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1st International Conference on computer, Power and Communications, ICCPC 2022

作者： Sundaram, S. Ganesh Ponmalar, A. Vijay Priya, V. Deeba, S. Harish Anantha Krishnan, R. Vishwath, J.H. Geomanage Corporation Higashi-nihonbashi Japan Sri Sai Ram Institute of Technology Department of Information Technology Tambaram India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Sri Manakula Vinayagar Engineering College Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798350397840

Weed generally refers to any plant growing where it is not wanted. These unwanted plants are undesirable as they are aggressive in growth, consuming light, water, nutrients, and space that desirable crops utilize. It would affect production efficiency both in quality and quantity. For decades, weeds have been removed using chemicals that are not very effective. With the advancement in technology, this paper proposes an image processing-based framework and machine learning model for weed detection using Convolutional Neural Network (CNN) and the Xception model for classification. With increased layers, the efficiency and accuracy of the system are improved compared to existing methods. © 2022 IEEE.

关键词： Production efficiency

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DFD-Net:lung cancer detection from denoised CT scan image using deep learning

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Frontiers of computer science 2021年第2期15卷 119-131页

作者： Worku J.SORI Jiang FENG Arero W.GODANA Shaohui LIU Demissie J.GELMECHA School of Electrical Engineering and Computing Department of Computer Science and EngineeringAdama Science and Technology UniversityAdama1888Ethiopia Faculty of Natural Computational Science Department of Mathematics Bule Hora UniversityBule HoraHagere Maryam144Ethiopia School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China Peng Cheng Laboratory Shenzhen518052China School of Electrical Engineering and Computing Department of Electronics and Communications EngineeringAdama Science and Technology UniversityAdama1888Ethiopia

The availability of pulmonary nodules in CT scan image of lung does not completely specify *** noise in an image and morphology of nodules,like shape and size has an implicit and complex association with cancer,and thus,a careful analysis should be mandatory on every suspected nodules and the combination of information of every *** this paper,we introduce a“denoising first”two-path convolutional neural network(DFD-Net)to address this *** introduced model is composed of denoising and detection part in an end to end ***,a residual learning denoising model(DR-Net)is employed to remove noise during the preprocessing ***,a two-path convolutional neural network which takes the denoised image by DR-Net as an input to detect lung cancer is *** two paths focus on the joint integration of local and global *** this end,each path employs different receptive field size which aids to model local and global *** further polish our model performance,in different way from the conventional feature concatenation approaches which directly concatenate two sets of features from different CNN layers,we introduce discriminant correlation analysis to concatenate more representative ***,we also propose a retraining technique that allows us to overcome difficulties associated to the image labels *** found that this type of model easily first reduce noise in an image,balances the receptive field size effect,affords more representative features,and easily adaptable to the inconsistency among nodule shape and *** intensive experimental results achieved competitive results.

关键词： medical image discriminant correlation analysis features fusion image detection denoising

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A Machine Learning based Real-Time Application for Engagement Detection 9

A Machine Learning based Real-Time Application for Engagemen...

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9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Iacobelli, Emanuele Russo, Samuele Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Department of Psychology Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

The study of human engagement has significantly grown in recent years, particularly accelerated by the interaction with a growing number of smart computing machines [1, 2, 3]. Engagement estimation has significant importance across various domains of study, including advertising, marketing, human-computer interaction, and healthcare [4, 5, 6]. In this paper, we propose a real-time application that leverages a single RGB camera to capture user behavior. Our approach implements a novel method for estimating human engagement in real-world scenarios by extracting valuable information from the combination of facial expressions and gaze direction analysis. To acquire this data, we employed fast and accurate machine learning algorithms from the external library dlib, along with custom versions of Residual Neural Networks implemented from scratch. For training our models, we used a modified version of the DAiSEE dataset, a multi-label user affective states classification dataset that collects frontal videos of 112 different people recorded in real-world scenarios. In the absence of a baseline for comparing the results obtained by our application, we conducted experiments to assess its robustness in estimating engagement levels, leading to very encouraging results. © 2023 Copyright for this paper by its authors.

关键词： Eye tracking

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drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network

arXiv

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arXiv 2024年

作者： Inoue, Yoshitaka Lee, Hunmin Fu, Tianfan Luna, Augustin Department of Computer Science and Engineering University of Minnesota United States Computational Biology Branch National Library of Medicine Computer Science Department Rensselaer Polytechnic Institute United States Developmental Therapeutics Branch National Cancer Institute

Drug development is a lengthy process with a high failure rate. Increasingly, machine learning is utilized to facilitate the drug development processes. These models aim to enhance our understanding of drug characteristics, including their activity in biological contexts. However, a major challenge in drug response (DR) prediction is model interpretability as it aids in the validation of findings. This is important in biomedicine, where models need to be understandable in comparison with established knowledge of drug interactions with proteins. drGAT, a graph deep learning model, leverages a heterogeneous graph composed of relationships between proteins, cell lines, and drugs. drGAT is designed with two objectives: DR prediction as a binary sensitivity prediction and elucidation of drug mechanism from attention coefficients. drGAT has demonstrated superior performance over existing models, achieving 78% accuracy (and precision), and 76% F1 score for 269 DNA-damaging compounds of the NCI60 drug response dataset. To assess the model's interpretability, we conducted a review of drug-gene co-occurrences in Pubmed abstracts in comparison to the top 5 genes with the highest attention coefficients for each drug. We also examined whether known relationships were retained in the model by inspecting the neighborhoods of topoisomerase-related drugs. For example, our model retained TOP1 as a highly weighted predictive feature for irinotecan and topotecan, in addition to other genes that could potentially be regulators of the drugs. Our method can be used to accurately predict sensitivity to drugs and may be useful in the identification of biomarkers relating to the treatment of cancer patients. © 2024, CC BY.

关键词： Genes

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Challenges in Fuzzy Decision Making for Future Research 21

Challenges in Fuzzy Decision Making for Future Research

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21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022

作者： Trillo, José Ramón Herrera-Viedma, Enrique Higueras-Ruiz, María José Alonso, Sergio Morente-Molinera, Juan Antonio Cabrerizo, Francisco Javier Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Department of Information and Communication University of Granada Granada18071 Spain Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

ISBN: (纸本)9781643683164

Fuzzy Decision Making is a line of research that has had an important presence in the literature since 1965 until nowadays. From that year to the present, the line of research has been consolidated. The research field goals have evolved along with all the research that makes use of these systems and the new technologies requirements. Therefore, new challenges, different from the ones that were faced at the beginning, need to be solved. The aim of this paper is to describe the recent challenges faced by those working in this line of research. This paper will help researchers to know how their work should be oriented in the future. © 2022 The authors and IOS Press. All rights reserved.

关键词： Decision making

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A Fully Automatic Visual Attention Estimation Support System for A Safer Driving Experience 9

A Fully Automatic Visual Attention Estimation Support System...

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9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Fiani, Francesca Russo, Samuele Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Department of Psychology Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Drivers’ attention is a key element in safe driving and in avoiding possible accidents. In this paper, we present a new approach to the task of Visual Attention Estimation in drivers. The model we introduce consists of two branches, one which performs Gaze Point Detection to determine the exact point of focus of the driver, and the other which executes Object Detection to recognize all relevant elements on the road (e.g. vehicles, pedestrians, and traffic signs). The combination of the two outputs from the two branches allows us to determine whether the driver is attentive and, eventually, on which element of the road they are focusing. Two models are tested for the gaze detection task: the GazeCNN model and a model consisting of a CNN+Transformer. The performance of both models is evaluated and compared with other state-of-the-art models to choose the best approach for the task. Finally, the results of the Visual Attention Estimation performed on 3761 pairs of images (driver view and corresponding road view) from the DGAZE dataset are reported and analyzed. © 2023 Copyright for this paper by its authors.

关键词： Traffic signs

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A multimodal sentiment recognition method based on attention mechanism 5

A multimodal sentiment recognition method based on attention...

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5th Artificial Intelligence and Cloud Computing Conference, AICCC 2022

作者： Liu, Bo Zhang, Jidong Xu, Yuxiao Li, Jianqiang Pei, Yan Qu, Guanzhi School of Mathematical and Computational Sciences Massey University Palmerston North New Zealand Faculty of Information Technology Beijing University of Technology Beijing China Computer Science Division The University of Aizu Japan Computer Science and Engineering Department Oakland University RochesterMI48309 United States

ISBN: (纸本)9781450398749

Effective sentiment analysis on social media data can help to better understand the public's sentiment and opinion tendencies. Combining multimodal content for sentiment classification uses the correlation information of data between modalities, thereby avoiding the situation that a single modality does not fully grasp the overall sentiment. This paper proposes a multimodal sentiment recognition model based on the attention mechanism. Through transfer learning, the latest pre-Trained model is used to extract preliminary features of text and image, and the attention mechanism is deployed to achieve further feature extraction of prominent image key regions and text keywords, better mining the internal information of modalities and learning the interaction between modalities. In view of the different contribution of each modal to sentiment classification, a decision-level fusion method is proposed to design fusion rules to integrate the classification results of each modal to obtain the final sentiment recognition result. This model integrates various unimodal features well, and effectively mines the emotional information expressed in Internet social media comments. This method is experimentally tested on the Twitter dataset, and the results show that the classification accuracy of sentiment recognition is significantly improved compared with the single-modal method. © 2022 ACM.

关键词： Sentiment analysis

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On Locally Identifying Coloring of Cartesian Product and Tensor Product of Graphs

arXiv

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arXiv 2023年

作者： Bhyravarapu, Sriram Kumari, Swati Reddy, I. Vinod The Institute of Mathematical Sciences HBNI Chennai India Department of Computer Science and Engineering IIT Bhilai India

For a positive integer k, a proper k-coloring of a graph G is a mapping f : V (G) → {1, 2, . . ., k} such that f(u) ≠ f(v) for each edge uv of G. The smallest integer k for which there is a proper kcoloring of G is called the chromatic number of G, denoted by χ(G). A locally identifying coloring (for short, lid-coloring) of a graph G is a proper k-coloring of G such that every pair of adjacent vertices with distinct closed neighborhoods has distinct set of colors in their closed neighborhoods. The smallest integer k such that G has a lid-coloring with k colors is called locally identifying chromatic number (for short, lid-chromatic number) of G, denoted by χlid(G). This paper studies the lid-coloring of the Cartesian product and tensor product of two graphs. We prove that if G and H are two connected graphs having at least two vertices then (a) χlid(GH) ≤ χ(G)χ(H) − 1 and (b) χlid(G × H) ≤ χ(G)χ(H). Here GH and G × H denote the Cartesian and tensor products of G and H respectively. We determine the lid-chromatic number of CmPn, CmCn, Pm × Pn, Cm × Pn and Cm × Cn, where Cm and Pn denote a cycle and a path on m and n vertices respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Coloring

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Enhanced Cluster Head Based Data Gathering (ECHGS) Technique in IoT Based Smart Irrigation System 1st

Enhanced Cluster Head Based Data Gathering (ECHGS) Technique...

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1st International Conference on Artificial Intelligence for Smart Community, AISC 2020

作者： Padmanaban, K. SenthilKumar, A.M. Velmurugan, A.K. Madhan, E.S. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science & Technology Tamil Nadu Chennai India Department of Computational Intelligence SRM Instititute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9789811621826

Internet of Things (IOT) is an emerging technique which is used in several applications like home automation, office automation, hospital monitoring, network device monitoring, industrial related problems and smart agricultural monitoring system. In IoT environment, sensors are dynamically connected with the network to monitoring and routing the data to the server. Energy utilization and maximizing throughput are important issues in the Internet of Things (IOT). Energy plays a vital role in increasing network lifetime. Throughput improves the network performance and it is calculated based on average successful data delivered at a time instance. Various models such as cluster tree, clustering and Tree Cluster based Data Gathering Scheme (TCDGS) are used for handling maximizing energy and throughput. In this paper, a novel technique "Enhanced Cluster Head based Data gathering" (ECHGS) is presented to focus the energy and delay in the Internet of Things (IOT) based smart irrigation system for agriculture. In this proposed technique, the cluster head for the group of sensor nodes is selected based on the workload and energy. The node which has high residual energy and less workload is selected as a cluster head to collect and deliver the data to the server node effectively. The proposed technique results are evaluated and compared to the existing techniques in terms of residual energy and throughput. The residual energy is saved by 4.54% higher than the Tree Cluster based Data Gathering Scheme (TCDGS) and the throughput is maximized by 11.57% higher than the Tree Cluster based Data Gathering Scheme (TCDGS). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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A Detailed Structured Monitoting System for ICU Patient Mortality rate by using AI Algorithm

A Detailed Structured Monitoting System for ICU Patient Mort...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Shivani Joshi Rajiv Kumar Vipin Rai Avinash Dwivedi Vikash Chandra Tripathi Department of Computer Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida India Department of Applied Computational Science and Engineering NVIDIA AI and Learning & Research Center G L Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science & Engineering Galgotias University Greater Noida India Department of Computer Science & Engineering Galgotias University Greater Noida

The complexity and seriousness of diseases in ICU patients, though, make it difficult for medical practitioners to identify high-risk patients who may face irreversible harm. With ANN (Artificial Neural Network), this study will provide an ICU admission prediction model to help identify high-risk patients at an earlier stage. After collecting and analyzing extensive data from various perspectives including demographics, medical history, vital signs, test results and treatment modalities, exploratory data analysis has been conducted. These are used as markers on how statistical techniques and machine learning algorithms can be used to predict mortality among critically ill patients. Consequently, the parameters are designed within the ANN model to create a predictive model that will be able to effectively classify those at higher risk. Advanced machine learning algorithms together with modern tools such as deep learning techniques will aid in the prompt analysis of complex information for accurate forecasts by this predictive model. The ANN model’s training set will include a large dataset of ICU patients so that it can become reliable and valid. This makes the predictive model developed capable of accurately identifying high risk cases expeditiously

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