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Underwater estimation of audio signal prediction using fruit fly algorithm and hybrid wavelet neural network

Journal of Reliable Intelligent Environments 2022年第2期8卷 211-221页

作者： Sagayam, K. Martin Ghosh, Ahona Bhushan, Bharat Andrew, J. Cengiz, Korhan Elngar, Ahmed A. Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computational Science Brainware University Kolkata India School of Engineering and Technology Sharda University Greater Noida India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Electrical and Electronics Engineering Traka University Edirne Turkey Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef City62511 Egypt

Underwater acoustic signal is one of its most vital features in oceanography for predicting the local variants, helps to process the signal, and also assists to discover non-stationary signals. wavelet neural network (WNN) architecture acquires a huge benefit from the artificial neural network (ANN) and time-frequency localizing features of wavelet analysis (WA). However, the system could train using the ANN model, it has tendency of falling into local minima and generating convergence. A wavelet-aided neural network is integrated with a meta-heuristic optimizer named fruit fly algorithm (FFA) that aids our proposed system in which reduces the computational complexity and improves performance measures. The main objective of the proposed work is of modelling an optimized wavelet neural network for efficient measurement of the mean absolute error and the root mean square error. The simulation of the sound signals has been performed with the use of optimized neural networks that has predicted the unknown signal. The proposed model is assessed by comparing the outcomes with the complements of it, i.e. the traditional neural networks. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Forecasting

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Convolutional Neural Network-based image tamper detection with Error Level Analysis 2

Convolutional Neural Network-based image tamper detection wi...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Manjunatha, S. Swetha, M.D. Rashmi, S. Subramanian, Ananda Kumar Vinoth Kumar, V. Mallikarjuna Swamy, S. Global Academy of Technology Department of Information Science and Engineering Bengaluru India BNM Institute of Technology Department of Computer Science and Engineering Bengaluru India Bengaluru India Vit - University School of Computer Science Engineering Dept of Computational Intelligence Vellore India Vit - University School of Computer Science Engineering and Information Systems Vellore India Jss Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350306415

Photography is the most important, powerful, and reliable means of expression. Today, digital images not only provide disinformation but also act as agents for secret communication. Users and editing professionals work with digital images for a variety of purposes. Images are often regarded as facts or proof of reality, so they are misleading and fake news or publications of any form that use images manipulated in a highly misleading way. To recognize image tampering needs multiple image data and a model that can handle all the pixels in the image. Furthermore, training the data more efficiently and needed flexibility support everyday use. Models based on Deep learning such as Convolutional Neural Networks with error level analysis (ELA) are the perfect solution. © 2024 IEEE.

关键词： Convolutional Neural Network Deep Learning Efficiency Error Level Analysis Image Forgery Detection

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Aspects of motile microorganism and thermophoretic particle deposition in the binary chemical reactive ternary hybrid nanofluid past a porous microchannel

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ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik 2025年第5期105卷

作者： Ajjanna Roja Umair Khan Thulasiram Ramachandran Ankur Kulshreshta Nizampatnam Neelima Chander Prakash El-Sayed M. Sherif Ioan Pop Department of Mathematical and Computational Sciences Sri Sathya Sai University for Human Excellence Kalaburagi Karnataka India Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Mathematics Faculty of Science Sakarya University Serdivan/Sakarya Turkey Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-Be University) Bangalore Karnataka India Department of Mechanical & Aerospace Engineering NIMS Institute of Engineering & Technology NIMS University Rajasthan Jaipur India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India School of Mechanical Engineering Rayat Bahra University Mohali India Mechanical Engineering Department College of Engineering King Saud University Al-Riyadh Saudi Arabia Department of Mathematics Babes-Bolyai University Cluj-Napoca Romania

This study explores the optimization of Casson fluids, focusing on the role of ternary hybrid nanofluids in enhancing thermal efficiency in industrial and engineering applications. Specifically, the impact of thermophoretic particles and chemical reactions on bioconvective Casson ternary hybrid nanofluid flow through a vertical microchannel embedded with porous media is examined. The governing equations are reduced using similarity transformations, and the resulting nonlinear equations are solved using the Runge–Kutta–Fehlberg 4th and 5th order method. The findings reveal that increasing the thermophoretic constraint leads to a decrease in nanoparticle concentration, highlighting the impact of thermophoretic forces on particle movement and deposition. Additionally, the porous parameter causes a reduction in flow velocity, which is observed to affect the overall fluid dynamics in the system. The presence of variable thermal conductivity enhances the thermal field, suggesting that temperature distribution can be significantly improved. Moreover, increasing the nanoparticle volume fraction enhances temperature distribution, indicating a positive correlation between nanoparticle concentration and thermal efficiency. On the other hand, increasing the thermophoretic constraint results in a decrease in mass transfer rate, emphasizing the trade-off between enhanced thermal performance and reduced mass transport. These findings are valuable for the design of advanced micro-cooling devices, micro-heat exchangers, micro-pumps, and macro mixing technologies, where both thermal and mass transfer performance are critical.

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Revolutionizing Face Detection: VisionaryNet's Breakthrough in Unraveling Facial Camplexity 2

Revolutionizing Face Detection: VisionaryNet's Breakthrough ...

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2nd International Conference on Computer, communication and Control, IC4 2024

作者： Nallarasan, V. Arivazhagan, N. Lakshminarayanan, R. Sharmila, D. Sangolgi, Nagaveni B Anand, M. Srm Institute of Science and Technology School of Computing Department of Networking and Communications Chengalpattu Kattankulathur India Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chengalpattu Kattankulathur India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Sharnbasva University Department of Electronics and Communication Engineering Kalaburagi India Srm Institute of Science and Technology Department of Computing Technologies Chengalpattu Kattankulathur India

ISBN: (纸本)9798350387933

The initial step in facial recognition and analysis systems often involves facial detection. Traditional methods like Haar cascades and directed gradient histograms relied on manually designed features, limiting their effectiveness in diverse image environments. However, the emergence of deep learning in computer vision has accelerated the development of face detection frameworks, significantly improving accuracy. Detecting small-scale faces, varying positions, occlusions, blurs, and partially obscured faces in uncontrolled settings remains a persistent challenge in face identification. To address this, we propose a new approach called 'Visionary Net.' This single-stage face detector has undergone refinements, boosting both detection speed and accuracy. We tested Visionary Net on two well-known datasets: Mega Face and CelebA. Notably, on the Mega Face benchmark, our method achieved an impressive AP of 48.5 at a speed of 15.2 FPS using single-scale inference and an exceptional AP of 52.7 with multi-scale inference. Implementing it with the PyTorch framework resulted in a remarkable 97.3% accuracy in successfully detecting faces. Our comprehensive experiments show that Visionary Net surpasses existing models, demonstrating superior detection and recognition capabilities across various evaluation metrics. This innovative algorithm holds significant promise for robust face detection in challenging real-world scenarios. © 2024 IEEE.

关键词： Face recognition

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ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness Neuroimaging

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Deblurring Algorithm Using Alternating Low Rank Augmented Lagrangian with Iterative Priors 1

Deblurring Algorithm Using Alternating Low Rank Augmented La...

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1st International Conference on Advances in computational science and engineering, ICACSE 2020

作者： Tojo, Laya Gurushankar, K. Maik, Vivek Devi, Manju Department of Electronics and Communication Engineering Oxford College of Engineering Bengaluru Karnataka India Laboratory of Computational Modeling of Drugs Higher Medical and Biological School South Ural State University Chelyabinsk454 080 Russia Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India

The paper focuses on the Enhanced Augmented Lagrangian method with sparse regularization for image deblurring. The method suggested by ALTERNATING LOW RANK AUGMENTED LAGRANGIAN WITH ITERATIVE A PRIOR is novel in the following ways. (i) Faster convergence leading to speeder execution through rank regulations (ii) using derivatives and low rank together as regularization priors (iii) penalty and regularization weights ensure that each iteration hits a global minimum with a steep descent. The proposed method begins with the lowest rank matrix, which is the sparsest matrix available. The final deblurred result is very successful in achieving good dB improvements through rank regulation. © Published under licence by IOP Publishing Ltd.

关键词： Image reconstruction

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SuccSite:Incorporating Amino Acid Composition and Informative k-spaced Amino Acid Pairs to Identify Protein Succinylation Sites

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Genomics, Proteomics & Bioinformatics 2020年第2期18卷 208-219页

作者： Hui-Ju Kao Van-Nui Nguyen Kai-Yao Huang Wen-Chi Chang Tzong-Yi Lee Department of Computer Science and Engineering Yuan Ze UniversityTaoyuan 32003TaiwanChina Department of Information Technology University of Information and Communication TechnologyThai Nguyen 1000Vietnam School of Science and Engineering The Chinese University of Hong KongShenzhen 518172China Warshel Institute for Computational Biology The Chinese University of Hong KongShenzhen 518172China Institute of Tropical Plant Sciences Cheng Kung UniversityTainan 701TaiwanChina

Protein succinylation is a biochemical reaction in which a succinyl group(-CO-CH2-CH2-CO-)is attached to the lysine residue of a protein *** succinylation plays important regulatory roles in living ***,studies in this field are limited by the difficulty in experimentally identifying the substrate site specificity of lysine *** facilitate this process,several tools have been proposed for the computational identification of succinylated lysine *** this study,we developed an approach to investigate the substrate specificity of lysine succinylated sites based on amino acid *** experimentally verified lysine succinylated sites collected from public resources,the significant differences in position-specific amino acid composition between succinylated and non-succinylated sites were represented using the Two Sample Logo *** findings enabled the adoption of an effective machine learning method,support vector machine,to train a predictive model with not only the amino acid composition,but also the composition of k-spaced amino acid *** the selection of the best model using a ten-fold crossvalidation approach,the selected model significantly outperformed existing tools based on an independent dataset manually extracted from published research ***,the selected model was used to develop a web-based tool,SuccSite,to aid the study of protein *** proteins were used as case studies on the website to demonstrate the effective prediction of succinylation *** will regularly update SuccSite by integrating more experimental *** is freely accessible at http://***/SuccSite/.

关键词： Protein succinylation Succinyl group Substrate specificity Amino acid composition k-spaced amino acid pair composition

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Research and implementation of complex task based on DMP 9

Research and implementation of complex task based on DMP

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9th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2020

作者： Jia, Jie Wang, Yucan Zuo, Guoyu Cao, Yilin Yu, Naigong Beijing University of Technology Faculty of Information Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China University of Science and Technology School of Computer and Communication Engineering Beijing China

Aiming at the problem of poor task versatility in the strategy learning of complex tasks and cannot be generalized to obstacle environments, a method for robots learning from demonstration based on Dynamic Movement Primitive (DMP) is proposed. This method divides the demonstration task into several subtasks, and uses DMP to model each subtask. We utilize the local weighted regression algorithm to learn the corresponding weight of each subtask. Then the task trajectory is obtained by sequentially combining the generated subtask trajectory. The obstacle avoidance coupling item is added to the DMP framework to generate the obstacle avoidance task trajectory. The UR5 robotic arm is used to perform grasp and place tasks on the simulation platform as a demonstration task to verify the method in this article. © 2020 IEEE.

关键词： Trajectories

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An Internet of Things Based system for Road Surface Condition Assessment Using Machine Learning

An Internet of Things Based system for Road Surface Conditio...

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AFRICON Conference

作者： Michael Ochiel Janeth Riwa Japheth Abashe Shubi Kaijage Judith Leo School of computational and communication science and engineering The Nelson Mandela African Institution of Science and Technology Arusha Tanzania

Road surface condition assessment is crucial for maintaining road safety and preserving road infrastructure. However, techniques for assessing road conditions employed in Tanzania are manual and labor-intensive, leading to slow and ineffective repair practices. To address these challenges, this project developed an Internet of Things-based system for Road Surface Condition Assessment using Machine Learning. The system collects real-time data on road surface conditions using camera and gyroscope sensors; processes the data using machine learning algorithms to detect defects on the road surface. The data is used for decision support by the maintenance authorities to ease their process by automation of road condition surveys. The effectiveness of the developed system was evaluated through a qualitative study that collected data on road condition assessment practices in Tanzania. The results indicate that the proposed system provides a more efficient, cost-effective, and comprehensive method for assessing road conditions in Tanzania. The study found that the proposed system facilitated efficient road maintenance. This project highlights the potential of IoT and machine learning in addressing road safety and labor requirement challenges faced by developing countries in the field of infrastructure management, it also offers a valuable contribution to the field of transportation and engineering.

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