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Informatica (Slovenia) 2025年第5期49卷 37-48页

作者： Remmach, Hassnae Razak, Siti Fatimah Abdul Ullah, Arif Yogarayan, Sumendra Sayeed, Md Shohel Mrhari, Amine Computer Systems Engineering Laboratory Cadi Ayyad University Marrakesh40000 Morocco Faculty of Information Science and Technology Multimedia University Ayer Keroh75450 Malaysia Research in Computer Science Laboratory Faculty of Sciences Ibn Tofail University Kenitra Morocco

In a number of industries, including computer graphics, robotics, and medical imaging, three-dimensional reconstruction is essential. In this research, a CNN-based Multi-output and Multi-Task Regressor with deep learning capabilities is proposed for three-dimensional object reconstruction from 3D point cloud. Our approach is grounded in the original Point Net architecture, which addresses the difficulties associated with convolution when applied to point clouds. Firstly, this paper is modified using a Multi-Output Regressor to accurately recreate Super forms from 3D point clouds. Using this method, we first extract features from the 3D point cloud using Point Net. After that, a Multi-Output Regressor receives these data and uses them to anticipate the Super shape parameters needed to reconstruct the shape. Taking in the data, the Multi-Output Regressor retrieved characteristics from Point Net and simultaneously predicts several outcomes. Second, a Multi-Task Regressor is used to modify the Point Net. The network gains from the capacity to transfer knowledge from one task to another, improving the model's overall performance. The model would forecast the ten parameters needed to create the shape in the case of rebuilding Super shapes. The test findings were better than expected;they are intriguing in terms of prediction accuracy and cost, and they update the result by 80%, which is a good accomplishment for the study. © 2025 Slovene Society Informatika. All rights reserved.

关键词： 3D reconstruction

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Text Augmentation-Based Model for Emotion Recognition Using Transformers

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computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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The Smart Development of Short Range Communication Protocol Development in Efficient Device Discovery 2

The Smart Development of Short Range Communication Protocol ...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Narasimhayya, B.E. Lanke, Ravikumar Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

The development of short-range communication protocols has been essential for efficient device discovery. Short-range communication allows two devices to communicate over short distances, typically up to 10 meters, using low-power radio frequencies. This type of communication is used extensively in applications such as Wi-Fi, Bluetooth, and Zigbee. Short-range communication protocols allow devices to discover each other without the need for a central server or intermediary. This is important for applications such as home automation, where it is necessary for various devices to be able to find each other and communicate. In order for efficient device discovery, short-range communication protocols must be reliable and secure. Reliable protocols are necessary to ensure that devices can find each other, while secure protocols are necessary to ensure that only authorized devices can access one another. Protocols such as Bluetooth, Wi-Fi, and Zigbee have been designed to be reliable and secure. The development of short-range communication protocols can be improved by using predictive algorithms. Predictive algorithms allow devices to automatically detect and respond to changes in the network environment. This enables devices to more efficiently discover each other and establish connections. © 2023 IEEE.

关键词： Zigbee

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Multi-Agent Dynamic Area Coverage Based on Reinforcement Learning with Connected Agents

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computer Systems Science & engineering 2023年第4期45卷 215-230页

作者： Fatih Aydemir Aydin Cetin STM Defence Technologies Engineering and Trade.Inc. Ankara06560Turkey Department of Computer Engineering Faculty of TechnologyGazi UniversityAnkara06500Turkey

Dynamic area coverage with small unmanned aerial vehicle(UAV)systems is one of the major research topics due to limited payloads and the difficulty of decentralized decision-making *** behavior of a group of UAVs in an unknown environment is another hard problem to be *** this paper,we propose a method for decentralized execution of multi-UAVs for dynamic area coverage *** proposed decentralized decision-making dynamic area coverage(DDMDAC)method utilizes reinforcement learning(RL)where each UAV is represented by an intelligent agent that learns policies to create collaborative behaviors in partially observable *** agents increase their global observations by gathering information about the environment by connecting with other *** connectivity provides a consensus for the decision-making process,while each agent takes *** each step,agents acquire all reachable agents’states,determine the optimum location for maximal area coverage and receive reward using the covered rate on the target area,*** method was tested in a multi-agent actor-critic simulation *** the study,it has been considered that each UAV has a certain communication distance as in real *** results show that UAVs with limited communication distance can act jointly in the target area and can successfully cover the area without guidance from the central command unit.

关键词： Dynamic environments multi-agent reinforcement learning dynamic area coverage

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Novel Anomaly Detection Scheme for Cyber Physical System using C-GAN in Fog assisted IoT Environment 2

Novel Anomaly Detection Scheme for Cyber Physical System usi...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Mathur, Ashwini Babu, S. Anantha Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

An Internet of Things (IoT) appears to be an innovative technology with great potential for widespread development. There has been a rise in data security issues in recent years as a consequence of various technological developments. Despite the proliferation of security precautions, criminals have learned to circumvent them. We employ a Contingently Generative Adversarial Network in our research to detect harmful online behaviour (CC-GAN). Our proposed system employs CC-GAN, which can identify novel threats, to carry out intrusion detection based on anomalies. An Internet-of-Things layer, a fog layer, as well as a cloud layer make up the whole of our proposed fog-based infrastructure. The IoT layer consists of many connected devices, and it is here that the CC-GAN receives the training data it needs to learn to recognise potential security breaches. A convolutional neural network & encoder are trained on the cloud layer, where additional computing takes place. Adding a fog layer might help decrease the delay. Finally, the attack detection score is evaluated. For this exercise, we use the network simulator version 2.6. (NS 3.26). The simulation result shows that the proposed system achieves a higher detection rate than the state-of-the-art systems. © 2023 IEEE.

关键词： Cyber Physical System

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Fake News Classification: Past, Current, and Future

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computers, Materials & Continua 2023年第11期77卷 2225-2249页

作者： Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science&Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbulTurkey

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

关键词： Supervised learning algorithms fake news classification online disinformation transformers recurrent neural network(RNN)disinformation transformers recurrent neural network(RNN)

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Study on optimization of multi-UAV nucleic acid sample delivery paths in large cities under the influence of epidemic environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7593-7620页

作者： Han, Yuhang Xiang, Hongyu Cao, Jianing Yang, Xiaohua Pan, Nan Huang, Linhai Mechanical Engineering Faculty of Civil Aviation and Aeronautics Kunming University of Science and Technology Yunnan China Computer Science and Technology Metrology Center Yunnan Power Grid Co. Ltd. Yunnan China Computer Science and Technology Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan China

In the context of global novel coronavirus infection, we studied the distribution problem of nucleic acid samples, which are medical supplies with high urgency. A multi-UAV delivery model of nucleic acid samples with time windows and a UAV (Unmanned Aerial Vehicle) dynamics model for multiple distribution centers is established by considering UAVs’ impact cost and trajectory cost. The Golden Eagle optimization algorithm (SGDCV-GEO) based on gradient optimization and Corsi variation is proposed to solve the model by introducing gradient optimization and Corsi variation strategy in the Golden Eagle optimization algorithm. Performance evaluation by optimizing test functions, Friedman and Nemenyi test compared with Golden Jackal Optimization (GJO), Hunter-Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA) and Golden Eagle Optimization (GEO), the convergence performance of SGDCV-GEO algorithm was demonstrated. Further, the improved RRT (Rapidly-exploring Random Trees) algorithm is used in the UAV path planning, and the pruning process and logistic chaotic mapping strategy are introduced in the path generation method. Finally, simulation experiments are conducted based on 8 hospitals and 50 randomly selected communities in the Pudong district of Shanghai, southern China. The experimental results show that the developed algorithm can effectively reduce the delivery cost and total delivery time compared with simulated annealing algorithm (SA), crow search algorithm (CSA), particle swarm algorithm (PSO), and taboo search algorithm (TS), and the developed algorithm has good uniformity, robustness, and high convergence accuracy, which can be effectively applied to the multi-UAV nucleic acid sample delivery path optimization in large cities under the influence of an epidemic environment. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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An Innovation Development of Decentralized Prove Piercing Data Recuperation Using Improved Red in Mobile Grid Computing 2

An Innovation Development of Decentralized Prove Piercing Da...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Karthikeyan, S. Thomas, Merin Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

The recent advancements in mobile computing have opened up the possibilities of decentralized data recovery in mobile grid computing. With the help of improved Red (Recovery of Erased Data) technique, data recovery can be done in a decentralized manner. This method is a combination of distributed computing, data replication, and data recovery algorithms which improves the data recovery process. The improved Red technique uses distributed computing to replicate the data and store it in multiple nodes in the grid. This helps to ensure that the data is distributed and available in multiple nodes for recovery. Data recovery is done by the Red algorithm which traverses the grid and identifies the nodes which have the data and then recovers it. The algorithm uses a technique called "greedy search"to identify the nodes which have the data. This helps to reduce the time and cost associated with the recovery process. The improved Red technique also makes use of data replication which helps to improve the data recovery process. The data is replicated and stored in multiple nodes which helps to improve the speed of data recovery. The replication process helps to ensure that the data is always available and can be recovered quickly. The improved Red technique also makes use of distributed computing to improve the scalability of the data recovery process. The distributed computing helps to ensure that the data recovery process can be done efficiently and quickly. The data recovery process can also be done on a large scale which helps to improve the speed of data recovery. © 2023 IEEE.

关键词： Recovery

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Mathematical Modelling of Three Phase Four Leg Converters in Quaternion Space 4

Mathematical Modelling of Three Phase Four Leg Converters in...

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4th International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2025

作者： Pongyart, Witthawas Vanichchanunt, Pisit Roengriang, Suppachai King Mongkut's University of Technology North Bangkok Electrical and Computer Engineering Faculty of Engineering Bangkok Thailand

ISBN: (纸本)9798331505400

Typically, AC-DC converters are modeled in two-dimensional space using the dq-coordinates, assuming they are supplied by a balanced three-phase system. However, this space vector model struggles to accurately represent converter behavior under unbalanced conditions. This paper presents the development of a mathematical model for a four-leg converter in quaternion space, offering a higher-dimensional model that captures converter behavior under both balanced and unbalanced input voltage conditions. To validate the proposed model, simulations were conducted, and the results confirm its accuracy. © 2025 IEEE.

关键词： Vector spaces

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