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1st International Conference on data Engineering and Machine Intelligence, ICDEMI 2023

作者： Mohan, Ramya Kadry, Seifedine Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates

ISBN: (纸本)9789819776153

In addition to traditional approaches, several computerized techniques have been developed to enhance the results. The automation in the medical domain considerably reduced the burden and improved disease diagnosis, treatment planning, and implementation. With the advent of automation, the medical domain has greatly reduced the burden of diagnosing diseases, developing treatment plans, and implementing them. This research aims to develop image-processing assisted detection of Diabetic Foot Ulcer (DF) from digital photographs. Digital photographs are used to assist in diagnosing Diabetic Foot Ulcer (DF). As part of the developed tool, (i) images are collected and resized, (ii) Otsu/Kapur and Hummingbird-optimizer thresholding is supported, (iii) level-set segmentation is used to extract the DF region, and (iv) the developed scheme is compared with the binary mask and its performance is verified. A total of 300 images are examined in this work, and MATLAB software is used to conduct the experiments. Comparing Otsu + LS with Kapur + LS, the overall result achieved with Kapur’s enhanced image is better than that of Otsu + LS. Authentic clinical DF images can be used to verify the proposed scheme in the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： MATLAB

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An Overview of Different Types of Recommendations Systems-A Survey 4

An Overview of Different Types of Recommendations Systems-A ...

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4th International Conference on Innovative Trends in Information Technology, ICITIIT 2023

作者： Duraisamy, Premkumar Yuvaraj, S. Natarajan, Yuvaraj Niranjani, V. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665494144

In recent years the boom of internet and social media usage everyone spend their invaluable time in social media app and looking for the solution for all kind of their problems. This work analysis deeply on how recommendation system works and its types in different platforms. Most of the modern recommendation system use machine learning algorithms like linear regression, random forest regression and support vector model with collaborative filtering method. Recommendation is nothing but an choice making system. It is vary from person to person based on their interest, culture, locality, education background, interpersonal skills etc., The huge item can be filtered from one by one based on each parameter and finally it will reach the right recommendation item. The research community has worked tremendous way in the field of recommendation system and produced huge variety of result. This survey enlightening the ideas about variety of recommendation system and techniques used by the research community. © 2023 IEEE.

关键词： Recommender systems

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Fast Spatiotemporal Learning Framework for Traffic Flow Forecasting

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IEEE Transactions on Intelligent Transportation Systems 2023年第8期24卷 8606-8616页

作者： Guo, Canyang Chen, Chi-Hua Hwang, Feng-Jang Chang, Ching-Chun Chang, Chin-Chen Fuzhou University College of Computer and Data Science Fuzhou350108 China National Sun Yat-sen University Department of Business Management Kaohsiung804 Taiwan University of Warwick Department of Computer Science CoventryCV4 7AL United Kingdom Feng Chia University Department of Information Engineering Taichung407802 Taiwan

The graph convolution network (GCN), whose flexible convolution kernels perfectly adapt to the complex topology of the road network, has gradually dominated the spatiotemporal dependency learning of traffic flow data. Defining and learning the spatiotemporal characteristics and relationships of the traffic network efficiently and accurately, which are the important prerequisites for the success of the GCN, have become one of the most burning research problems in the field of intelligent transportation systems. This paper proposes a fast spatiotemporal learning (FSTL) framework containing the fast spatiotemporal GCN module, which reduces the computational complexity of the spatiotemporal GCN from O(k2} to O(k) , where k is the number of time steps of data learned in each GCN operation. To mine globally and fast the correlations of road node pairs, a correlation analysis based on the normal distribution with the complexity of O(N)}, where N is the number of nodes in the traffic network, is proposed to construct the global correlation matrix. Besides, the multi-scale temporal learning is integrated into the FSTL to overcome the receptive field constraints of the spatiotemporal GCN. The experimental results on four real-world datasets demonstrate that the FSTL achieves 48.88% and 5.26% reductions in the training time and mean absolute error, respectively, compared with the state-of-the-art model. © 2000-2011 IEEE.

关键词： Convolution

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CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

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computers, Materials & Continua 2023年第9期76卷 2779-2793页

作者： Mehwish Zafar JaveriaAmin Muhammad Sharif Muhammad Almas Anjum Seifedine Kadry Jungeun Kim Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science University ofWahWah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Worldwide cotton is the most profitable cash *** year the production of this crop suffers because of several *** an early stage,computerized methods are used for disease detection that may reduce the loss in the production of *** several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex *** to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease *** in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×*** that,the extracted features are serially concatenated having a feature vector lengthN×*** prominent features are selected usingEmperor PenguinOptimizer(EPO)*** method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle *** EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,*** classification is performed using 5,7,and 10 folds *** Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.

关键词： Deep learning cotton disease detection features selection classification efficientnet-b0 inception-v3 quadratic discriminant analysis subspace KNN

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Learning Shadow Variable Representation for Treatment Effect Estimation under Collider Bias 41

Learning Shadow Variable Representation for Treatment Effect...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Baohong Li, Haoxuan Xiong, Ruoxuan Wu, Anpeng Wu, Fei Kuang, Kun College of Computer Science and Technology Zhejiang University Hangzhou China Center for Data Science Peking University Beijing China Department of Quantitative Theory & Methods Emory University Atlanta United States

One of the significant challenges in treatment effect estimation is collider bias, a specific form of sample selection bias induced by the common causes of both the treatment and outcome. Identifying treatment effects under collider bias requires well-defined shadow variables in observational data, which are assumed to be related to the outcome and independent of the sample selection mechanism, conditional on the other observed variables. However, finding a valid shadow variable is not an easy task in real-world scenarios and requires domain-specific knowledge from experts. Therefore, in this paper, we propose a novel method that can automatically learn shadow-variable representations from observational data without prior knowledge. To ensure the learned representations satisfy the assumptions of the shadow variable, we introduce a tester to perform hypothesis testing in the representation learning process. We iteratively generate representations and test whether they satisfy the shadow-variable assumptions until they pass the test. With the help of the learned shadow-variable representations, we propose a novel treatment effect estimator to address collider bias. Experiments show that the proposed methods outperform existing treatment effect estimation methods under collider bias and prove their potential application value. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Provably Secure Conditional-Privacy Access Control Protocol for Intelligent Customers-Centric Communication in VANET

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1747-1756页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Ayub, Muhammad Faizan Bashir, Ali Kashif Omar, Marwan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Manchester Metropolitan University Department of Computing and Mathematics ManchesterM15 6BH United Kingdom Woxsen University Woxsen School of Business Hyderabad502345 India Lebanese American University Department of Computer Science and Mathematics Beirut1102 2801 Lebanon Illinois Institute of Technology Information Technology and Management ChicagoIL60555 United States

Globally, the development of Intelligent Cyber-Physical Transportation Systems (ICTS) aims to tackle several challenges, including reducing traffic accidents and fuel usage, alleviating congestion, shortening travel time, and enhancing overall transportation safety. These systems leverage advanced customer-centric communication and networked control methods, such as inter-vehicle, vehicle-to-roadside (V2R), and vehicle-to-vehicle (V2V) communication through the use of vehicular ad hoc networks (VANETs) to cover all aspects of transportation-based information. In existing systems, once devices are registered with a Trusted Authority (TA), subsequent authentication still relies on the TA's assistance. However, these devices typically remain stationary, and frequent interaction with the TA becomes impractical and costly in highly mobile VANET environments. To address this challenge, we propose a secure access control protocol with conditional privacy for VANETs. Unlike other protocols, our protocol does not require the TA's involvement during authentication between vehicles and RSUs. Additionally, our protocol leverages pseudonym mechanisms to provide conditional privacy, enabling legitimate vehicles to remain anonymous while malicious ones can be tracked. Our proposed scheme is supported by both formal and informal security analyses and has been shown to be secure against several known attacks in VANETs. Furthermore, compared to relevant studies, our protocol achieves 30.2505% of efficiency in terms of computation cost and 11.09276% of efficiency in terms of communication cost, respectively. © 1975-2011 IEEE.

关键词： Vehicular ad hoc networks

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Sketch2Human: Deep Human Generation with Disentangled Geometry and Appearance Constraints

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IEEE Transactions on Visualization and computer Graphics 2024年 PP卷 1-14页

作者： Qu, Linzi Shang, Jiaxiang Ye, Hui Han, Xiaoguang Fu, Hongbo School of Creative Media City University of Hong Kong Hong Kong Department of Computer Science & Engineering HKUST Hong Kong Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China

Geometry- and appearance-controlled full-body human image generation is an interesting but challenging task. Existing solutions are either unconditional or dependent on coarse conditions (e.g., pose, text), thus lacking explicit geometry and appearance control of body and garment. Sketching offers such editing ability and has been adopted in various sketch-based face generation and editing solutions. However, directly adapting sketch-based face generation to full-body generation often fails to produce high-fidelity and diverse results due to the high complexity and diversity in the pose, body shape, and garment shape and texture. Recent geometrically controllable diffusion-based methods mainly rely on prompts to generate appearance. It is hard to balance the realism and the faithfulness of their results to the sketch when the input is coarse. This work presents Sketch2Human, the first system for controllable full-body human image generation guided by a semantic sketch (for geometry control) and a reference image (for appearance control). Our solution is based on the latent space of StyleGAN-Human with inverted geometry and appearance latent codes as input. Specifically, we present a sketch encoder trained with a large synthetic dataset sampled from StyleGAN-Human's latent space and directly supervised by sketches rather than real images. Considering the entangled information of partial geometry and texture in StyleGAN-Human and the absence of disentangled datasets, we design a novel training scheme that creates geometry-preserved and appearance-transferred training data to tune a generator to achieve disentangled geometry and appearance control. Although our method is trained with synthetic data, it can also handle hand-drawn sketches. Qualitative and quantitative evaluations demonstrate the superior performance of our method to state-of-the-art methods. IEEE

关键词： Semantics

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A Mobile App for Seed Grading by Incorporating Deep Learning Approach with Bio Inspired Optimization Technique 7

A Mobile App for Seed Grading by Incorporating Deep Learning...

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Mundada, Monica R. Sowmya, J.B. Anurag, G. Mani, G.M. Narayana, Satya Ramaiah Institute of Technology Department of Master of Computer Applications Bengaluru India Ramaiah Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Ramaiah Institute of Technology Department of Computer Science Bengaluru India

ISBN: (纸本)9798350343144

One of the main production stumbling blocks that hinders the growth of healthy crops is seed quality. The ability to cultivate more nutrient-dense, higher-yielding crops that can help feed a growing population is made possible by high-quality seeds, which are crucial for ensuring food security. As a result, it is crucial to quickly and accurately identify high-quality seeds. Manually identifying the type of wheat requires skill and time. It gets difficult to manually differentiate seeds from an array when they all seem so identical. This prompted us to develop an app that would automate the process of determining seed quality. With the corn seed data set, a convolutional neural network is trained. By eliminating the thick layers, the characteristics of this neural network are recovered. The moth flame optimization technique, a bio-inspired algorithm, is used to reduce noise and extraneous data. The KNN classifier is used to assess the fitness of the features that have been chosen. Lastly, the KNN classifier is used to categorize the seeds based on the fewest attributes © 2023 IEEE.

关键词： Convolutional neural networks

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Improving Audience Ratings Prediction of Japanese TV Dramas Using Knowledge-Based Embeddings 23

Improving Audience Ratings Prediction of Japanese TV Dramas ...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Bonnell, Jerry Wuchty, Stefan Ogihara, Mitsunori Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States University of Miami Department of Computer Science Coral GablesFL33146 United States

ISBN: (纸本)9798350374889

Accurate prediction of television (TV) audience ratings is a crucial technology for advertisers and broadcast-ers, which influences advertising costs and indicates program popularity. As for Japanese TV dramas, previous studies have examined factors available prior to broadcast, such as cast com-position, metadata variables (e.g., scheduled time slot, broadcasting station, episode duration), and indicators of actor popularity. However, such studies have been limited in their evaluation of contextual information related to these factors and have not considered additional modalities such as text data. Here, we propose the use of knowledge-based embeddings derived from Japanese Wikipedia to improve audience ratings prediction. The proposed bag-of-entities method is applied to represent dramas in terms of cast, production team, and plot synopsis elements, and is evaluated against conventional metadata features. In an ablation study, supervised forecasting models using Support Vector Machine (SVM) are trained on combinations of the feature groups. These are devised and tested on viewership data collected from the Audience Rating TV (ARTV) database for 574 Japanese TV dramas aired between 2003 and 2021. Our experimental results indicate that a SVM model conditioned on the full feature set outperforms baseline methods, achieving a 77.4 % F1-score. After stratifying predictions based on time slot popularity, the approach is found to be effective for predicting audience ratings in challenging, high-popularity time slots, with improvements in recognition rate up to 19.1 %. © 2024 IEEE.

关键词： Support vector machines

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A Centralized Blockchain Architecture with Optimum Sharing 8

A Centralized Blockchain Architecture with Optimum Sharing

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Ramasamy, Viswanathan Padmapriya, S. Kavitha, G. Lekha, D. Kl Univrsity Department of Computer Science and Engineering Andhra Pradesh Gundoore India Velammal Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Namakkal India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350396638

The monolithic network architectures are used in Bitcoin and other big cryptos. Different hierarchical ways to protect the decentralized blockchain have been discussed to improve its usability. However, no debate in the literature on how an appropriate shard size can be calculated to optimize the output and how deceptive or unreliable nodes can affect the optimum shard size range. This study includes an exchange and authentication scheme for hierarchical blockchain architecture in order to fix these issues. The hierarchy splits the network nodes into many disjoint fragments, with most transfers unregulated between these shards. Taking into account the performance and precision of the transaction validation in the case of malicious or defective nodes, the optimum shard size is calculated. It ensures that most of the good shards are available with the right scale, according to the highest acceptable threshold of defective nodes. It also offers a method to distinguish damaged shards by overlapping a tiny proportion of purchases across all shields and discarding all transactions. The theoretical efficiency analysis and the influence of network sharing on performance were based on experimental findings conducted on Amazon EC-2 nodes. © 2023 IEEE.

关键词： Authentication

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