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Improved coupled autoencoder based zero shot recognition using active learning

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Improved coupled autoencoder based zero shot recognition usi...

2019 IEEE Conference on information and Communication technology, CICT 2019

作者： Singh, Upendra Pratap Rakesh, Kaustubh Rishabh Kumar, Vipul Singh, Krishna Pratap Machine Learning and Optimization Lab Indian Institute of Information Technology Allahabad India

ISBN: (纸本)9781728153988

Zero shot learning seeks to learn useful patterns in the source domain and identify novel concepts in the target domain. This transfer learning paradigm has recently gained immense popularity given the inherent limitations in data acquisition and subsequent annotation for a task (or domain). While typical zero shot learning methods utilize all the classes (and their instances) in the source domain in a passive way, we, in our work, actively use only a handful of relevant classes for learning in the source domain. With this intelligent data subset, we jointly learn the source and target domain parameters using coupled semantic autoencoders. This joint learning reduces the projection domain shift problem. We further extend the above model for word embedding based semantic space as well. For classes with no word embedding, we have solved prototype sparsity problem by training a neural network with all classes that has one. This neural network seeks to learn a mapping from attribute space to word embedding space. Experiments on AWA2 and CUB-UCSD datasets confirm the superiority of our hybrid approach over state of art methods by up to 16% and 8% in attribute and word embedding space respectively. © 2019 IEEE.

关键词： Semantics

Hexagonally ordered microbowl arrays decorated with ultrathin CuInS_(2) nanosheets for enhanced photoelectrochemical performance

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Journal of Energy Chemistry 2020年第12期29卷 134-142页

作者： Ming Li Le Chen Yanjie Su Huan Yin Kexiang Hu State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyKey Laboratory of Optoelectronic Information Processing of Guangxi Colleges and Uniy/ersitiesGuilin 541004GuangxiChina Key Laboratory for Thin Film and Microfabrication of the Ministry of Education Department of Micro/Nano ElectronicsSchool of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China National and Local Joint Engineering Laboratory of Internet Technology on Mine School of Information and Control EngineeringChina University of Mining and TechnologyXuzhou 221008JiangsuChina School of Physics and Telecommunication Engineering Guangxi Colleges and Universities Key Lab of Complex System Optimization and Big Data ProcessingYulin Normal UniversityYulin 537400GuangxiChina

This paper demonstrates the design and fabrication of three-dimensional(3 D) hexagonally ordered microbowl arrays(MBAs) decorated with Cu In S2 nanosheets for enhanced photoelectrochemical(PEC) performance. The 3 D MBAs are fabricated by a micro-fabrication technique. The ultrathin CuInS2 nanosheets are grown on the 3 D electrodes by solvothermal transformation of Cu film. The photocurrent density of 3 D photocathode(CuInS2@MBAs) is about two times higher than that of the planar counterpart(CuInS2@Planar). The improved PEC performance can be ascribed to the elevated light trapping ability and the increased surface area for loading photocatalysts. In addition, CdS quantum dots as cocatalysts are modified onto the Cu In S2 nanosheets to further enhance the PEC activity because the formed p-n heterojunction can accelerate the separation of photogenerated carriers. As a result, the 3 D photocathode of CuInS2/CdS@MBAs shows an optimal incident photon to current efficiency of 10% at the wavelength of400 nm. It is believed that this work can be generalized to design other hierarchical 3 D photoelectrodes for improved solar water splitting.

关键词： Microbowl array CuInS_(2) Nanosheet Photocathode Photoelectrochemical performance

Spread & Peak Prediction of Covid-19 using ANN and Regression (Workshop Paper)

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Spread & Peak Prediction of Covid-19 using ANN and Regressio...

IEEE International Conference on Multimedia Big Data (BigMM)

作者： Anupam Prakash Piyush Sharma Indrajeet Kumar Sinha Upendra Pratap Singh Machine Learning and Optimization Lab Indian Institute of Information Technology Allahabad Prayagraj

ISBN: (数字)9781728193250

ISBN: (纸本)9781728193267

Covid-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus, has presented tough times for countries all over the world with number of cases and casualties running in millions. While virologists and doctors have spent sleepless nights to come up with a potent vaccine, the work life of government personnel including administrative staffs, hospital employees etc. has not been any easier. Amidst this turmoil, the common question crossing every mind is concerned with the statistics about this infection including expected number of infections, peak prediction etc. We try to answer these questions by analyzing the time series data of Covid-19 infections for certain hard-hit countries and states in India. A series of machine and deep learning models have been built to capture the infection distribution so that these models could predict the fate of this infection in the near future. We also make an attempt to predict the time when active cases would cease to increase.

关键词： Predictive models Data models COVID-19 Analytical models Linear regression Time series analysis Testing

Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

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arXiv 2022年

作者： Rahman, Chnoor M. Rashid, Tarik A. Ahmed, Aram Mahmood Mirjalili, Seyedali Applied Computer Department College of Medicals and Applied Sciences Charmo University Sulaimany Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Department of Information Technology Technical College of Informatics Sulaimani Polytechnic University Sulaimany Iraq Department of Information Technology College of Science and Technology University of Human Development Sulaimany Iraq Centre for Artificial Intelligence Research and Optimization Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

In this work, a new multiobjective optimization algorithm called multiobjective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of transferring students from high school to college. The proposed technique produces a set of non-dominated solutions. To judge the ability and efficacy of the proposed multiobjective algorithm, it is evaluated against a group of benchmarks and five real-world engineering optimization problems. Additionally, to evaluate the proposed technique quantitatively, several most widely used metrics are applied. Moreover, the results are confirmed statistically. The proposed work is then compared with three multiobjective algorithms, which are MOWCA, NSGA-II, and MODA. Similar to the proposed technique, the other algorithms in the literature were run against the benchmarks, and the real-world engineering problems utilized in the paper. The algorithms are compared with each other employing descriptive, tabular, and graphical demonstrations. The results proved the ability of the proposed work in providing a set of non-dominated solutions, and that the algorithm outperformed the other participated algorithms in most of the cases. © 2022, CC BY-NC-SA.

关键词： Multiobjective optimization

Evolving Deep Neural Network by Customized Moth Flame optimization Algorithm for Underwater Targets Recognition

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arXiv 2023年

作者： Khishe, Mohammad Mohammadi, Mokhtar Rashid, Tarik A. Mahmud, Hoger Mirjalili, Seyedali Department of Marine Electronics and Communication Engineering Imam Khomeini Marine Science University Nowshahr Iran Department of Information Technology College of Engineering and Computer Science Lebanese French University KR Iraq Computer Science and Engineering Department Science and Engineering Science University of Kurdistan Hewler KR Erbil Iraq Department of Computer Science College of Science and Technology University of Human Development KR Sulaymaniyah Iraq Centre for Artificial Intelligence Research and Optimization Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

This chapter proposes using the Moth Flame optimization (MFO) algorithm for fine-tuning a Deep Neural Network to recognize different underwater sonar datasets. Same as other models evolved by metaheuristic algorithms, premature convergence, trapping in local minima, and failure to converge in a reasonable time are three defects MFO confronts in solving problems with high-dimension search space. Spiral flying is the key component of the MFO as it determines how the moths adjust their positions in relation to flames;thereby, the shape of spiral motions can regulate the transition behavior between the exploration and exploitation phases. Therefore, this chapter investigates the efficiency of seven spiral motions with different curvatures and slopes in the performance of the MFO, especially for underwater target classification tasks. To assess the performance of the customized model, in addition to benchmark Sejnowski & Gorman's dataset, two experimental sonar datasets, i.e., the passive sonar and active datasets, are exploited. The results of MFO and its modifications are compared to four novel nature-inspired algorithms, including Heap-Based Optimizer (HBO), Chimp optimization Algorithm (ChOA), Ant Lion optimization (ALO), Stochastic Fractals Search (SFS), as well as the classic Particle Swarm optimization (PSO). The results confirm that the customized MFO shows better performance than the other state-of-the-art models so that the classification rates are increased 1.5979, 0.9985, and 2.0879 for Sejnowski & Gorman, passive, and active datasets, respectively. The results also approve that time complexity is not significantly increased by using different spiral motions. © 2023, CC BY.

关键词： Deep neural networks

Current Studies and Applications of Shuffled Frog Leaping Algorithm: A Review

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arXiv 2022年

作者： Maaroof, Bestan B. Rashid, Tarik A. Abdulla, Jaza M. Hassan, Bryar A. Alsadoon, Abeer Mohammadi, Mokhtar Khishe, Mohammad Mirjalili, Seyedali Information Technology Department College of Commerce University of Sulaimani KRI Iraq Computer Science and Engineering University of Kurdistan Hewler KRI Erbil Iraq Computer Science Department College of Science Komar University of Science and Technology KRI Sulaymaniyah Iraq Information Technology College of Commerce University of Sulaimani KRI Sulaymaniyah Iraq Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Information Technology Department Sydney Australia Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Marine Electronics and Communication Engineering Imam Khomeini Marine Science University Nowshahr Iran Centre for Artificial Intelligence Research and Optimization Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

Shuffled Frog Leaping Algorithm (SFLA) is one of the most widespread algorithms. It was developed by Eusuff and Lansey in 2006. SFLA is a population-based metaheuristic algorithm that combines the benefits of memetics with particle swarm optimization. It has been used in various areas, especially in engineering problems due to its implementation easiness and limited variables. Many improvements have been made to the algorithm to alleviate its drawbacks, whether they were achieved through modifications or hybridizations with other well-known algorithms. This paper reviews the most relevant works on this algorithm. An overview of the SFLA is first conducted, followed by the algorithm's most recent modifications and hybridizations. Next, recent applications of the algorithm are discussed. Then, an operational framework of SLFA and its variants is proposed to analyze their uses on different cohorts of applications. Finally, future improvements to the algorithm are suggested. The main incentive to conduct this survey to provide useful information about the SFLA to researchers interested in working on the algorithm's enhancement or application. © 2022, CC BY.

关键词： Particle swarm optimization (PSO)

A quantum central path algorithm for linear optimization

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arXiv 2023年

作者： Augustino, Brandon Leng, Jiaqi Nannicini, Giacomo Terlaky, Tamás Wu, Xiaodi Sloan School of Management Massachusetts Institute of Technology United States Simons Institute for the Theory of Computing Department of Mathematics University of California Berkeley United States Department of Industrial and Systems Engineering University of Southern California United States Department of Industrial and Systems Engineering Lehigh University United States Quantum Computing and Optimization Lab Lehigh University United States Department of Mathematics University of Maryland College Park United States Department of Computer Science University of Maryland College Park United States Joint Center for Quantum Information and Computer Science University of Maryland United States

We propose a novel quantum algorithm for solving linear optimization problems by quantum-mechanical simulation of the central path. While interior point methods follow the central path with an iterative algorithm that works with successive linearizations of the perturbed KKT conditions, we perform a single simulation working directly with the nonlinear complementarity equations. This approach yields an algorithm for solving linear optimization problems involving m constraints and n variables to Ε-optimality using O (√m + n · RΕ1) queries to an oracle that evaluates a potential function, where R1 is an 1-norm upper bound on the size of the optimal solution. In the standard gate model (i.e., without access to quantum RAM) our algorithm can obtain highly-precise solutions to LO problems using at most O (√m + n · nnz(A)RΕ1) elementary gates, where nnz(A) is the total number of non-zero elements found in the constraint matrix. © 2023, CC BY.

关键词： optimization algorithms

Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach

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arXiv 2023年

作者： Shi, Zhaoyuan Lu, Huabing Xie, Xianzhong Yang, Helin Huang, Chongwen Cai, Jun Ding, Zhiguo The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China The Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS’s amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users’ dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

Improved Coupled Autoencoder based Zero Shot Recognition using Active Learning

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Improved Coupled Autoencoder based Zero Shot Recognition usi...

Conference on information and Communication technology (CICT)

作者： Upendra Pratap Singh Kaustubh Rakesh Rishabh Vipul Kumar Krishna Pratap Singh Machine Learning and Optimization Lab Indian Institute of Information Technology Allahabad

ISBN: (数字)9781728153988

ISBN: (纸本)9781728153995

关键词： Semantics Training Neural networks Prototypes Task analysis Decoding Entropy