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International Conference on Advances in Information Technology (ICAIT)

作者： Pellakuri Vidyullatha Bhuvan Unhelkar Prasun Chakrabarti G S Pradeep Ghantasala Amjan Shaik Meghana Pyla Department Of Information Systems And Decision Sciences Muma College Of Business University of South Florida South Florida USA Department of CSE Koneru Lakshmaiah Educationn Foundation Vaddeswaram Guntur AP India Department of Computer Science and Engineering Sir Padampat Singhania University Udaipur Rajasthan India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering St.Peters Engineering College Maisammaguda TS India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Generative Adversarial Networks (GANs) have the ability to produce realistic images from random noise vectors and has attracted a lot of attention in recent years. The textual input is discontinuous and requires efficient semantic information capture, extending GANs to create images from text descriptions has special obstacles. In this research, a unique GAN-based text-to-image creation method that makes use of conditional GAN architectures and text embeddings. The generator neural network in our suggested model converts text embeddings into appropriate image representations, whereas the discriminator network separates real images from those produced by the generator. This paper shows the strategies like multi-modal fusion mechanisms and attention mechanisms in the network architecture to help textual descriptions and visual information match. This method uses the benchmark datasets and show that it can produce realistic and varied visuals that correspond with textual descriptions. Both qualitative and quantitative assessments demonstrate our model's capacity to grasp minute details and subtle semantic nuances found in the input text. In addition, we perform user studies to evaluate the fidelity and perceived quality of the generated images relative to ground truth images. Based on our experimental findings, it appears that the suggested framework for text-to-pixel image production provides both textual relevance and image quality. This paper illuminates how our methodology may be utilized in different settings, including virtual universes, PC supported plan, and the production of imaginative substance. We likewise accentuate future bearings and potential for profound learning and normal language handling procedures to further develop text- to-picture creating models' abilities.

关键词： Visualization Roads Semantics Noise Neural networks Text to image Production Generative adversarial networks Generators Vectors

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Near-Optimal Battery Swapping Algorithm in Dockless Electric Bike Sharing systems

Near-Optimal Battery Swapping Algorithm in Dockless Electric...

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IEEE International Conference on Communications (ICC)

作者： Chun-An Yang Shih-Chieh Chen Yi-Hsuan Peng Yu-Wen Chen Jian-Jhih Kuo Ming-Jer Tsai Dept. of Computer Science National Tsing Hua University Hsinchu Taiwan Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Computer Systems Technology New York City College of Technology Brooklyn New York USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Dockless electric bike (E-bike) sharing has become a new urban modality of green transportation to offer convenient services. Typically, the service provider arranges a truck starting from the depot to visit multiple parking locations to replace low-energy batteries. However, visiting many parking locations may cause a considerable tour cost. One efficient way is to aggregate low-energy E-bikes together. Some incentive mechanisms are thus adopted to encourage E-bike users to move their bikes to suitable parking locations, but leading to an incentive cost. The service provider would like to balance the tour cost of the truck and the incentive cost of E-bike users. To address this problem, the paper formulates an optimization problem and then proposes an approximation algorithm. The simulation results with the real dataset show that our algorithm outperforms the other baselines.

关键词： Costs Shared transport Simulation Aggregates Approximation algorithms Batteries Green transportation

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Motor Imagery Classification Enhancement using Generative Adversarial Networks for EEG Spectrum Image Generation

Motor Imagery Classification Enhancement using Generative Ad...

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Annual IEEE Symposium on computer-Based Medical systems

作者： Ahmed G. Habashi Ahmed M. Azab Seif Eldawlatly Gamal M. Aly Computer and Systems Engineering Department Ain Shams University Cairo Egypt Biomedical Engineering Department Technical Research Center Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt

The development of practical Brain-computer Interface (BCI) systems has been hindered by significant issues related to data, specifically the lack of sufficient data needed for training. To address this challenge, generating synthetic data that mimics real recorded data has been proposed to augment the real data. One promising technique for data augmentation is through the use of Generative Adversarial Networks (GANs), which have been successfully applied in many other fields. This paper proposes a novel GAN-based approach for generating synthetic spectrum images of Motor Imagery (MI) Electroencephalogram (EEG). The proposed GAN is examined with two Convolutional Neural Network (CNN) architectures in the context of MI classification. Using the public dataset BCI competition IV, our findings reveal that the generated EEG spectrum images using GANs exhibit temporal, spectral, and spatial characteristics similar to the real ones. The average classification accuracy of right-hand versus left-hand MI using the proposed GAN/CNN models has improved to 76.71% with an enhancement of 2.5% in comparison to using the CNN applied to the real data only. These results suggest that using GANs could improve MI BCI systems with limited data.

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A Cross-subject Transfer Learning Method for CSI-based Wireless Sensing

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IEEE Internet of Things Journal 2025年

作者： He, Zhengran Bouazizi, Mondher Gui, Guan Ohtsuki, Tomoaki Keio University School of Science for Open and Environmental Systems Kanagawa 223-0061 Japan Keio University Department of Information and Computer Science Yokohama Japan Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing210003 China

WiFi-based passive non-contact sensing is widely regarded as a leading technology in wireless sensing, owing to its extensive application scope and favorable growth outlook. Nevertheless, although current WiFi-based sensing techniques attain remarkable accuracy in identifying activities within particular scenarios, they need stronger generalization capabilities across different targets and environments, hindering further commercial development. To address this issue, this paper uses CNN, BLSTM, and Attention layers to propose a cross-subject transfer learning method based on the CNN-ABLSTM algorithm model. This method combines widely used transfer learning methods with deep neural network algorithms in cross-domain sensing. Specifically, this method leverages the performance advantages of the CNN-ABLSTM algorithm model in processing time-series data like channel state information (CSI) and utilizes transfer learning to fine-tune the pre-trained model from the source domain for application in the target domain with different subjects. This enables faster and more accurate achievement of cross-subject tasks. The simulated results show that the proposed new approach achieves higher recognition accuracy and shorter training times than traditional transfer learning methods for cross-subject tasks. In testing with the dataset used, it achieves up to around 85% performance of activity recognition accuracy in cross-subject tasks. © 2014 IEEE.

关键词： Contrastive Learning

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Coding Theorems for Repetition and Superposition Codes over Binary-Input Output-Symmetric Channels

arXiv

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arXiv 2024年

作者： Wang, Yixin Ma, Xiao The School of Systems Science and Engineering Sun Yat-sen University Guangzhou510006 China The Guangdong Key Laboratory of Information Security Technology School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

This paper is concerned with a class of low density generator matrix codes (LDGM), called repetition and superposition (RaS) codes, which have been proved to be capacity-achieving over binary-input output-symmetric (BIOS) channels in terms of bit-error rate (BER). We prove with a recently proposed framework that the RaS codes are also capacity-achieving over BIOS channels in terms of frame-error rate (FER). With this new framework, the theorem for the RaS codes can be generalized to source coding and joint source and channel coding (JSCC). In particular, we prove with this framework that the corresponding low-density parity-check (LDPC) codes, as an enlarged ensemble of quasi-cyclic LDPC (QC-LDPC) codes, can also achieve the capacity. To further improve the iterative decoding performance, we consider the convolutional RaS (Conv-RaS) code ensemble and prove it to be capacity-achieving over BIOS channels in terms of the first error event probability. The construction of Conv-RaS codes is flexible with rate (defined as the ratio of the input length to the encoding output length) ranging from less than one (typically for channel codes) to greater than one (typically for source codes), which can be implemented as a universal JSCC scheme, as confirmed by simulations. © 2024, CC BY.

关键词： Iterative decoding

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Energy-Efficient and Latency-Optimized Computation Offloading with Improved MOEA for Industrial Internet of Things

Energy-Efficient and Latency-Optimized Computation Offloadin...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jiahui Zhai Jing Bi Haitao Yuan Jinhong Yang Jia Zhang MengChu Zhou College of Computer Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions.

关键词： Energy consumption Quantum computing Multitasking Energy efficiency Internet Manufacturing Optimization Sorting Industrial Internet of Things Genetic algorithms

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Robotics in Healthcare: Artificial Intelligence-Driven Application for Medical Diagnosis and Treatment

Robotics in Healthcare: Artificial Intelligence-Driven Appli...

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Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： Dhanya D A. Devendran S. Balaji Vaishali M R. Sujatha K.V. Sreelekha Department of Artificial Intelligence and Data Science Mar Ephraem College of Engineering and Technology Kanyakumari Tamil Nadu India School of Business Woxsen University Hyderabad Telangana India Department of Information Technology Sri Manakula Vinayagar Engineering College Puducherry India Deportmet of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science SubbalakshmiLakshmipathy College of Science Madurai Tamil Nadu India Department of Computer Science and Engineering Hindusthan College of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The rapid development of deep learning methods presents a potentially game-changing opportunity in the realm of education, particularly in the promotion of student involvement and the comprehension of the subject matter. To exemplify learning gestures and resolve educational challenges, this exploratory study investigates the operation of deep learning algorithms to determine the interests of students. Our deep learning model can provide direct predictions on the areas of interest for individual students by analyzing enormous volumes of educational data. These data include pupil relations, performance standards, and behavioral patterns of students. By aligning instructional tactics with the preferences of students, this strategy not only makes it easier for students to become accustomed to newly presented educational material but also encourages active learning. The research reveals that deep learninghelps capture the intricacies of student engagement. It also provides preceptors with valuable insights that may be used to cultivate a learning landscape that is more engaging and investigative. The results of our research highlight the possibility that deep learning will be used to change educational procedures to make them more adaptable and sensitive to the various needs of students. In this paper, the practice of using deep learning for interest identification in education is discussed, along with its methodology, perpetrators, and counteraccusations

关键词： Deep learning Education Surgery Predictive models Robot sensing systems Prediction algorithms Real-time systems Pupils Medical diagnostic imaging Standards

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SHAP-Guided GWO and ABC Feature Selection for Early Diabetes Prediction with XGBoost 2

SHAP-Guided GWO and ABC Feature Selection for Early Diabetes...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Pavan Kumar, V.S. Koteswara Rao Yarlagadda, S. Pilli, Neelima Koti Mani Kumar Tirumanadham, N.S. Muniyandy, Elangovan Ruth, Gulli Hyderabad India Sir C R Reddy College of Engineering Department of IT Eluru India S R K R Engineering College Department of CSE Bhimavaram India Sir C R Reddy College of Engineering Department of CSE Eluru India Saveetha Institute of Medical and Technical Sciences Department of Biosciences Chennai India B V Raju Institute of Technology Department of Computer Science and Business Systems Medak India

ISBN: (纸本)9798350371314

The study examines the relationship of autonomous motivation with physical activity in type 2 diabetes mellitus (T2DM) patients after participation in a fitness intervention trial. The study also longitudinally explores the various factors regarding motivation in the newly diagnosed population, which gives useful insight into dietary self-care and treatment of diabetes. This pointed towards research gaps that are existing and hence the further need of conducting research for gender-specific inequities, management of cardio metabolic risk, provision of autonomy support, psychological issues, and foot health among diabetic patients. This work hereby brings in feature selection with interpretable modelling techniques in diabetes prediction using optimization algorithms such as Grey Wolf Optimization (GWO) and Artificial Bee Colony (ABC), together with SHAP-XGBoost. Concerning the comparison between GWO and SHAP-XGBoost and ABC with SHAP-XGBoost, it can be said that the latter is the best performer. Concerning the comparison of GWO and SHAP-XGBoost and ABC with SHAP-XGBoost, the latter is said to exhibit the best with 82.26% of accuracy. The outcomes of this research can be very useful for improvement in diabetic therapies, and in particular, for tailored treatment options and overall outcomes for the patients. © 2024 IEEE.

关键词： Optimization

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Bit Rate Reduction in Cloud Gaming Using Object Detection Technique

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computers, Materials & Continua 2021年第9期68卷 3653-3669页

作者： Daniyal Baig Tahir Alyas Muhammad Hamid Muhammad Saleem Saadia Malik Nadia Tabassum Natash Ali Mian Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Statistics and Computer Science University of Veterinary and Animal SciencesLahore54000Pakistan Department of Industrial Engineering Faculty of EngineeringRabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology-RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Virtual University of PakistanLahore54000Pakistan School of Computer and Information Technology Beaconhouse National University Lahore53700Pakistan

The past two decades witnessed a broad-increase in web technology and on-line *** the broadband confinements is viewed as one of the most significant variables that prompted new gaming *** immense utilization of web applications and games additionally prompted growth in the handled devices and moving the limited gaming experience from user devices to online cloud *** internet capabilities are enhanced new ways of gaming are being used to improve the gaming *** cloud-based video gaming,game engines are hosted in cloud gaming data centers,and compressed gaming scenes are rendered to the players over the internet with updated *** such systems,the task of transferring games and video compression imposes huge computational complexity is required on cloud *** basic problems in cloud gaming in particular are high encoding time,latency,and low frame rates which require a new methodology for a better *** improve the bandwidth issue in cloud games,the compression of video sequences requires an alternative mechanism to improve gaming adaption without input *** this paper,the proposed improved methodology is used for automatic unnecessary scene detection,scene removing and bit rate reduction using an adaptive algorithm for object detection in a game *** a result,simulations showed without much impact on the players’quality experience,the selective object encoding method and object adaption technique decrease the network latency issue,reduce the game streaming bitrate at a remarkable scale on different *** proposed algorithm was evaluated for three video game *** this paper,achieved 14.6%decrease in encoding and 45.6%decrease in bit rate for the first video game scene.

关键词： Video encoding object detection bit rate reduction game video motion estimation computational complexity

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CapsEnhancer: An Effective Computational Framework for Identifying Enhancers Based on Chaos Game Representation and Capsule Network

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Journal of Chemical Information and Modeling 2024年第14期64卷 5725-5736页

作者： Yao, Lantian Xie, Peilin Guan, Jiahui Chung, Chia-Ru Huang, Yixian Pang, Yuxuan Wu, Huacong Chiang, Ying-Chih Lee, Tzong-Yi Kobilka Institute of Innovative Drug Discovery School of Medicine The Chinese University of HongKong Shenzhen518172 China School of Science and Engineering The Chinese University of HongKong Shenzhen518172 China School of Medicine The Chinese University of HongKong Shenzhen518172 China Department of Computer Science and Information Engineering National Central University Taoyuan320317 Taiwan Division of Health Medical Intelligence Human Genome Center The University of Tokyo Tokyo108-8639 Japan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

Enhancers are a class of noncoding DNA, serving as crucial regulatory elements in governing gene expression by binding to transcription factors. The identification of enhancers holds paramount importance in the field of biology. However, traditional experimental methods for enhancer identification demand substantial human and material resources. Consequently, there is a growing interest in employing computational methods for enhancer prediction. In this study, we propose a two-stage framework based on deep learning, termed CapsEnhancer, for the identification of enhancers and their strengths. CapsEnhancer utilizes chaos game representation to encode DNA sequences into unique images and employs a capsule network to extract local and global features from sequence "images". Experimental results demonstrate that CapsEnhancer achieves state-of-the-art performance in both stages. In the first and second stages, the accuracy surpasses the previous best methods by 8 and 3.5%, reaching accuracies of 94.5 and 95%, respectively. Notably, this study represents the pioneering application of computer vision methods to enhancer identification tasks. Our work not only contributes novel insights to enhancer identification but also provides a fresh perspective for other biological sequence analysis tasks. © 2024 The Authors. Published by American Chemical Society.

关键词： DNA sequences

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