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Exploring Non-convex Optimization in Sparse Signal Recovery: A Comparative Study of Non-convex Dantzig Selector and LASSO 1

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5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Devadas, Raghavendra M. Hiremani, Vani Sharma, Aditi Venugopal, Anita Ichangi, Raghavendra M. Kulkarni, Naveen Pavithra, N. Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Pune India Department of IT Unit Dhofar University Muscat Oman Sphoorthy Engineering College Nadargul Hyderabad India School of Engineering Dayananda Sagar University Kanakapura Road Bangalore India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (数字)9789819761036

ISBN: (纸本)9789819761029

Sparse signal recovery, lying at the core of signal processing and machine learning, has been traditionally attacked with convex optimization approaches such as LASSO and the Dantzig Selector. This paper investigates the field of non-convex optimization for sparse signal recovery, particularly comparing non-convex variants of Dantzig Selector and LASSO. A motivating choice of non-convex penalties may thus capture more complicated structures in sparse signals, offering a flexible framework for modeling complex sparsity patterns. Evaluate the performance of a non-convex Dantzig Selector in comparison with a non-convex variant of LASSO and investigate the sensitivity of the parameters in both methods. The non-convex Dantzig Selector introduces Huber loss, whereas the non-convex LASSO introduces a square root penalty. The performance metrics include sparsity and accuracy of recovered signals. The comparison between those methods in different scenarios gives the relative merits, adding up to the discussion on what methods to use for the reconstruction of signals. The study finds that the non-convex Dantzig Selector is of the final sparsity of the order of 10%, basically selecting only 10% of the variables. Contrarily, the non-convex LASSO is of about 90% sparsity, selecting about 90% of the variables. Finally, the non-convex LASSO has final accuracy closer to 1 with low prediction error, while the non-convex Dantzig Selector has final accuracy closer to 0.6, pointing to the reality of higher prediction error. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convex optimization

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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MyOmics: A Galaxy-based Analysis and Visualization Platform for Omics Data 2

MyOmics: A Galaxy-based Analysis and Visualization Platform ...

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2nd International Conference on Electronic Information Engineering and computer Communication, EIECC 2022

作者： Tao, Xin Chang, Cheng Yang, Chunde Xu, Kaikun School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Beijing Institute of Lifeomics Beijing102206 China Research Unit of Proteomics Driven Cancer Precision Medicine Chinese Academy of Medical Sciences Beijing102206 China

ISBN: (纸本)9781510663121

High-throughput omics technologies have been widely used in biomedical research. While it's still challenging for researchers without statistical foundations or programming skills to obtain biological insights from omics data. The published tools have some deficiencies in the environment configuration and analysis workflow definition. Here, we propose a one-stop analysis and visualization platform for omics data named MyOmics, which is freely available at http://***/. Its functions include data preprocessing, statistical analysis, functional enrichment analysis and visualization based on multi-index data frame structure. To evaluate the performance of MyOmics for omics studies of clinical cohorts, we re-analyzed a published Hepatocellular Carcinoma dataset and reproduced their main findings, which demonstrated that MyOmics is reliable and efficient to analyze omics data. Based on the Galaxy framework, MyOmics provides an easy-to-use solution for exploring omics data. © 2023 SPIE.

关键词： Data visualization

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A Novel Kite Cross Hexagonal Search Algorithm for Fast Block Motion Estimation 2

A Novel Kite Cross Hexagonal Search Algorithm for Fast Block...

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2nd International Conference of Modern Applications on Information and Communication technology, ICMAICT 2020

作者： Hamood, Tareq Zaid Abdulmunem, Matheel Emaduldeen Unit of Remote Sensing College of Science University of Baghdad Baghdad Iraq Department of Computer Science University of Technology Baghdad Iraq

The performance quality and searching speed of Block Matching (BM) algorithm are affected by shapes and sizes of the search patterns used in the algorithm. In this paper, Kite Cross Hexagonal Search (KCHS) is proposed. This algorithm uses different search patterns (kite, cross, and hexagonal) to search for the best Motion Vector (MV). In first step, KCHS uses cross search pattern. In second step, it uses one of kite search patterns (up, down, left, or right depending on the first step). In subsequent steps, it uses large/small Hexagonal Search (HS) patterns. This new algorithm is compared with several known fast block matching algorithms. Comparisons are based on search points and Peak Signal to Noise Ratio (PSNR). According to results obtained in this paper, KCHS needs less search time than others algorithms and gives very acceptable performance quality. © Published under licence by IOP Publishing Ltd.

关键词： Motion estimation

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Experimental Evaluation of a Passive Upper Limb Exoskeleton for High Voltage Live-Line Operations 16

Experimental Evaluation of a Passive Upper Limb Exoskeleton ...

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16th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2024

作者： Zhao, Dongwei Wang, Song Sun, Shouqian Zhang, Xuequn Vanni, Kimmo Zhejiang University College of Computer Science and Technology Hangzhou China Zhejiang University Innovation Center of Yangtze River Delta Jiaxing China Hangzhou Kuntai Robotics Co. Ltd. Hangzhou China Häme University of Applied Sciences Smart Research Unit Hämeenlinna Finland

ISBN: (纸本)9798331540241

In high-voltage live-line work, workers often need to hold insulating tubes and other tools for extended periods, leading to upper limb muscle strain, especially among inexperienced workers. Reducing muscle load can effectively prevent such hazards. This paper validates a newly designed passive upper limb exoskeleton through workspace analysis, biomechanical simulation, and filed experiment. The results demonstrate that the exoskeleton meets the movement space requirements of live-line workers. In the simulation environment, we made the model perform the lead connection task, the average muscle activation of the deltoid muscle when wearing the exoskeleton is reduced by 34.07%-55.56% compared to without the exoskeleton, and the average muscle activation of the trapezius muscle is reduced by 19.40%-25.63%. In live-line working experiments, the muscle activation of subjects decreased by 38.33% in the insulating protective cover construction task, 33.49% in the lead connection task, and 23.69% in the climbing down the pole task. Meanwhile, wearing the exoskeleton did not negatively impact task efficiency. These findings suggest that the exoskeleton can significantly reduce muscle strain without compromising productivity during high-voltage live-line work. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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Application of Remote Sensing for Drought Monitoring with NDVI-based Standardized Vegetation Index in Nan Province, Thailand 7

Application of Remote Sensing for Drought Monitoring with ND...

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7th International Conference on Digital Arts, Media and technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, NCON 2022

作者： Pachanaparn, Chatpong Jeefoo, Phaisarn Rojanavasu, Pornthep University of Phayao School of Information and Communication Technology Geographic Information Science Phayao Thailand University of Phayao Research Unit of Spatial Innovation Development Geographic Information Science School of Information and Communication Technology Phayao Thailand University of Phayao Computer Engineering School of Information and Communication Technology Phayao Thailand

ISBN: (纸本)9781665495103

The objective of the study is to develop a methodology to detect drought in Nan province, northern part of Thailand using vegetation spectrum in different period of the satellite passing approximately 11,420 ***. the Normalized Vegetation Index (NDVI) obtained from the MODIS data has been used in order to detect the vegetation condition in the study area. In addition, the Standardized Vegetation Index (SVI) was used to examine the area with the NDVI difference from the average value of NDVI in the same period. This can reflect the drought through the vegetation index. The study has discovered that in the year 2018, the drought in the area was the most severe. The second and the third most severe drought occurred in 2016 and 2020 respectively. This will be useful for the preparation of the drought mitigation in the area for concern agencies. © 2022 IEEE.

关键词： Remote sensing

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Imbalanced aspect categorization using bidirectional encoder representation from transformers

Imbalanced aspect categorization using bidirectional encoder...

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2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022

作者： Jayaraman, Ashok Kumar Murugappan, Abirami Trueman, Tina Esther Ananthakrishnan, Gayathri Ghosh, Ashish Department of Information Science and Technology Anna University Chennai600025 India Department of Computer Science University of the People United States Department of Information Technology VIT University Vellore632014 India Machine Intelligence Unit Indian Statistical Institute Kolkata700108 India

Sentiment analysis (also called opinion mining) is one of the widely used research fields of natural language processing. E-commerce service providers use this technique to analyze the sentiment of a product or a service in texts, posts, and comments. In particular, the service providers and users want to understand the sentiment on product aspect categories rather than the overall sentiment of a product. These aspect categories encounter the class imbalance problem. Therefore, the BERT (Bidirectional Encoder Representation from Transformers) based fine-tuning model is presented to deal with the imbalanced aspect categorization task. Specifically, this paper studies various data sampling techniques such as stratified random sampling (SRS), random undersampling (RUS), and random oversampling (ROS) for reducing the class imbalance problem. Empirically, the results show that the proposed BERT fine-tuning model with the SRS technique achieves better results. In particular, the model achieves 96.21% for the validation and 96.47% for testing using the news aggregator data. Similarly, the SMS spam collection data achieves 99.20% for the validation and 99.10% for testing. © 2023 The Authors. Published by Elsevier B.V.

关键词： Sentiment analysis

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Non-Uniform Illumination Attack for Fooling Convolutional Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Jain, Akshay Dubey, Shiv Ram Singh, Satish Kumar Santosh, K.C. Chaudhuri, Bidyut Baran Indian Institute of Information Technology Allahabad Computer Vision and Biometrics Lab Department of Information Technology Uttar Pradesh Prayagraj211015 India University of South Dakota AI Research Lab Department of Computer Science VermillionSD57069 United States Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata700108 India

Convolutional Neural Networks (CNNs) have made remarkable strides;however, they remain susceptible to vulnerabilities, particularly to image perturbations that humans can easily recognize. This weakness, often termed as 'attacks,' underscores the limited robustness of CNNs and the need for research into fortifying their resistance against such manipulations. This study introduces a novel Non-Uniform Illumination (NUI) attack technique, where images are subtly altered using varying NUI masks. Extensive experiments are conducted on widely-accepted datasets including CIFAR10, TinyImageNet, CalTech256 and NWPU-RESISC45 focusing on image classification with 12 different NUI masks. The resilience of VGG, ResNet, MobilenetV3-small, InceptionV3 and efficientNet b0 models against NUI attacks are evaluated. Our results show a substantial decline in the CNN models' classification accuracy when subjected to NUI attacks, due to changes in the image pixel value distribution, indicating their vulnerability under non-uniform illumination. To mitigate this, a defense strategy is proposed, including NUI-attacked images, generated through the new NUI transformation, into the training set. The results demonstrate a significant enhancement in CNN model performance when confronted with perturbed images affected by NUI attacks. This strategy seeks to bolster CNN models' resilience against NUI attacks. A comparative study with other attack techniques shows the effectiveness of the NUI attack and defense technique. © 2020 IEEE.

关键词： Convolutional neural networks

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Optihybrid: a modified firebug swarm optimization algorithm for optimal sizing of hybrid renewable power system

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Neural Computing and Applications 2024年第34期36卷 21517-21543页

作者： Abd El-Sattar, Hoda Kamel, Salah Hashim, Fatma A. Sabbeh, Sahar F. Luxor Higher Institute of Engineering and Technology Luxor85834 Egypt Department of Electrical Engineering Faculty of Engineering Aswan University Aswan81542 Egypt Faculty of Engineering Helwan University Cairo Egypt MEU Research Unit Middle East University Amman1183 Jordan College of Computer Science and Engineering University of Jeddah Jeddah21493 Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha13518 Egypt

In areas where conventional energy sources are unavailable, alternative energy technologies play a crucial role in generating electricity. These technologies offer various benefits, such as reliable energy supply, environmental sustainability, and employment opportunities in rural regions. This study focuses on the development of a novel optimization algorithm called the modified firebug swarm algorithm (mFSO). Its objective is to determine the optimal size of an integrated renewable power system for supplying electricity to a specific remote site in Dehiba town, located in the eastern province of Tataouine, Tunisia. The proposed configuration for the standalone hybrid system involves PV/biomass/battery, and three objective functions are considered: minimizing the total energy cost (COE), reducing the loss of power supply probability (LPSP), and managing excess energy (EXC). The effectiveness of the modified algorithm is evaluated using various tests, including the Wilcoxon test, boxplot analysis, and the ten benchmark functions of the CEC2020 benchmark. Comparative analysis between the mFSO and widely used algorithms like the original Firebug Swarm Optimization (FSO), Slime Mold Algorithm (SMA), and Seagull Optimization Algorithm (SOA) demonstrates that the proposed mFSO technique is efficient and effective in solving the design problem, surpassing other optimization algorithms. © The Author(s) 2024.

关键词： Hybrid power

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A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

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computer Systems science & Engineering 2024年第3期48卷 571-608页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Abdulatif Alabdulatif Ruba Abu Khurma Maribel Garcia-Arenas Pedro A.Castillo University Institute of Computing Chandigarh UniversityPunjab143001India Department of Computer Science and Engineering Chandigarh UniversityPunjab143001India Department of Computer Science and Engineering Uttaranchal UniversityUttarakhand248007India Department of Computer Science College of Computer Qassim UniversityBuraydah52571Saudi Arabia MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

As cloud computing usage grows,cloud data centers play an increasingly important *** maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance *** purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data *** aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management *** review revealed three task allocation research topics and seven performance management *** allocation research areas are resource allocation,load-Balancing,and *** management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network *** study proposes new techniques to enhance cloud computing work allocation and performance ***-comings in each approach can guide future *** research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and *** methodologies can steer future research to fill gaps in the literature.

关键词： Cloud computing data centre task allocation performance management resource utilization

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