SUNSHINE resolves this issue through the HM image post-processing algorithm. Moreover, SLIC is an effective alternative to traditional contour-wise segmentation, enabling the identification of irregularly shaped fluorescent nuclei. We found that SUNSHINE generated results comparable to commercial software like Imaris and machine learning (ML)-based tools, such as StarDist and Cellpose, in our analysis of the effects of seeding density and cell type on spheroid growth. We also used it to measure the volume and spatial distribution of nuclei, focusing on the hypoxic and peripheral regions of spheroids. Overall, this study finds that SUNSHINE serves as a valuable and economical approach for characterizing cellular activity and interactions in 3D, diminishing the reliance on costly proprietary software. © 2025

关键词： Cell culture

Survey on Deep Face Restoration: From Non-blind to Blind and Beyond

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arXiv 2023年

作者： Li, Wenjie Wang, Mei Zhang, Kai Li, Juncheng Li, Xiaoming Zhang, Yuhang Gao, Guangwei Deng, Weihong Lin, Chia-Wen The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China The Computer Vision Lab ETH Zürich Zürich Switzerland The School of Communication and Information Engineering Shanghai University Shanghai China The Nanyang Technological University Singapore The Intelligent Visual Information Perception Laboratory Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China The Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan

Face restoration (FR) is a specialized field within image restoration that aims to recover low-quality (LQ) face images into high-quality (HQ) face images. Recent advances in deep learning technology have led to significant progress in FR methods. In this paper, we begin by examining the prevalent factors responsible for real-world LQ images and introduce degradation techniques used to synthesize LQ images. We also discuss notable benchmarks commonly utilized in the field. Next, we categorize FR methods based on different tasks and explain their evolution over time. Furthermore, we explore the various facial priors commonly utilized in the restoration process and discuss strategies to enhance their effectiveness. In the experimental section, we thoroughly evaluate the performance of state-of-the-art FR methods across various tasks using a unified benchmark. We analyze their performance from different perspectives. Finally, we discuss the challenges faced in the field of FR and propose potential directions for future advancements. The open-source repository corresponding to this work can be found at https://***/24wenjie-li/Awesome-Face-Restoration. Copyright © 2023, The Authors. All rights reserved.

关键词： Restoration

Fault-Tolerant Quaternion Neural Network Fast Terminal Sliding Mode Control for Guidance and Navigation of UAV’s under Actuator Faults

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Fault-Tolerant Quaternion Neural Network Fast Terminal Slidi...

International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Fernando E. Serrano Ahmad Taher Azar Saim Ahmed Nashwa Ahmad Kamal Ibraheem Kasim Ibraheem Instituto de Investigación en Energía Universidad Nacional Autónoma de Honduras Tegucigalpa Honduras Instituto de Robóticae Informática Industrial IRI-CSIC Universidad Politécnica de Cataluña Barcelona España College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Automated Systems and Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Engineering Cairo University Giza Egypt Department of Electrical Engineering College of Engineering University of Baghdad Baghdad Iraq Department of Electronics and Communication Engineering Uruk University Baghdad Iraq

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

This research presents a terminal sliding mode controller and quaternion neural network observer for unmanned aerial vehicle (UAV) guidance and navigation under actuator fault. Initially, to determine which of a quadrotor’s four actuators is failing, a quaternion neural network observer is created. The quadrotor dynamic model includes the DC motor dynamics, which is taken into account when designing the fault-tolerant observer. The quaternion neural network presents four failure modes pertaining to an actuator state in which instability and divergence from the healthy state occur when one of the actuators fails. Next, a fault-tolerant terminal sliding mode controller is engineered to deliver the necessary controller action when all actuators are functioning normally, or if one or more quadrotor UAV actuators malfunction. The rapid terminal sliding mode controller is built in order to acquire the diverse control laws when one or many actuators fail or when the system is in healthy state. The suitable Lyapunov functional at various time instants is taken into account throughout the construction of this fault-tolerant controller to restore optimal performance. In addition to the theoretical outcomes of this investigation, a simulation is run to demonstrate the effectiveness of the observer/controller approach and to contrast it with other sliding mode and neural network observer/controller combinations. The research study’s conclusions and discussions are given at the end.

关键词： Fault diagnosis Actuators Fault tolerance Quaternions Fault tolerant systems Neural networks Observers