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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Bedane, Tarikwa Tesfa Kebede, Kidistie Bizuneh Mohapatra, Sudhir Kumar Das, Tapan Kumar Tripathy, Asis Kumar Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Agricultural Transformation Institute Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819735228

Given maize’s significant role as a staple crop, it becomes imperative to carry out precise crop yield prediction to ensure food security. This research employs machine learning algorithms to analyze historical data related to maize crop yields, with the aim of developing a predictive model. The outcome of this study can serve as a valuable tool for pre-harvest planning and measuring crop production per unit of land area. The dataset utilized in this research spans from 2010 to 2021 and was sourced from Ethiopia’s Central Statistical Agency. Three machine learning regression algorithms—Logistic Regression, XGBoost, and Random Forest—were employed to design the predictive model. After data preparation and preprocessing, a total of 436,358 records were used for model development. The models were further trained with reduced features using principal component analysis (PCA). The experimental results demonstrate that the Random Forest algorithm, trained with a reduced number of features through PCA, yielded superior performance, with a lower error rate and a high R2 score of 0.98. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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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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Enhancing the Evaluation Performance of Convolutional Neural Networks-Based Vehicle Classification Systems

Enhancing the Evaluation Performance of Convolutional Neural...

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Michel Precieux Kiyindou Samuel Enobong Sunday Zhou Hong Faculty of Electronic Information Engineering Huaiyin Institute of technology Huai’an City China Foreign Expeert Workshop Huai’an (Faculty of Computer Engineering) Huaiyin Institute of technology Huai’an City China

The rapid advancement of artificial intelligence, particularly deep learning, has spurred its integration into diverse domains. Among these, the application of deep learning in vehicle classification stands out as a vital focus within intelligent transportation systems, road traffic planning, safety warning, and driverless driving. In this research, a comprehensive approach of CNN’s evolution such as LeNet, AlexNet, and other CNN-based vehicle classification techniques are enhanced for a better performance, while also investigating R-CNN, YOLO, and related methods for vehicle detection. Evaluative datasets for these methods are highlighted. Moreover, the enhancement of the computing power of the Graphics Processing Unit (GPU) and the sharp increase in the amount of data, deep learning based on convolutional neural networks has become a research hotspot. To evaluate the accuracy and efficiency of our vehicle detection methods, RCNN and YOLO series datasets were used to evaluate these methods and an the development of CNN as the main line was introduced and use to analyze the vehicle classification methods based on LeNet, AlexNet and other CNNs. This research is pivotal for vehicle tracking, traffic management, and autonomous driving progress. In this landscape, convolutional neural networks (CNNs), self-encoder neural networks, and deep belief networks take prominence, with CNNs evolving as the most dynamic. Notably, CNN’s development has led to refined vehicle classification through models like LeNet, AlexNet, VGG, and GoogLeNet. The paper concludes by outlining prospective research directions in this domain.

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Research on Image Recognition technology and Application Based on Neural Network 2nd

Research on Image Recognition Technology and Application Bas...

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2nd International Conference on Artificial Intelligence and Communication Technologies, ICAICT 2024

作者： Huang, Changquan Chen, Yikun Liu, Huanyu Wang, Sheng Li, Chongru Liu, Henan School of College of Artificial Intelligence Yango University Fujian China Department of Economic and Management Zhan Jiang Preschool Education College Guangdong China School of Computer and Software Engineering Huaiyin Institute of Technology Jiangsu China University of Aircraft ordos Institute of Technology Inner Mongolia Ordos China Hongde college buct Beijing University of Chemical Technology Beijing China Harbin University of Science and Technology Heilongjiang China

ISBN: (纸本)9789819600915

The aim of this study is to explore neural network-based image recognition techniques and their applicability and performance in different application scenarios. With the rapid development of artificial intelligence technology, image recognition technology is more and more widely used in various fields, especially the introduction of convolutional neural network (CNN), which makes image recognition technology show excellent performance in processing complex data. Traditional machine learning algorithms often show low recognition accuracy and generalisation ability when dealing with complex image data due to their shallow structure. Deep learning, on the other hand, has made significant progress in image recognition tasks by constructing a multi-layered neural network structure that can better extract and characterise complex features in images. In the study of this paper, an improved convolutional neural network model is proposed, which consists of a convolutional layer, a filter bank, a residual learning module, and a classifier. In order to validate the effectiveness of the model, three datasets (D1, D2, and D3) with different sizes and features are used for experiments in this paper. The experimental results show that the proposed CNN model exhibits high recognition accuracy when dealing with different datasets, especially in the classification task of specific targets and realistic scenes, the model is able to extract and recognise key features effectively, showing good convergence and stability. The experimental results not only demonstrate the excellent performance of the deep learning-based target recognition algorithm on different datasets, but also reflect the stability and gradual optimisation ability of the model during the training process. Although a slight overfitting phenomenon occurs on the validation set, these problems can be effectively mitigated and improved by means of appropriate regularisation and hyperparameter tuning. The research in this pa

关键词： Convolutional neural networks

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Optimization-Based Comparative System Evaluation of Single and Dual Traction Inverters with Focus on Partial Load Efficiency and Chip Area

Optimization-Based Comparative System Evaluation of Single a...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Sachs, Christoph Stamer, Fabian Allgeier, Jan Thrimawithana, Duleepa Neuburger, Martin Esslingen University Faculty of Mobility and Technology Germany University of Stuttgart Institute for System Dynamics Germany Robert Bosch GmbH Germany The University of Auckland Dept. of Electrical Computer and Software Engineering New Zealand

ISBN: (纸本)9798350376067

The transition to electric transportation demands efficient and cost-effective powertrains. Optimizing energy use is crucial for extending range and reducing expenses. However, comparing inverter and motor efficiency based on inverter topologies is challenging due to biased methodologies that favor certain designs over others. This document introduces a novel optimization-based approach for enhancing partial load efficiency and minimizing chip area of single and dual traction inverters, indicating potential energy savings and cost reduction. Recent publications of both industry [1], [2] and academia [3] underscore the importance of these design goals achieved by either novel inverter topologies or enhanced control methods. Two promising topologies with the inherent capability of partial load optimization are evaluated regarding chip area and system efficiency to find the most suitable concept for future electric vehicle power trains. © 2024 IEEE.

关键词： Energy utilization

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A Method for Depth Camera Calibration Based on Motion Capture System

A Method for Depth Camera Calibration Based on Motion Captur...

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Jianchu Lin Ming Zhao Guojun Yin Haiping Zhou Toshboev Hudoyberdi Bo Jiang Faculty of Computer and Software Engineering Huaiyin Institue of Technology Huai’an China Faculty of Art and Design Huaiyin Institue of Technology Huai’an China Jiangsu Eazytec Co. LTD Yixin China

With the rapid development of 3D computer vision technology, depth cameras have been widely used. Depth camera calibration is mainly constrained by two aspects: (1) alignment of depth data, and (2) distortion in RGB images. Under the condition of unknown camera intrinsic parameters, a method for RGBD commercial camera calibration is studied based on an optical motion capture system. The proposed approach utilizes the projection from the motion capture space to the depth camera’s field of view, establishing a coupled parameter model for the camera’s CCD photosensitive elements to improve the calibration of the depth camera. Experimental results demonstrate a 33% reduction in errors in the central region of the camera’s field of view compared to commonly Least Squares (LS) calibration methods. Moreover, the method exhibits faster computation speed, enabling efficient real-time calibration of depth cameras with unknown parameters in practice.

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A Review of Algorithms's Complexities on Different Valued Sorted and Unsorted Data

A Review of Algorithms's Complexities on Different Valued So...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Majeed, Asad Iftikhar, Mahnoor Ali, Raja Hashim Arshad, Usama Shabbir, Muhammad Zeeshan Zeeshan Ijaz, Ali Ali, Nisar Aftab, Ali Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Sorting and searching are critical processes for effecttive data analysis. In this paper, we evaluate the performance of various sorting and searching algorithms and compare their time and space complexities on both sorted and unsorted data. The algorithms we analyzed include six common sorting algorithms (insertion, radix, bucket, merge, bubble, and quick sort) and three search algorithms (linear, binary, and jump search). The results of our study provide insights into the best algorithms to use for different input sizes and types of data. It was found that for small input sizes, all algorithms perform similarly, but for larger input sizes, insertion and radix sorts are better for time complexity while bubble sort is better for space complexity. Additionally, jump search outperformed linear and binary search algorithms in both time and space complexity. Besides, difference between time and space complexity of sorted and unsorted data was significant. © 2023 IEEE.

关键词： Computational complexity

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A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1476-1489页

作者： Shoufei Han Kun Zhu MengChu Zhou Xiaojing Liu Haoyue Liu Yusuf Al-Turki Abdullah Abusorrah College of Computer Science and Technology Nanjing University of Aeronautics and Astronauticsand also with the Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjing 211106China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand also with the K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing *** goal is to find a Pareto front and as many equivalent Pareto optimal solutions as *** some evolutionary algorithms for them have been proposed,they mainly focus on the convergence rate in the decision space while ignoring solutions *** this paper,we propose a new multiobjective fireworks algorithm for them,which is able to balance exploitation and exploration in the decision *** first extend a latest single-objective fireworks algorithm to handle *** we make improvements by incorporating an adaptive strategy and special archive guidance into it,where special archives are established for each firework,and two strategies(i.e.,explosion and random strategies)are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special ***,we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization *** results show that the proposed algorithm is superior to compared algorithms in solving ***,its runtime is less than its peers'.

关键词： Adaptive strategy fireworks algorithm multimodal multiobjective optimization problems(MMOP)

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Mobile Medical Assistant System for Laboratory Report Analysis and Medical Drug Identification 4

Mobile Medical Assistant System for Laboratory Report Analys...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Warnakulasuriya, Deshani Dewangi, Tharushi Sewwandi, Navodya Rathnayake, Minoli Kodagoda, Nuwan Suriyawansha, Kushanra Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

It is quite common for medical drugs and prescriptions to be misidentified by hospitals and after drugs are being dispensed to the patients. Misidentification of medical drugs is more common among elderly and visually impaired patients. In hospital organizations, the leading medical error is adverse drug events. Another most common issue patients face is keeping track of medical lab reports. Our proposed mobile medical assistant system uses image processing to identify drugs with or without packaging, identifying prescription and medical lab reports. Furthermore, the mobile application will identify the trends of medical lab reports and predict next month's results of the medical lab report of the patient using machine learning. © 2022 IEEE.

关键词： Hospitals

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Emergency Vehicle Navigation in Connected Autonomous Systems Using Enhanced Traffic Management System

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IEEE Internet of Things Journal 2025年第12期12卷 20670-20677页

作者： Raja, Gunasekaran Anbalagan, Sudha Gurumoorthy, Sugeerthi Jegathesan, Darshini Subramanian Girivel, Niveditha Mani Shanmuga Sundaram, Varsha Ali Khowaja, Sunder Dev, Kapal Anna University NGNLab Department of Computer Technology Chennai600044 India Vellore Institute of Technology Chennai Centre for Smart Grid Technologies School of Computer Science and Engineering Chennai600127 India Software Development Citicorp Services Private Ltd. Chennai600113 India Dublin City University School of Computing Faculty of Engineering and Computing Dublin 9 D09 V209 Ireland Munster Technological University Department of Computer Science Cork 021 T12 P928 Ireland University of Johannesburg Department of Institute of Intelligent Systems Auckland Park2006 South Africa Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Rajpura140401 India

Autonomous vehicle (AV) usage has become predominant in the rapidly evolving landscape of urban transportation. Integrating AVs and non-AVs in the existing traffic infrastructure has significantly increased the complexity of traffic patterns. This research work primes the enhanced Traffic Management System (e-TMS), a solution implemented to expedite emergency vehicle (EV) travel in the context of connected AVs (CAVs) and ensured secured communication employing public key infrastructure (PKI) among the Internet of Vehicles (IoV) framework. When an EV is detected, the IoV system verifies the EV’s signal using PKI, ensuring its authenticity and integrity by encrypting the communication between the EV and road side units (RSUs). Further, the e-TMS activates the platooning process where CAVs in the EV’s lane shift to the adjacent lanes and dynamically form platoons to create a dedicated lane for the EV. To further optimize the platooning process, an adaptive smart leader selection (ASLS) algorithm is employed to select a leader vehicle spontaneously among the AVs based on the proximity to the EV, communication reliability, and lane position. Radio Detection and Ranging devices aid this platooning process by providing distance and target velocity, which are needed to maintain the required distance between the AVs and ensure safe platoon formation. The e-TMS enhances EV response times and overall traffic flow efficiency and resulted in 27.8% increase in mean speed compared to the traditional methods. © 2014 IEEE.

关键词： Emergency vehicles

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