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International Conference on informatics, Electronics & Vision (ICIEV)

作者： Abdul Matin Rasel Ahmed Bhuiyan Shafiur Raihan Shafi Amit Kumar Kundu Muhammad Usama Islam Department of Computer Science and Engineering Uttara University Bangladesh Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh Department of Electrical and Electronic Engineering Uttara University Bangladesh School of Computing and Informatics University of Louisiana Lafayette USA

Epilepsy is commonly regarded as a neurological disorder which can be characterized by repetitive unprovoked seizures. Electroencephalogram (EEG) is the neuro-physiological measurement of the brain's electrical activity recorded by electrodes placed in the cerebral cortex. The EEG signals play an essential role in the diagnosis of epilepsy. This paper proposes an approach for classifying the epileptic seizure patterns that carry significant indications regarding chronic neurological disorders. In this regard, a dimensionality reduction scheme, hybrid in nature, utilizing Independent and Principal Component Analysis (ICA and PCA) is developed followed by the extraction of statistical features for epileptic seizure identification. At first, a particular number of sub-frames is extracted from the given EEG signal. The extracted sub-frames are considered as the input of PCA and ICA. After that, the significant components of ICA and PCA are utilized for statistical feature extraction. Lastly, the supervised support vector machine (SVM) classifier is employed for the seizure classification purpose. To evaluate the raised method, the publicly available EPILEPTIC dataset is used. According to the experiments and result analysis, the proposed scheme achieves convincing performance in terms of accuracy when the first components of ICA and PCA algorithms are used for feature extraction.

关键词： Feature extraction Principal component analysis Electroencephalography Support vector machines Brain modeling Time series analysis Dimensionality reduction

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Unsupervised embedding of trajectories captures the latent structure of scientific migration

arXiv

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arXiv 2020年

作者： Murray, Dakota Yoon, Jisung Kojaku, Sadamori Costas, Rodrigo Jung, Woo-Sung Milojević, Staša Ahn, Yong-Yeol Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Network Science Institute Northeastern University BostonMA02115 United States Kellogg School of Management Northwestern University EvanstonIL60208 United States Northwestern Institute on Complex Systems EvanstonIL60208 United States Centre for Science and Technology Studies Leiden University AX Leiden2300 Netherlands DST-NRF Centre of Excellence in Scientometrics and Science Technology and Innovation Policy Stellenbosch University Stellenbosch South Africa Department of Physics Pohang University of Science and Technology Pohang37673 Korea Republic of Department of Industrial and Management Engineering Pohang University of Science and Technology Pohang37673 Korea Republic of

Human migration and mobility drives major societal phenomena including epidemics, economies, innovation, and the diffusion of ideas. Although human mobility and migration have been heavily constrained by geographic distance throughout the history, advances and globalization are making other factors such as language and culture increasingly more important. Advances in neural embedding models, originally designed for natural language, provide an opportunity to tame this complexity and open new avenues for the study of migration. Here, we demonstrate the ability of the model word2vec to encode nuanced relationships between discrete locations from migration trajectories, producing an accurate, dense, continuous, and meaningful vector-space representation. The resulting representation provides a functional distance between locations, as well as a "digital double" that can be distributed, re-used, and itself interrogated to understand the many dimensions of migration. We show that the unique power of word2vec to encode migration patterns stems from its mathematical equivalence with the gravity model of mobility. Focusing on the case of scientific migration, we apply word2vec to a database of three million migration trajectories of scientists derived from the affiliations listed on their publication records. Using techniques that leverage its semantic structure, we demonstrate that embeddings can learn the rich structure that underpins scientific migration, such as cultural, linguistic, and prestige relationships at multiple levels of granularity. Our results provide a theoretical foundation and methodological framework for using neural embeddings to represent and understand migration both within and beyond science. © 2020, CC BY.

关键词： Embeddings

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Partition-aware adaptive switching neural networks for post-processing in HEVC

arXiv

引用

arXiv 2019年

作者： Lin, Weiyao He, Xiaoyi Han, Xintong Liu, Dong See, John Zou, Junni Xiong, Hongkai Wu, Feng Department of Electronic Engineering Shanghai Jiao Tong University China Huya Inc. China Faculty of Computing and Informatics Multimedia University Malaysia Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Information Science and Technology University of Science and Technology of China

This paper addresses neural network based post-processing for the state-of-the-art video coding standard, High Efficiency Video Coding (HEVC). We first propose a partition-aware Convolution Neural Network (CNN) that utilizes the partition information produced by the encoder to assist in the post-processing. In contrast to existing CNN-based approaches, which only take the decoded frame as input, the proposed approach considers the coding unit (CU) size information and combines it with the distorted decoded frame such that the artifacts introduced by HEVC are efficiently reduced. We further introduce an adaptive-switching neural network (ASN) that consists of multiple independent CNNs to adaptively handle the variations in content and distortion within compressed-video frames, providing further reduction in visual artifacts. Additionally, an iterative training procedure is proposed to train these independent CNNs attentively on different local patch-wise classes. Experiments on benchmark sequences demonstrate the effectiveness of our partition-aware and adaptive-switching neural networks. The project page can be found in http://***/lwydemo/***. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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Approximate amplitude encoding in shallow parameterized quantum circuits and its application to financial market indicators

引用

Physical Review Research 2022年第2期4卷 023136-023136页

作者： Kouhei Nakaji Shumpei Uno Yohichi Suzuki Rudy Raymond Tamiya Onodera Tomoki Tanaka Hiroyuki Tezuka Naoki Mitsuda Naoki Yamamoto Graduate School of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama Kanagawa 223- 8522 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama Kanagawa 223-8522 Japan Mizuho Research & Technologies Ltd. 2-3 Kanda-Nishikicho Chiyoda-ku Tokyo 101-8443 Japan IBM Quantum IBM Research-Tokyo 19-21 Nihonbashi Hakozaki-cho Chuo-ku Tokyo 103-8510 Japan Mitsubishi UFJ Financial Group Inc. and MUFG Bank Ltd. 2-7-1 Marunouchi Chiyoda-ku Tokyo 100-8388 Japan Sony Group Corporation 1-7-1 Konan Minato-ku Tokyo 108-0075 Japan Sumitomo Mitsui Trust Bank Ltd. 1-4-1 Marunouchi Chiyoda-ku Tokyo 100-8233 Japan Department of Applied Physics and Physico-Informatics Keio University Hiyoshi 3-14-1 Kohoku-ku Yokohama 223-8522 Japan

Efficient methods for loading given classical data into quantum circuits are essential for various quantum algorithms. In this paper, we propose an algorithm called Approximate Amplitude Encoding that can effectively load all the components of a given real-valued data vector into the amplitude of quantum state, while the previous proposal can load only the absolute values of those components. The key of our algorithm is to variationally train a shallow parameterized quantum circuit, using the results of two types of measurement: the standard computational-basis measurement plus the measurement in the Hadamard-transformed basis, introduced in order to handle the sign of the data components. The variational algorithm changes the circuit parameters so as to minimize the sum of two costs corresponding to those two measurement basis, both of which are given by the efficiently computable maximum mean discrepancy. We also consider the problem of constructing the singular value decomposition entropy via the stock market data set to give a financial market indicator; a quantum algorithm (the variational singular value decomposition algorithm) is known to produce a solution faster than classical, which yet requires the sign-dependent amplitude encoding. We demonstrate, with an in-depth numerical analysis, that our algorithm realizes loading of time series of real stock prices on quantum state with small approximation error, and thereby it enables constructing an indicator of the financial market based on the stock prices.

关键词： Machine learning Quantum algorithms Quantum gates

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Approximate complex amplitude encoding algorithm and its application to data classification problems

arXiv

引用

arXiv 2022年

作者： Mitsuda, Naoki Ichimura, Tatsuhiro Nakaji, Kouhei Suzuki, Yohichi Tanaka, Tomoki Raymond, Rudy Tezuka, Hiroyuki Onodera, Tamiya Yamamoto, Naoki Sumitomo Mitsui Trust Bank Ltd. 1-4-1 Marunouchi Chiyoda-ku Tokyo100-8233 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan Department of Chemistry University of Toronto TorontoONM5G 1Z8 Canada 1-1-1 Umezono Ibaraki Tsukuba305-8568 Japan Mitsubishi UFJ Financial Group Inc. MUFG Bank Ltd. 2-7-1 Marunouchi Chiyoda-ku Tokyo100-8388 Japan IBM Quantum IBM Japan 19-21 Nihonbashi Hakozaki-cho Chuo-ku Tokyo103-8510 Japan Department of Computer Science University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Advanced Research Laboratory Technology Infrastructure Center Sony Group Corporation Technology Platform 1-7-1 Konan Minato-ku Tokyo108-0075 Japan Graduate School of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223- 8522 Japan Department of Applied Physics and Physico-Informatics Keio University Hiyoshi 3-14-1 Kohoku-ku Kanagawa Yokohama223-8522 Japan

Quantum computing has a potential to accelerate data processing efficiency, especially in machine learning, by exploiting special features such as the quantum interference. The major challenge in this application is that, in general, the task of loading a classical data vector into a quantum state requires an exponential number of quantum gates. The approximate amplitude encoding (AAE) method, which uses a variational means to approximately load a given real-valued data vector into the amplitude of a quantum state, was recently proposed as a general approach to this problem mainly for near-term devices. However, AAE cannot load a complex-valued data vector, which narrows its application range. In this work, we extend AAE so that it can handle a complex-valued data vector. The key idea is to employ the fidelity distance as a cost function for optimizing a parametrized quantum circuit, where the classical shadow technique is used to efficiently estimate the fidelity and its gradient. We apply this algorithm to realize the complex-valued-kernel binary classifier called the compact Hadamard classifier, and then we present a numerical experiment showing that it enables classification of the Iris dataset and credit card fraud detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Cost functions

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Developing Univariate Neurodegeneration Biomarkers with low-rank and sparse subspace decomposition

arXiv

引用

arXiv 2020年

作者： Wang, Gang Dong, Qunxi Wu, Jianfeng Su, Yi Chen, Kewei Su, Qingtang Zhang, Xiaofeng Hao, Jinguang Yao, Tao Liu, Li Zhang, Caiming Caselli, Richard J. Reiman, Eric M. Wang, Yalin Ulsan Ship and Ocean College Ludong University Yantai China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Banner Alzheimer’s Institute Banner Good Samaritan Pet Center PhoenixAZ United States School of Information and Electrical Engineering Ludong University Yantai China Shandong Province Key Lab of Digital Media Technology Shandong University of Finance and Economics Jinan China Department of Neurology Mayo Clinic Arizona ScottsdaleAZ United States

Cognitive decline due to Alzheimer’s disease (AD) is closely associated with brain structure alterations captured by structural magnetic resonance imaging (sMRI). It supports the validity to develop sMRI-based univariate neurodegeneration biomarkers (UNB). However, existing UNB work either fails to model large group variances or does not capture AD dementia (ADD) induced changes. We propose a novel low-rank and sparse subspace decomposition method capable of stably quantifying the morphological changes induced by ADD. Specifically, we propose a numerically efficient rank minimization mechanism to extract group common structure and impose regularization constraints to encode the original 3D morphometry connectivity. Further, we generate regions-of-interest (ROI) with group difference study between common subspaces of Aβ+ AD and Aβ− cognitively unimpaired (CU) groups. A univariate morphometry index (UMI) is constructed from these ROIs by summarizing individual morphological characteristics weighted by normalized difference between Aβ+ AD and Aβ− CU groups. We use hippocampal surface radial distance feature to compute the UMIs and validate our work in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. With hippocampal UMIs, the estimated minimum sample sizes needed to detect a 25% reduction in the mean annual change with 80% power and two-tailed P = 0:05 are 116, 279 and 387 for the longitudinal Aβ+ AD, Aβ+ mild cognitive impairment (MCI) and Aβ+ CU groups, respectively. Additionally, for MCI patients, UMIs well correlate with hazard ratio of conversion to AD (4:3, 95% CI=2:3 – 8:2) within 18 months. Our experimental results outperform traditional hippocampal volume measures and suggest the application of UMI as a potential UNB. Copyright © 2020, The Authors. All rights reserved.

关键词： Biomarkers

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Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings

arXiv

引用

arXiv 2022年

作者： Bano, Sophia Casella, Alessandro Vasconcelos, Francisco Qayyum, Abdul Benzinou, Abdesslam Mazher, Moona Meriaudeau, Fabrice Lena, Chiara Cintorrino, Ilaria Anita De Paolis, Gaia Romana Biagioli, Jessica Grechishnikova, Daria Jiao, Jing Bai, Bizhe Qiao, Yanyan Bhattarai, Binod Gaire, Rebati Raman Subedi, Ronast Vazquez, Eduard Plotka, Szymon Lisowska, Aneta Sitek, Arkadiusz Attilakos, George Wimalasundera, Ruwan David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women's Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto Giannina Gaslini Italy ENIB UMR CNRS 6285 LabSTICC 29238 France Department of Computer Engineering and Mathematics University Rovira i Virgili Spain ImViA Laboratory University of Bourgogne Franche-Comté France Physics Department Lomonosov Moscow State University Russia Fudan University China Medical Computer Vision and Robotics Group Department of Mathematical and Computational Sciences University of Toronto Canada Co. Ltd China NepAL Applied Mathematics and Informatics Institute for Research Nepal Redev Technology United Kingdom Sano Center for Computational Medicine Poland Quantitative Healthcare Analysis Group Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper,

关键词： Pixels

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AIROGS: ARTIFICIAL INTELLIGENCE FOR ROBUST GLAUCOMA SCREENING CHALLENGE

arXiv

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arXiv 2023年

作者： De Vente, Coen Vermeer, Koenraad A. Jaccard, Nicolas Wang, He Sun, Hongyi Khader, Firas Truhn, Daniel Aimyshev, Temirgali Zhanibekuly, Yerkebulan Le, Tien-Dung Galdran, Adrian Ballester, Miguel Ángel González Carneiro, Gustavo Devika, R.G. Hrishikesh, P.S. Puthussery, Densen Liu, Hong Yang, Zekang Kondo, Satoshi Kasai, Satoshi Wang, Edward Durvasula, Ashritha Heras, Jónathan Zapata, Miguel Ángel Araújo, Teresa Aresta, Guilherme Bogunović, Hrvoje Arikan, Mustafa Lee, Yeong Chan Cho, Hyun Bin Choi, Yoon Ho Qayyum, Abdul Razzak, Imran Van Ginneken, Bram Lemij, Hans G. Sánchez, Clara I. Group Informatics Institute Universiteit Van Amsterdam Noord-Holland Amsterdam Netherlands The Department of Biomedical Engineering and Physics Amsterdam Umc Locatie Amc Noord-Holland Amsterdam Netherlands Department of Radiology and Nuclear Medicine Radboudumc Gelderland Nijmegen Netherlands The Rotterdam Ophthalmic Institute Rotterdam Eye Hospital Rotterdam Netherlands Project Orbis International Inc. New York United States The Peking Union Medical College Hospital Beijing China The Xuanwu Hospital Capital Medical University Beijing100053 China The Tsinghua University Beijing China The Department of Diagnostic and Interventional Radiology University Hospital Aachen Aachen Germany Cmc Technologies Llp Nur-Sultan Kazakhstan Kbc Belgium Universitat Pompeu Fabra Barcelona Spain The Australian Institute for Machine Learning Aiml University of Adelaide Australia Icrea Barcelona Spain The Centre for Vision Speech and Signal Processing University of Surrey United Kingdom The College of Engineering Trivandrum India Founding Minds Software India The Institute of Computing Technology Chinese Academy of Sciences China The Muroran Institute of Technology Japan The Niigata University of Health and Welfare Japan The Schulich School of Medicine and Dentistry University of Western Ontario London Canada The Department of Mathematics and Computer Science University of la Rioja Spain Hospital Vall Hebron Passeig Roser 126 Sant Cugat del Vallès Barcelona08195 Spain UPRetina Barcelona Spain The Christian Doppler Laboratory for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria The Institute of Ophthalmology University College London London United Kingdom The Research Institute for Future Medicine Samsung Medical Center Seoul Korea Republic of Sungkyunkwan University Samsung Medical Center Seoul Korea Republic of The Department of Artificial Intelligence and Informatics Mayo Clinic Jacks

The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in realworld scenarios due to the presence of out-of-distribution and low-quality images. To address this issue, we propose the Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge. This challenge includes a large dataset of around 113,000 images from about 60,000 patients and 500 different screening centers, and encourages the development of algorithms that are robust to ungradable and unexpected input data. We evaluated solutions from 14 teams in this paper, and found that the best teams performed similarly to a set of 20 expert ophthalmologists and optometrists. The highest-scoring team achieved an area under the receiver operating characteristic curve of 0.99 (95% CI: 0.98-0.99) for detecting ungradable images on-the-fly. Additionally, many of the algorithms showed robust performance when tested on three other publicly available datasets. These results demonstrate the feasibility of robust AI-enabled glaucoma screening. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

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Efficient AI-Driven Design of Microwave Antennas Using PSADEA

Efficient AI-Driven Design of Microwave Antennas Using PSADE...

引用

IEEE Middle East and North Africa Communications Conference (MENACOMM)

作者： Mobayode O. Akinsolu Isah M. Danjuma Keyur K. Mistry Bo Liu Raed A. Abd-Alhameed Pavlos I. Lazaridis Zaharias D. Zaharis Peter Excell Faculty of Arts Science and Technology Wrexham Glyndŵr University UK Faculty of Engineering and Informatics University of Bradford Bradford UK School of Computing and Engineering University of Huddersfield UK Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

Nowadays, artificial intelligence plays a very significant role in the optimal design of microwave devices such as antennas. In particular, antenna design automation via surrogate model-based optimization (SMBO) methods is attracting a lot of interest due to the efficiency improvement in terms of computational cost. The parallel surrogate model-assisted hybrid differential evolution for antenna optimisation (PSADEA) method is a state-of-the-art SMBO method. In this paper, PSADEA is used to synthesize a compact slotted monopole antenna for ultra wide band body-centric applications. The performance of PSADEA is compared with 2019 Computer Simulation technology - Microwave Studio (CST-MWS) optimizers: trust region framework (TRF) and particle swarm optimisation (PSO). Results from the comparisons show that PSADEA obtains very satisfactory design solutions for the monopole antenna in all runs using an affordable optimization time in each, whereas the alternative optimizers fail to obtain a satisfactory design solution in all runs. A close agreement between the simulated and measured results for the fabricated prototype of a typical PSADEA synthesized design for the monopole antenna validates the design solution quality of PSADEA.

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The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions

引用

The Journal of Data Education 2025年第1期65卷

作者： Keng-Boon Ooi Garry Wei-Han Tan Mostafa Al-Emran Mohammed A. Al-Sharafi Alexandru Capatina Amrita Chakraborty Yogesh K. Dwivedi Tzu-Ling Huang Arpan Kumar Kar Voon-Hsien Lee Xiu-Ming Loh Adrian Micu Patrick Mikalef Emmanuel Mogaji Neeraj Pandey Ramakrishnan Raman Nripendra P. Rana Prianka Sarker Anshuman Sharma Ching-I Teng Samuel Fosso Wamba Lai-Wan Wong a UCSI Graduate Business School UCSI University Kuala Lumpur Malaysiab FORE School of Management New Delhi Indiac Faculty of Business Design and Arts Swinburne University of Technology Sarawak Campus Kuching Malaysia a UCSI Graduate Business School UCSI University Kuala Lumpur Malaysiac Faculty of Business Design and Arts Swinburne University of Technology Sarawak Campus Kuching Malaysiad College of Business Administration Adamson University Manila Philippines e Faculty of Engineering & IT The British University in Dubai Dubai UAEf Department of Computer Techniques Engineering Dijlah University College Baghdad Iraq g Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Selangor Malaysia h Department of Business Administration “Dunarea de Jos” University of Galati Galati Romania i Tech Talk New Delhi India j Digital Futures for Sustainable Business & Society Research Group School of Management Swansea University Bay Campus Swansea UKk Department of Management Symbiosis International (Deemed University) Pune India l Department of Information Management National Central University Taoyuan Taiwan m Department of Management Studies Indian Institute of Technology Delhi New Delhi India n Faculty of Business and Finance Universiti Tunku Abdul Rahman Kampar Malaysia o Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norwayp Department of Technology Management SINTEF Digital Trondheim Norway q Keele Business School Keele University Keele UK r Indian Institute of Management Mumbai Mumbai India k Department of Management Symbiosis International (Deemed University) Pune India s College of Business and Economics Qatar University Doha Qatar t Business School Manchester Metropolitan University Manchester UK u Department of Marketing College of Business Administration Ajman university Ajman UAE v Graduate Institute of Management Chang Gung University Taoyuan Taiw

ABSTRACTIn a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).

关键词： Generative artificial intelligence machine learning large language model ChatGPT Bard

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