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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Kanagaraj, R. Elakiya, E. Kumaresan, A. Selvakumar, J. Suganya, J. Shruthi, K. School of Computer Science and Engineering Vellore Institute of Technology Vellore India Vellore Institute of Technology Department of Computer Science and Engineering Chennai India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Mailam Engineering College Mailam India Bosch Global Software Technologies Coimbatore India

ISBN: (纸本)9798350321425

Agriculture is under tremendous pressure to produce more productivity with fewer resources as a result of the expanding global population. Decision-making along the entire supply chain needs to be improved considering competing economic, environmental, and societal demands. Data from satellites, drones, sensors, and other sources must be processed in order to make informed judgments. However, sophisticated machine learning and data analytics methods must be used due to the complexity and volume of the data. This initiative covers crucial duties in yield prediction and precision agriculture. Management zones are areas of a field that have high inter-regional variability and low intra-regional variability, dividing the field into uniform zones for which agronomic practices should be modified independently. For fertilizer optimization and post-harvest logistics, yield prediction is crucial. In order to make up for the nutrients that have been taken out of the soil, fertilizer type and quantity must be adjusted accordingly. The yield data is also essential for enhancing harvesting, shipping, storage, and sales. So, utilizing web scraping and convolution neural networks, the proposed model has been forecast yields from input satellite photos. The result of investigations is illustrated that the artificial neural network algorithm is used to predicting the agricultural data rules and powerfully decrease the error rate from crop yield data. © 2023 IEEE.

关键词： Neural networks

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A Multiobjective Evolutionary Multitasking Algorithm Based on Decomposition and Multiple Knowledge Transfer 4

A Multiobjective Evolutionary Multitasking Algorithm Based o...

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4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022

作者： Wu, Zhongjian Zhu, Qingling Chen, Jianyong Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Multiobjective evolutionary multitasking (MOEMT) has become very popular in recent years, as this kind of methods aims to solve a set of multiobjective optimization problems (MOPs) simultaneously, which has been validated to be more promising than the traditional way that solves MOPs separately. However, most existing studies of MOEMT solve the MOP as a whole and use one single knowledge transfer strategy for solving all tasks, which is not so efficient as the MOP could be further decomposed into a set of subproblems and needs different strategies for knowledge transfer. To solve this problem, this paper suggests a new MOEMT algorithm (MOEMTA) based on decomposition and multiple knowledge transfer (MOEMTA-DM). First, the decomposition method is used to transform an MOP in each task into a set of subproblems, and the subproblems with greater performance improvement are chosen. Then their associated solutions will be selected to run multiple knowledge transfer strategies via both implicit and explicit ways, which can share the useful search experience between tasks. This way, computational resources can be adaptively assigned to speed up the solving of all tasks and the effect of knowledge transfer among all tasks can be improved. The effectiveness of the proposed algorithm was verified by studying benchmark multitasking MOPs (MTMOPs). Experimental results showed the proposed algorithm is more effective than other compared MOEMT algorithms. © 2022 IEEE.

关键词： Multiobjective optimization

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Enhancing Secure Development in Globally Distributed software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

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computers, Materials & Continua 2024年第6期79卷 5031-5049页

作者： Marya Iqbal Yaser Hafeez Nabil Almashfi Amjad Alsirhani Faeiz Alserhani Sadia Ali Mamoona Humayun Muhammad Jamal University Institute of Information Technology PMAS-Arid Agriculture UniversityRawalpindiPakistan Department of Software Engineering College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72388Saudi Arabia Department of Mathematics Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindiPakistan

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software ***,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity *** paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of *** the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and ***,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient *** experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in *** digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

关键词： Machine Learning variability management cybersecurity digital ecosystems cyber-resilience

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IVRSandplay: An Immersive Virtual Reality Sandplay System Coupled with Hand Motion Capture and Eye Tracking 26

IVRSandplay: An Immersive Virtual Reality Sandplay System Co...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Haibo Ding, Ding Liu, Yuhao Wang, Chi Southeast University School of Computer Science and Engineering Nanjing211189 China Southeast University College of Software Engineering Nanjing211189 China

ISBN: (纸本)9798350331684

Sandplay therapy is a novel psychological consultation and treatment method for people suffering from mental stress and psychological issues. It is popular among the masses owing to its interest and curative effect. However, traditional sandplay games face several problems, such as the requirement of large space, low accessibility, difficulty in keeping treatment records, and lack of engagement. To solve these problems, we put forward a sandplay system based on virtual reality technology. This system provides a virtual scenario of the sandplay room, allowing users to place various sandplay miniatures naturally and smoothly into the sandbox with multiple themes by using motion capture gloves. What is more, users can switch to the first-person perspective to experience the world they have built. Additionally, the system could collect users' eye-tracking data and analyze it by a machine learning algorithm to estimate individuals' emotions which could assist in further treatment. Finally, twelve participants were invited to experience the system. They were asked to evaluate this system as well as their emotional status before and after using it. The experimental results indicate that the participants are satisfied with this system, while their emotions can be improved to a certain extent. © 2023 IEEE.

关键词： Virtual reality

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Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus 25

Transformer-based Text Classification on Unified Bangla Mult...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Sourav, Md Sakib Ullah Mahmud, Mohammad Sultan Zheng, Hua Aljaidi, Mohammad Talukder, Md. Simul Hasan Bin Sulaiman, Rejwan Nur, Abdullah Hafez Al-Qerem, Ahmad Shandong University of Finance and Economics School of Management Science and Engineering Jinan China Shenzhen University College of Computer Science and Software Engineering Senzhen China Software Engineering Institute of Guangzhou Department of Software Engineering Guangzhou510990 China Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Dhaka University of Engineering and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh Northumbria University Newcastle Tayne School of Computer Science & Technology Northumbria United Kingdom International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong Bangladesh Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798331540012

Due to its importance in studying people's thoughts on various Web 2.0 services, emotion classification is a critical undertaking. Most existing research is focused on the English language, with little work on low-resource languages. Though sentiment analysis, particularly emotion classification in English, has received increasing attention in recent years, little study has been done in the context of Bangla, one of the world's most widely spoken languages. In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes, i.e., anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labelled datasets of Bangla comments on six emotion classes with fresh manually labelled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available. © 2024 IEEE.

关键词： Classification (of information)

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Intelligent Vocal Training Assistant System 2nd

Intelligent Vocal Training Assistant System

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2nd EAI International Conference on Edge Computing and IoT: Systems, Management and Security, ICECI 2021

作者： Li, Yihong Luo, Chengzhe College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9783031042300

In professional vocal training, a way to evaluate the quality of vocalization is often needed. In order to solve various problems caused by the lack of professional instructors, a vocal training system for detecting the closed state of the vocal cords is proposed. We have proposed a robust vocal training system for the detection of the closed state of the vocal cords on the mobile terminal, which can analyze the closure of the vocal cords of the human body during vocalization, so as to evaluate the vocal cord ability without a professional teacher or professional equipment. In this system, we can use two wearable sensors, vibrating plate and headset, to collect the signals of the human vocal cords, or directly use the microphone of the mobile device to collect. And we use the convolutional neural network to analyze the signals and classify the closed state of the vocal cords. In order to build this system, we constructed a vowel data set classified by degree vocal cords closure, including the wearable sensors and mobile phone microphones we used. We compared the performance of the traditional vocalization pattern classification method and the convolutional neural network method we used to classify the vocal cord closure types on the data set we constructed and the public data set, and finally tested under two noise environments, and the preliminary results proved the usability of our system. © 2022, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Professional aspects

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MFT: Modal Fusion Transformer for Cross-Modal Fusion in 3D Object Detection

MFT: Modal Fusion Transformer for Cross-Modal Fusion in 3D O...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Cai, Haojie Yin, Dongfu Yu, Fei Xiong, Siting Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University Shenzhen China

ISBN: (纸本)9798350368741

Increasing attention has been garnered by LiDAR points and multi-view images fusion based on Transformer for supplementing another modality in 3D object detection. However, challenges persist for cross-modal fusion methods due to the heterogeneity of these two modalities, leading to issues such as inaccurate detection results encountered by Transformer-based methods. In this work, a one-way mid-level fusion based framework for 3D object detection named Modal Fusion Transformer (MFT) using LiDAR points and multi-view images is introduced. It comprises a Depth-Guided Generation(DGG) module, Position Encoding Generation (PEG) module and Cross Modal Fusion(CMF) module. Specifically, depth information from point cloud is utilized for both gathering the image depth map and initializing object queries in DGG. PEG unifies the form of position encoding from LiDAR features and multi-view image features. Depth and position information of object from images is aggregated to point clouds by CMF, which fully explores dual-modal information. Furthermore, a Modal Fusion Network with deformable attention named fast-MFT is introduced to reduce the relatively large computational cost associated with global attention. Our MFT and fast-MFT achieve competitive performance while maintaining a faster inference speed than other models. © 2025 IEEE.

关键词： 3D Object Detection Autonomous Driving Cross-Modal Transformer Positional Embedding Speed comparison

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Research on Improved Quick Sort Algorithm with Duplicate Value Handling 8

Research on Improved Quick Sort Algorithm with Duplicate Val...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Hemin Yadav, Amit Khan, Asif Sah, Abhishek Pratap Chengdu Neusoft University College of Computer and Software Chengdu China Charles Darwin University College of Engineering It and Environment NT Australia Integral University Department of Computer Application Lucknow India University of Electronic Science and Technology of China School of Management Science and Engineering Chengdu China

ISBN: (纸本)9798350376425

An improved algorithm based on Quick Sort algorithm research method is proposed to deal with prevailing duplicate values in the sorting of data. The duplicate values are specially processed, which effectively reduces the number of division and greatly reduces the number of recursions. The experimental results show that the time efficiency is improved by more than 50% when dealing with large data than the traditional Quick Sort algorithm without reducing the space efficiency. © 2024 IEEE.

关键词： Duplicate Value Quick Sort Recursion

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Clustering-based Autoencoding for Dynamic Multiobjective Evolutionary Optimization 4

Clustering-based Autoencoding for Dynamic Multiobjective Evo...

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4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022

作者： Ye, Yulong Zhu, Qingling Li, Lingjie Chen, Jianyong Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Dynamic multiobjective optimization problems (DMOPs) usually contain multiple conflicting objectives that change over time, which requires the optimization algorithms to quickly track the Pareto optimal front (POF) when the environment changes. In recent years, transfer learning (TL)-based methods have been considered promising in solving DMOPs. However, most existing TL-based methods are computationally extensive and therefore time-consuming. In this paper, a clustering based autoencoding for DMOEA, called CAE-DMOEA, is proposed, which aims to generate a high-quality initial population to accelerate the evolutionary process and improve the optimization performance. In particular, by learning the mapping relationship between the regional centroids of the approximate Pareto-optimal solutions (POS) from the previous two environments, the CAE-DMOEA can effectively predict the regional centroids of the POS in the new environment, which helps to tracking the moving POS and enhance the optimization efficiency. To study the performance of the proposed method, extensive experiments have been carried out by comparing three state-of-the-art DMOEAs. The experimental results show that the overall performance of the CAE-DMOEA is superior to that of the compared algorithms. © 2022 IEEE.

关键词： Evolutionary algorithms

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RoSe: Rotation-Invariant Sequence-Aware Consensus for Robust Correspondence Pruning 24

RoSe: Rotation-Invariant Sequence-Aware Consensus for Robust...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Yizhang Zhou, Weiwei Li, Yanping Zhao, Shengjie School of Software Engineering Tongji University Shanghai China College of Computer and Data Science Fuzhou University Fuzhou China Department of Computer Science and Technology Tongji University Shanghai China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai China

ISBN: (纸本)9798400706868

Correspondence pruning has recently drawn considerable attention as a crucial step in image matching. Existing methods typically achieve this by constructing neighborhoods for each feature point and imposing neighborhood consistency. However, the nearest-neighbor matching strategy often results in numerous many-to-one correspondences, thereby reducing the reliability of neighborhood information. Furthermore, the smoothness constraint fails in cases of large-scale rotations, leading to misjudgments. To address the above issues, this paper proposes a novel robust correspondence pruning method termed RoSe, which is based on rotation-invariant sequence-aware consensus. We formulate the correspondence pruning problem as a mathematical optimization problem and derive a closed-form solution. Specifically, we devise a rectified local neighborhood construction strategy that effectively enlarges the distribution between inliers and outliers. Meanwhile, to accommodate large-scale rotation, we propose a relative sequence-aware consistency as an alternative to existing smoothness constraints, which can better characterize the topological structure of inliers. Experimental results on image matching and registration tasks demonstrate the effectiveness of our method. Robustness analysis involving diverse feature descriptors and varying rotation degrees further showcases the efficacy of our method. © 2024 ACM.

关键词： correspondence pruning

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