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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Kaviyaraj, R. Sathya, P. Nandhini, P. Vinoth Kumar, P. Chelliah, Sureshkumar Ezhilmathi, S. Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Sri Shanmuga Collage of Engineering and Technology Department of Computer Science and Engineering Sankari India St. Joseph's Institute of Technology Omr Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350361186

Sign language, the vibrant tapestry of hand gestures and facial expressions, is the lifeblood of Deaf and hardof-hearing communities. For millions of signers, American Sign Language (ASL) runs deeper than communication, fundamental to identity, expression, and belonging. And yet an unshakeable communication gap leaves users of ASL frequently marooned away from the hearing world, kept from education, healthcare, or employment, or from basic, everyday transactions. By posing this new unsolved challenge to the power and promise of Artificial Intelligence (AI), this work leads the way towards closing that chasm by real-time recognition and translation of full ASL. Our approach employs a novel variant of Random Forest and utilizes cutting-edge video processing techniques to identify and understand the nuanced, often exquisitely delicate detail of ASL signing, at unprecedented levels of accuracy, and at speed. Another layer of innovation that characterizes our work is our integration of augmented reality (AR). By embedding AR along with our translator of artificial intelligence tech, we intend to completely change the way American Sign Language (ASL) is conveyed by directly engraining our already robust Random Forest model and advanced video processing techniques to project the ASL translation directly into your visual field in real time. The goal to demystify this complex and vivid language and, in doing so, to remove the communication barriers that persist between the Deaf community and the rest of the world, thus, fostering inclusion. © 2024 IEEE.

关键词： Augmented reality

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Enhanced metric learning via dempster-shafer evidence theory 25th

Enhanced metric learning via dempster-shafer evidence theory

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25th International Conference on Neural Information Processing, ICONIP 2018

作者： Li, Ying Zhang, Yabo Peng, Yaxin School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China Department of Mathematics School of Science Shanghai University Shanghai200444 China

ISBN: (纸本)9783030041816

Metric learning is a hot topic in machine learning. A proper learned metric can measure the similarity between samples better and hence significantly improves the performance of machine learning algorithm. In this paper, we propose a novel enhanced distance metric learning method via Dempster-Shafer (D-S) evidence theory. We consider each instance as an independent source of evidence and combine these pieces of evidence by using Dempster’s rule. Firstly, with reference to the D-S theory, we construct the balanced weight function corresponding to each instance in the metric. Secondly, the novel competitive-cost function is given, which can improve classifier accuracy by narrowing the inner-class distance and increasing the inter-class distance. Finally, we implement a series of experiments on classification by using UCI and face recognition data sets. Experimental results validate that the proposed method can significantly improve the performance of the classifier and the robustness of the algorithm. © Springer Nature Switzerland AG 2018.

关键词： Classification (of information)

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Empowering NGOs with Remote Sensing and CNN-LSTM Models for Social and Environmental Transformation

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 554-568页

作者： Ramachandran, Jayakarthik Gupta, Ashwani Kumar Syamala, Maganti Banerjee, Sudipta Baburao, D. Ranadive, Amol Deivasigamani, Shanmugavel Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai India School of Business and Commerce Chandigarh University Unnao India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur India Department of Computer Science and Engineering Symbiosis Institute of Technology Pune India Campus Symbiosis International (Deemed University) (SIU) Maharashtra Pune India Department of Computer Science and Engineering Vidya Jyothi Institute of Technology (VJIT) Telangana Hyderabad India BIMHRD Sri Balaji University Pune India Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Anna University Tamilnadu Chennai India

Non-governmental organizations (NGOs) play a pivotal role in addressing some of the most pressing environmental and social challenges across the globe. With the rise of advanced technologies, NGOs have an unprecedented opportunity to enhance their impact through the adoption of innovative tools. Among these, remote sensing technology has emerged as a cornerstone in environmental monitoring, offering real-time data on land, water, and atmospheric conditions. The integration of remote sensing with CNN-LSTM models offers a transformative approach for NGOs to tackle environmental and social challenges. This study explores the efficacy of these technologies in monitoring deforestation and habitat loss. By leveraging high-resolution satellite imagery and ground truth data, our proposed CNN-LSTM model successfully identifies and tracks changes in land cover. Throughout various tests, the model demonstrated a high accuracy of 0.97, showcasing its potential in providing reliable, real-time data for environmental conservation efforts. The findings suggest that combining CNNs and LSTMs enhances the ability to interpret complex spatiotemporal data, thus supporting NGOs in their decision-making processes. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： CNN-LSTM models Deforestation Environmental monitoring Habitat loss Non-governmental organizations (NGOs) Remote sensing Satellite imagery Spatial pattern recognition Temporal dynamics

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Crop for Est - Crop Forecasting and Estimation. Crop Yield Estimation and Profitability Analysis for Precision Agriculture

Crop for Est - Crop Forecasting and Estimation. Crop Yield E...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Shanmugasundaram, C. Umamaheswari, C. Vijayalakshmi, A. Varghese, Prabha Elizabeth Manakula Vinayagar Institute of Technology Department of Electrical and Electronics Engineering Puducherry India St. Thomas College of Arts and Science Department of Computer Science with Data Science Tamil Nadu India Sri Manakula Vinayagar Engineering College Department of Biomedical Engineering Puducherry India

ISBN: (纸本)9798331510022

The agriculture industry significantly contributes to the nation's economy by employing a large segment of the population and enhancing the GDP. It is a primary source of livelihood for many people, particularly in rural areas. India's agricultural sector is known for its diversity, producing a wide range of crops including rice, wheat pulses, sugarcane, cotton, fruits, and vegetables. This industry faces a significant challenge in predicting crop yield production and crop demand accurately. Efforts have been made to address these challenges through various government initiatives such as subsidies and continued investment in agricultural research and development. The traditional methods of predicting crop yield and market demand is cumbersome and time consuming and is susceptible to errors, resulting in inefficient resource utilization and revenue losses. There is a need for predicting the most efficient crop based on various influential environmental markers and forecasting the profitability. This project aims to develop an intelligent platform that uses machine learning algorithms to estimate the crop for cultivation in each area based on environmental factors. The project also aims to forecast market demand and pricing trends for the identified crop with the aim of increasing the profitability for the farmers and other stakeholders in the agriculture industry. © 2024 IEEE.

关键词： Agricultural economics

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PROTEIN DISCOVERY WITH DISCRETE WALK-JUMP SAMPLING 12

PROTEIN DISCOVERY WITH DISCRETE WALK-JUMP SAMPLING

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12th International Conference on Learning Representations, ICLR 2024

作者： Frey, Nathan C. Berenberg, Daniel Zadorozhny, Karina Kleinhenz, Joseph Lafrance-Vanasse, Julien Hötzel, Isidro Wu, Yan Ra, Stephen Bonneau, Richard Cho, Kyunghyun Loukas, Andreas Gligorijević, Vladimir Saremi, Saeed Genentech United States Antibody Engineering Genentech United States Department of Computer Science New York University United States Center for Data Science New York University United States

We resolve difficulties in training and sampling from a discrete generative model by learning a smoothed energy function, sampling from the smoothed data manifold with Langevin Markov chain Monte Carlo (MCMC), and projecting back to the true data manifold with one-step denoising. Our Discrete Walk-Jump Sampling formalism combines the contrastive divergence training of an energy-based model and improved sample quality of a score-based model, while simplifying training and sampling by requiring only a single noise level. We evaluate the robustness of our approach on generative modeling of antibody proteins and introduce the distributional conformity score to benchmark protein generative models. By optimizing and sampling from our models for the proposed distributional conformity score, 97-100% of generated samples are successfully expressed and purified and 70% of functional designs show equal or improved binding affinity compared to known functional antibodies on the first attempt in a single round of laboratory experiments. We also report the first demonstration of long-run fast-mixing MCMC chains where diverse antibody protein classes are visited in a single MCMC chain. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Antibodies

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The Utilization of Mobile Communication on Marketing: A Systematic Review 8

The Utilization of Mobile Communication on Marketing: A Syst...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Permana, Adora Vania Purnomo, Agung Sarjono, Haryadi Maulana, Fairuz Iqbal Setyani, Etsa Astridya Entrepreneurship Department BINUS Business School Undergraduated Program Bina Nusantara Univerisity Jakarta11480 Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The advancement of mobile communication technology with entrepreneurial minds is advancing, and its use is increasingly playing an important role in business, marketing, and life. However, there are not many studies that focus on the role of mobile communication in the business world, especially those that focus on marketing. The purpose of writing this paper is to determine the status of research and the relationship between mobile communication in the field of marketing. The method used in this research is systematic literature review using the PRISMA protocol, involving 29 articles. Through quantitative analysis, we found that many years of article publication occurred mostly in 2020 due to the development of 5G technology. Australia is the largest article producing country in the world because many researchers are trying to examine public acceptance of mobile communication technology. The use of mobile communication is also mostly used in the marketing and segmentation processes in multilevel firm level marketing with multipoint mobile communication. On the other hand, articles with a marketing perspective that discuss price issues have never been discussed. © 2023 The Authors. Published by Elsevier B.V.

关键词： Entrepreneurship marketing systematic review

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Generative Adversarial Networks for Spatio-Spectral Compression of Hyperspectral Images 14

Generative Adversarial Networks for Spatio-Spectral Compress...

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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： Fuchs, Martin Hermann Paul Byju, Akshara Preethy Walda, Alisa Rasti, Behnood Demir, Begum Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Technische Universität Berlin Faculty of Electrical Engineering and Computer Science Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

ISBN: (纸本)9798331513139

Deep learning-based hyperspectral image (HSI) compression has recently attracted great attention in remote sensing due to the growth of hyperspectral data archives. Most of the existing models achieve either spectral or spatial compression and do not jointly consider the spatio-spectral redundancies present in HSIs. To address this problem, in this paper, we propose High Fidelity Compression (HiFiC)-based models for spatio-spectral compression of HSIs. In detail, we introduce two new models: i) HiFiC using Squeeze and Excitation (SE) blocks (denoted as HiFiCsE);and ii) HiFiC with 3D convolutions (denoted as HiFiC3D) in the framework of compression of HSIs. We analyze the effectiveness of HiFiCsE and HiFiC3D in compressing the spatio-spectral redundancies with channel attention and inter-dependency analysis. Experimental results show the efficacy of the proposed models in performing spatio-spectral compression, while reconstructing images at reduced bitrates with higher reconstruction quality. The code of the proposed models is publicly available at https://***/rsim/HSI-SSC. © 2024 IEEE.

关键词： Generative adversarial networks

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Emergent communication in a multi-modal, multi-step referential game 6

Emergent communication in a multi-modal, multi-step referent...

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6th International Conference on Learning Representations, ICLR 2018

作者： Evtimova, Katrina Drozdov, Andrew Kiela, Douwe Cho, Kyunghyun Center for Data Science New York University United States Department of Computer Science New York University United States Facebook AI Research CIFAR Azrieli Global Scholar Canada

Inspired by previous work on emergent communication in referential games, we propose a novel multi-modal, multi-step referential game, where the sender and receiver have access to distinct modalities of an object, and their information exchange is bidirectional and of arbitrary duration. The multi-modal multi-step setting allows agents to develop an internal communication significantly closer to natural language, in that they share a single set of messages, and that the length of the conversation may vary according to the difficulty of the task. We examine these properties empirically using a dataset consisting of images and textual descriptions of mammals, where the agents are tasked with identifying the correct object. Our experiments indicate that a robust and efficient communication protocol emerges, where gradual information exchange informs better predictions and higher communication bandwidth improves generalization. © Learning Representations, ICLR 2018 - Conference Track *** right reserved.

关键词： Mammals

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Optimizing Random Forest Classification Using Chi-Square and SMOTE-ENN on Student Drop-Out data 8

Optimizing Random Forest Classification Using Chi-Square and...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Andri Yunis, Roni Tanti Universitas Mikroskil Undergraduate Program of Computer Science Department Medan Indonesia Universitas Mikroskil Undergraduate Program of Information Systems Department Medan Indonesia Universitas Mikroskil Undergraduate Program of Information Technology Department Medan Indonesia

ISBN: (纸本)9798350342604

Dropout is a particular concern for countries striving to increase human capital. Various attempts have been made by universities to minimize the number of dropouts. Machine learning has also developed various predictive models to determine the likelihood of students dropping out. However, there is a challenge in dropout data, specifically the problem of class imbalance, where the number of students who drop out (minority class) is significantly less than those who do not drop out (majority class). This imbalance can reduce the model's ability to classify students at risk of dropping out. This study proposes classification optimization using the Random Forest algorithm to handle class imbalances in student dropout data. To overcome class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) and Edited Nearest Neighbors (ENN) techniques are used. Additionally, the attribute selection method is also applied to enhance the predictive results. The test results demonstrate that the combination of implementing feature selection with Chi-Square, followed by class imbalance handling with SMOTE-ENN, provided the most optimal predictive performance for identifying the status of both dropouts and graduates. © 2023 IEEE.

关键词： Students

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Design of Intelligent Detection and Alarm System for Accident Anti-collision Based on ARM Technology 5th

Design of Intelligent Detection and Alarm System for Acciden...

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5th International Conference on Smart Computing and Communication, SmartCom 2020

作者： Tan, Minfu Hong, Yujie Quan, Guiyan Lin, Yinhua Xi, Erhui Zhang, Shiwen Huang, Weihong Big Data Development and Research Center Guangzhou College of Technology and Business Guangzhou528138 China College of Computer Science and Electronic Engineering Hunan University Changsha410000 China Department of Computer Science and Engineering Guangzhou College of Technology and Business Guangzhou528138 China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China

ISBN: (纸本)9783030747169

With the increase of social transportation, traffic accidents are not uncommon, and various disputes often arise in accident responsibility. Law enforcement agencies can only understand the situation at the scene of the accident through the two parties who caused the accident, and the two sides of the accident often have their own opinions. This article designs an anti-collision intelligent detection and alarm system. When cars collide, the collision video recorded during driving can be used as direct evidence, saving a lot of time for both sides of the accident and law enforcement agencies, and improving the efficiency of law enforcement. This article first introduces the research significance of the ARM-based accident collision detection and alarm system, and then deeply analyzes the key technologies used in the system, including the main control A10, embedded system Melis, bus I2C and acceleration sensor BMA250. Then analyze the entire hardware module of the system. After that, it analyzes in detail the initialization and control of the acceleration sensor BMA250 in the software, and the alarm processing process when an accident occurs. Finally, we summarized the design and research of the entire system, analyzed the system's shortcomings, and looked forward to the work that needs to be improved in the future. © 2021, Springer Nature Switzerland AG.

关键词： Alarm systems

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