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AI, science, Engineering, and Technology (AIxSET), Conference on

作者： Gede Sughiarta Atiek Kurnianingsih Srikar Reddy Gopaladinne Sujay Shrivastava Hemanth Kumar Reddy Gorla Marc Böhlen Wisnu foundation Denpasar Indonesia Department of Computer Science and Engineering University at Buffalo Buffalo USA Department of Art Institute for Artificial Intelligence and Data Science University at Buffalo Buffalo USA

ISBN: (数字)9798350390995

ISBN: (纸本)9798350391008

This paper describes spatially aware Artificial Intelligence, GeoAI, tailored for small organizations such as NGOs in resource constrained contexts where access to large datasets, expensive compute infrastructure and AI expertise may be restricted. We furthermore consider future scenarios in which resource-intensive, large geospatial models may homogenize the representation of complex landscapes, and suggest strategies to prepare for this condition.

关键词： Computational modeling Organizations Geospatial analysis Artificial intelligence

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SKIN_ML: An Efficient Approach for Skin Cancer Detection Using Soft Computing Methods 3

SKIN_ML: An Efficient Approach for Skin Cancer Detection Usi...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Farag, Mohamed S Gomaa, Ibrahim A Abd-Elghany Zeid, Magdy Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia

ISBN: (纸本)9798350306231

In today's environment, cancer is a fatal disease. Skin cancer has become a fairly common malignancy due to the spread of several forms of cancer. Skin cancer is divided into two types: melanoma and non-melanoma. Melanoma is one of the most fatal tumors on the planet, and it can spread to other parts of the body if not diagnosed early enough. Our proposed system uses Five alternative methods to predict a skin lesion's borders, texture, and color: a neural network and four standard machine learning classifiers. To enhance their performance, the outputs of these systems are merged using majority voting. Experiments have demonstrated that combining the five strategies yields the maximum level of accuracy. Pre-processing, Segmentation, Feature Extraction, and Classification are four critical phases in skin cancer identification. Skin lesion images were collected for this research from the International Skin Imaging Collaboration (ISIC), which contains over 3297 photos. The accuracy of the NN classifier is 91.9%, compared to 87.2% for the KNN classifier, 81.5% for the Naive Bayes classifier, 72.5% for the SVM classifier, and 68.3% for the DT classifier. © 2023 IEEE.

关键词： Support vector machines

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Wheat Leaf Disease Identification Based on a Fusion Model Integrating Attention Mechanisms and Channel-Spatial Reconstruction Convolution

Wheat Leaf Disease Identification Based on a Fusion Model In...

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Big data & Artificial Intelligence & Software Engineering (ICBASE), International Conference on

作者： Haifan Xie Rongxin Zou Zicong Yang Aitong Jin Junxi Wang Yu Li Department of Computer Zhuhai College of Science and Technology Zhuhai China Department of Big Data Zhuhai College of Science and Technology Zhuhai China Zhuhai College of Science and Technology Institute of Intelligent Manufacturing and Aviation ZhuHai China Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

With the continuous growth in food demand and the escalating importance of crop health, the challenges posed by the high costs and low efficiency of traditional wheat leaf disease detection methods have become increasingly acute. In light of the intricacies and low discernibility of the morphological and color characteristics of wheat leaf diseases and pests in natural environments, this study introduces a novel and efficient recognition and classification model, SCC2f-CSPDarknet53-CBAM, offering a precise and rapid solution for identifying diseases and pests, thereby supporting early prevention and scientific management. By incorporating the SCC-C2f module into the base of C2f-CSPDarknet53, this model enhances the efficiency of network feature fusion and the capability to extract critical features. Furthermore, the integration of the ResBlock-CBAM module at the terminal end bolsters the generalization and recognition abilities for targets. The study employs the Wheat Leaf Disease dataset, which is constructed through a blend of online and offline methods, with meticulous data processing, for training purposes. Experimental results reveal that the SCC2f-CSPDarknet53-CBAM model achieves an average recognition accuracy of $99.54 \%$ in the task of wheat leaf disease detection and classification, surpassing mainstream models in recent years, showcasing remarkable accuracy and adaptability.

关键词： Training Measurement Adaptation models Accuracy Attention mechanisms Target recognition Prevention and mitigation Feature extraction Diseases Software engineering

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A data-Efficient Deep Learning Framework for Segmentation and Classification of Histopathology Images 17th

A Data-Efficient Deep Learning Framework for Segmentation a...

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17th European Conference on computer Vision, ECCV 2022

作者： Singh, Pranav Cirrone, Jacopo Department of Computer Science Tandon School of Engineering New York University New York New York11202 United States Center for Data Science New York University and Colton Center for Autoimmunity NYU Grossman School of Medicine New York New York10011 United States

ISBN: (纸本)9783031250651

The current study of cell architecture of inflammation in histopathology images commonly performed for diagnosis and research purposes excludes a lot of information available on the biopsy slide. In autoimmune diseases, major outstanding research questions remain regarding which cell types participate in inflammation at the tissue level, and how they interact with each other. While these questions can be partially answered using traditional methods, artificial intelligence approaches for segmentation and classification provide a much more efficient method to understand the architecture of inflammation in autoimmune disease, holding a great promise for novel insights. In this paper, we empirically develop deep learning approaches that uses dermatomyositis biopsies of human tissue to detect and identify inflammatory cells. Our approach improves classification performance by 26% and segmentation performance by 5%. We also propose a novel post-processing autoencoder architecture that improves segmentation performance by an additional 3%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computer vision

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Flow-Guided Deformable Alignment Network with Self-Supervision for Video Inpainting

Flow-Guided Deformable Alignment Network with Self-Supervisi...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhiliang Wu Kang Zhang Changchang Sun Hanyu Xuan Yan Yan School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Illinois Institute of Technology USA School of Big Data and Statistics Anhui University China

Video inpainting aims to utilize plausible contents to fill missing regions in the video. State-of-the-art video inpainting methods typically generate the missing contents of the target frame (current frame) by aggregating the temporal information of reference frames (neighboring frames) aligned using deformable convolution. However, these deformable convolution alignment networks often suffer from offset overflow during training, resulting in unsatisfactory alignment, thereby obtaining compromised inpainting performance. In this paper, we propose a self-supervised Flow-Guided Deformable Alignment (FGDA) network for aligning reference frames at the feature level. FGDA computes the residual of the optical flow as the offsets. This design can effectively reduce the burden of offsets learning, thereby avoiding offset overflow. Furthermore, a gradient-weighted reconstruction loss for supervised completed frame reconstruction is designed, which can use the gradients in all directions of the video frame to emphasize the difficultly reconstructed texture regions, so that detail textures get more attention during training. Experiments show that FGDA-based video inpainting model trained with gradient-weighted reconstruction loss outperforms the state-of-the-art by a significant margin in terms of PSNR and SSIM with relative improvements of 6.2% and 2.1%, respectively.

关键词： Training Optical losses Convolution Acoustics Speech processing Optical flow

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ML_Alzheimer: Alzheimer Disease Prediction Using Machine Learning 3

ML_Alzheimer: Alzheimer Disease Prediction Using Machine Lea...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

ISBN: (纸本)9798350306231

Alzheimer's disease is a fatal brain disorder that impacts predominantly the elderly. The early identification of Alzheimer's illness requires the use of efficient automated methods. For the categorization of Alzheimer's disease, researchers have proposed several distinct methods. To create more effective learning strategies, however, a deeper comprehension of Alzheimer's research is necessary. The condition is diagnosed but not until a later stage. As a consequence, the disease's progression or its symptoms' onset can be slowed if detected earlier. This article uses two datasets and machine learning techniques to predict Alzheimer's disease based on psychological characteristics such as age, gender, number of visits, and Mini-Mental State Examination (MMSE). We ultimately focused on the masculine gender and characteristics. The following results (accuracy) were discovered: SVM: 84%, KNN: 79%, and Random forest: 86%. Neural network: 86%;Naive Bayes: 83%. © 2023 IEEE.

关键词： Support vector machines

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Deep Attention Driven Reinforcement Learning (DAD-RL) for Autonomous Decision-Making in Dynamic Environment

arXiv

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arXiv 2024年

作者： Chowdhury, Jayabrata Shivaraman, Venkataramanan Dangi, Sumit Sundaram, Suresh Sujit, P.B. Robert Bosch Centre for Cyber-Physical Systems Indian Institute of Science Bangalore India The Department of Aerospace Engineering Indian Institute of Science Bangalore India The Department of Data Science & Engineering and Electrical Engineering & Computer Science Indian Institute of Science Education and Research Bhopal India

Autonomous Vehicle (AV) decision-making in urban environments is inherently challenging due to the dynamic interactions with surrounding vehicles. For safe planning, AV/ego must understand the weightage of various spatiotemporal interactions in a scene. Contemporary works use colossal transformer architectures to encode interactions mainly for trajectory prediction, resulting in increased computational complexity. To address this issue without compromising spatiotemporal understanding and performance, we propose the simple Deep Attention Driven Reinforcement Learning (DAD-RL) framework, which dynamically assigns and incorporates the significance of surrounding vehicles into the ego’s RL-driven decision-making process. We introduce an AV-centric spatiotemporal attention encoding (STAE) mechanism for learning the dynamic interactions with different surrounding vehicles. To understand map and route context, we employ a context encoder to extract features from context maps. The spatiotemporal representations combined with contextual encoding provide a comprehensive state representation. The resulting model is trained using the Soft-Actor Critic (SAC) algorithm. We evaluate the proposed framework on the SMARTS urban benchmarking scenarios without traffic signals to demonstrate that DAD-RL outperforms recent state-of-the-art methods. Furthermore, an ablation study underscores the importance of the context-encoder and spatio-temporal attention encoder in achieving superior performance. © 2024, CC BY-SA.

关键词： Deep reinforcement learning

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Super-Resolution Displacement Spectroscopic Sensing over a Surface“Rainbow”

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Engineering 2022年第10期17卷 75-81页

作者： Lyu Zhou Nan Zhang Chang Chieh Hsu Matthew Singer Xie Zeng Yizheng Li Haomin Song Josep Jornet Yun Wu Qiaoqiang Gan Department of Electrical Engineering The State University of New York at BuffaloBuffaloNY 14260USA Department of Biomedical Engineering The State University of New York at BuffaloBuffaloNY 14260USA Department of Electrical and Computer Engineering Northeastern UniversityBostonMA 02115USA Material Science Engineering Program King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia

Subwavelength manipulation of light waves with high precision can enable new and exciting applications in spectroscopy,sensing,and medical *** these applications,miniaturized spectrometers are desirable to enable the on-chip analysis of spectral *** particular,for imaging-based spectroscopic sensing mechanisms,the key challenge is to determine the spatial-shift information accurately(i.e.,the spatial displacement introduced by wavelength shift or biological or chemical surface binding),which is similar to the challenge presented by super-resolution ***,we report a unique"rainbow"trapping metasurface for on-chip spectrometers and *** with super-resolution image processing,the low-setting 4×optical microscope system resolves a displacement of the resonant position within 35 nm on the plasmonic rainbow trapping metasurface with a tiny area as small as0.002 *** unique feature of the spatial manipulation of efficiently coupled rainbow plasmonic resonances reveals a new platform for miniaturized on-chip spectroscopic analysis with a spectral resolution of 0.032 nm in wavelength *** this low-setting 4×microscope imaging system,we demonstrate a biosensing resolution of 1.92×109exosomes per milliliter for A549-derived exosomes and distinguish between patient samples and healthy controls using exosomal epidermal growth factor receptor(EGFR)expression values,thereby demonstrating a new on-chip sensing system for personalized accurate bio/chemical sensing applications.

关键词： Rainbow trapping Metasurface Surface plasmon polaritons Super-resolution displacement On-chip biosensing

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Instagram Augmented Reality Marketing in Boosting Brand Awareness and e-WOM in the Indonesian Bottled Tea Industry

Instagram Augmented Reality Marketing in Boosting Brand Awar...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Banu Rinaldi Maria Grace Herlina Karto Iskandar Department of BINUS Business School Undergraduate Program Business Creation Program Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350366648

ISBN: (纸本)9798350366655

This study explores the efficacy of Instagram's augmented reality (AR) features in enhancing brand awareness and electronic word-of-mouth (e-WOM) within Indonesia's bottled tea industry. The research uses a cross-sectional survey and partial least square structural equation modeling (PLS-SEM) to focus on how AR can transform consumer engagement and interaction on social media platforms. Findings from a sample of 260 bottled tea consumers in Greater Jakarta indicate that AR significantly enhances brand awareness, substantially influencing e-WOM. The integration of AR into Instagram marketing not only captivates and engages users but also fosters stronger consumer-brand relationships and promotes brand loyalty through interactive and immersive digital experiences. The study’s implications underscore AR's role in revolutionising marketing strategies by providing immersive experiences that are shown to increase brand visibility and influence purchasing decisions. Limitations and future research directions are discussed, highlighting the potential for broader applications of AR in different market segments and its impact on various consumer demographics.

关键词： Industries Social networking (online) Urban areas Transforms Boosting Mathematical models Web sites Multimedia communication Augmented reality Consumer electronics

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An Augmented Reality Game's User Experience and Its Effect on Immersion Leading to Intention

An Augmented Reality Game's User Experience and Its Effect o...

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International Conference on ICT For Smart Society (ICISS)

作者： Tanty Oktavia Kawang Utomo Ford Lumban Gaol Takaaki Hosoda Information Systems Management Department BINUS Graduate Program - Master of Information System Management Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Decision Science Department Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Since the emergence of Pokémon Go in 2016, the world has been introduced to Augmented Reality (AR) games. Many IT companies have begun developing augmented reality (AR) games due to the great commercial potential in this industry. Certain products may have managed to remain viable in the market, while others were forced to close due to one or two factors. Since the emergence of Pokémon Go, no other game has managed to surpass its unprecedented success. The company responsible for developing Pokémon Go has decided to shut down one of their games due to a lack of consumer interest. The technology of augmented reality (AR) is consistently associated with the concept of immersion. Immersion is a key aspect of the game that allows users to deeply engage and feel fully involved in the experience. This study will employ a quantitative methodology, utilising a questionnaire that will be distributed to and completed by those who have engaged in augmented reality (AR) games. The data will be analysed using Smart PLS to examine the impact of user experience on immersion, which in turn influences intention. After gathering over 200 participants by spreading google form questionnaires and doing data analysis, the findings indicate that only a few aspects of user experience, namely Brand Experience and User Need Experience, have an impact on reasons. Niantic, the developer of Pokémon Go, was unable to replicate their success with a similar augmented reality game called Harry Potter: Wizards Unite. The initial release occurred on June 21, 2019, and the shutdown took place on December 21, 2021. In the beginning, they shown a profound enthusiasm for the game but were unable to sustain it, much as Pokémon Go. According to Niantic CEO John Hanke, the primary reason for the game's shutdown was its immersion. Immersion has a notable impact on User Confirmation, which in turn has a notable impact on Satisfaction, and Satisfaction has a notable impact on Intention. Moreover, there have be

关键词： Surveys Industries data analysis Games Companies User experience Software Hardware Internet Augmented reality

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