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International Journal of Interactive Mobile Technologies 2024年第1期18卷 59-74页

作者： Sowmya, B.J. Meeradevi Seema, S. Dayananda, P. Supreeth, S. Shruthi, G. Rohith, S. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Karnataka Bengaluru India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering M S Ramaiah Institute of Technology Karnataka Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India School of Computer Science and Engineering REVA University Karnataka Bengaluru India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Karnataka Bengaluru India

Vision Studio aims to utilize a diverse range of modern deep learning and computer vision principles and techniques to provide a broad array of functionalities in image and video processing. Deep learning is a distinct class of machine learning algorithms that utilize multiple layers to gradually extract more advanced features from raw input. This is beneficial when using a matrix as input for pixels in a photo or frames in a video. computer vision is a field of artificial intelligence that teaches computers to interpret and comprehend the visual domain. The main functions implemented include deepfake creation, digital ageing (de-ageing), image animation, and deepfake detection. Deepfake creation allows users to utilize deep learning methods, particularly autoencoders, to overlay source images onto a target video. This creates a video of the source person imitating or saying things that the target person does. Digital aging utilizes generative adversarial networks (GANs) to digitally simulate the aging process of an individual. Image animation utilizes first-order motion models to create highly realistic animations from a source image and driving video. Deepfake detection is achieved by using advanced and highly efficient convolutional neural networks (CNNs), primarily employing the EfficientNet family of models. © 2024 by the authors of this article. Published under CC-BY.

关键词： Animation

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The Campus Localization System Based on LoRa 5

The Campus Localization System Based on LoRa

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5th International Conference on computer Communication and the Internet, ICCCI 2023

作者： Wei, Ching-Chuan Tsai, Si-Kai Chang, Chia-Chi Chaoyang University of Technology Department of Information and Communication Engineering Taichung City Taiwan National Changhua University of Education Department of Computer Science and Information Engineering Taichung City Taiwan

ISBN: (纸本)9798350326956

Nowadays, loT applications are thriving, and LoRa (Long Range) technology plays an important role due to its characteristics of energy efficiency and wide-area transmission. This paper aims to use LoRa transmission for locating the target in the campus. The campus of Chaoyang University of technology was chosen as the experimental area, where was divided into nine small areas. The target localization system in this study means to identify in which small outdoor area the target is now. The monitored target with LoRa transmitter sends signals when stayed in the campus of Chaoyang University of technology. Several receiving nodes with LoRa receivers were deployed in different places in the campus. Various deployment places of LoRa receivers seriously affect the RSSI (Received Signal Strength Indicator) due to the severely fluctuating building environments in the campus. About five different receiving patterns were used to locate the target for comparison in the study. Due to the distribution of towering buildings and the influence of terrain within the campus, the RSSI of the received signals may encounter problems such as loss or multipath, making it impossible to use the traditional distance calculation methods for positioning. Therefore, LR (Logistic Regression), KNN (K-Nearest Neighbors), SVM (Support Vector Machine) and BPNN (Backpropagation Neural Network) were used for the location estimation. Experimental results show that by utilizing the BPNN method under some specific pattern, the accuracy in identifying the location of the target can reach up to 98.1%. It indicates that the receiving pattern and training method have significant influences on campus positioning using LoRa. © 2023 IEEE.

关键词： Location

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STARS Assisted Semi-Grant-Free NOMA Communications

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4904-4919页

作者： Xie, Jin Yue, Xinwei Zhao, Jingjing Zou, Yixuan Liu, Yuanwei Liu, Rongke Ding, Zhiguo Key Laboratory of Information and Communication Systems China Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100101 China Beihang University School of Electronic and Information Engineering Beijing100191 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates

This paper investigates the performance of simultaneously transmitting and reflecting surface (STARS) assisted semi-grant-free non-orthogonal multiple access network with randomly distributed users. By deploying STARS, the transmit signals of grant-based user (GBU) and grant-free users (GFUs) can be exquisitely adjusted to reduce interference. We propose a maximum channel scheduling (MCS) protocol that allows a GFU to access GBU’s channel with the assistance of STARS. In particular, the impacts of perfect/imperfect successive interference cancellation (pSIC/ipSIC) on MCS protocol are taken into account. To characterize the performance of STARS aided MCS (STARS-MCS) network, we derive the expressions of outage probability for GBU and GFU with pSIC/ipSIC. By applying convolution theorem and Laplace transform, the asymptotic expressions of outage probability and diversity orders for GBU and GFU are attained. We further design a STARS-based power control (SPC) strategy to eliminate the outage probability error floor and improve the outage performance. Numerical results show that: 1) The performance of STARS-MCS outperforms the existing benchmarks in terms of outage probability and system throughput;2) The SPC strategy can effectively improve the performance of the STARS-MCS network and eliminate the outage probability error floor at high signal-to-noise ratios;and 3) By adjusting reflection and transmission coefficients of STARS, the outage performance of GBU and GFU can be greatly enhanced. © 2025 IEEE. All rights reserved.

关键词： Laplace transforms

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A Coarse to Fine Detection Method for Prohibited Object in X-ray Images Based on Progressive Transformer Decoder 24

A Coarse to Fine Detection Method for Prohibited Object in X...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Ma, Chunjie Du, Lina Gao, Zan Zhuo, Li Wang, Meng Shandong Jinan China School of Computer Science and Technology Shandong Jianzhu University Shandong Jinan China Faculty of Information Technology Beijing University Of Technology Beijing China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9798400706868

Currently, Transformer-based prohibited object detection methods in X-ray images appear constantly, but there are still some shortcomings such as poor performance and high computational complexity for prohibited object detection with heavily occlusion. Therefore, a coarse to fine detection method for prohibited object in X-ray images based on progressive Transformer decoder is proposed in this paper. Firstly, a coarse to fine framework is proposed, which includes two stages: coarse detection and fine detection. Through adaptive inference in stages, the computational efficiency of the model is effectively improved. Then, a position and class object queries method is proposed, which improves the convergence speed and detection accuracy of the model by fusing the position and class information of prohibited object with object queries. Finally, a progressive Transformer decoder is proposed, which distinguishes high and low score queries by decreasing confidence thresholds, so that high-score queries are not affected by low-score queries in the decoding stage, and the model can focus more on decoding low-score queries, which usually correspond to prohibited object with severe occlusion. The experimental results on three public benchmark datasets (SIXray, OPIXray, HiXray) demonstrate that compared with the baseline DETR, the proposed method achieves the state-of-the-art detection accuracy with a 21.6% reduction in model computational complexity. Especially for prohibited objects with heavily occlusion, accurate detection can be carried out. © 2024 ACM.

关键词： Decoding

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Fortifying Smart Grids: A Holistic Assessment Strategy against Cyber Attacks and Physical Threats for Intelligent Electronic Devices

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computers, Materials & Continua 2024年第8期80卷 2579-2609页

作者： Yangrong Chen June Li Yu Xia Ruiwen Zhang Lingling Li Xiaoyu Li Lin Ge School of Computer Science Zhengzhou University of AeronauticsZhengzhou450046China Henan Xin’an Century Technology Co. Ltd.Henan Data Security Engineering Technology Research CenterZhengzhou450046China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan430072China

Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control *** the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication *** grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for ***,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for ***,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security ***,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal *** index system contains 18 indicators in 3 ***,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and *** higher the level,the greater the security ***,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by *** results show that calculated security risk level based on the proposed assessment method are consistent with actual ***,the reasonableness and effectiveness of the proposed index system and assessment method are validated.

关键词： Smart grid intelligent electronic device security assessment abnormal behaviors network traffic running states

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Unlocking the Power of Natural Language Processing to Automate Knowledge Discovery 3

Unlocking the Power of Natural Language Processing to Automa...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Ravikumar, V. Kodmelwar, Manohar K. Adlin Jebakumari, S. Shekhawat, Jyoti Sakhare, Nitin N. Vijayalakshmi, V.J. Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798350319125

natural language processing (NLP) is an increasing number of powerful tools for knowledge discovery, permitting companies to automate techniques like textual content analysis and sentiment analysis that used to require big manual effort. NLP helps the automation of tasks inclusive of information person intent, comprehending textual content, extracting records, producing summaries, and classifying documents. With NLP, those duties can be achieved in a fraction of the time with some distance extra accuracy and without the want for manual processing. By way of leveraging present technology, companies can liberate the energy of NLP to automate knowledge discovery and substantially improve their aggressive advantage. © 2023 IEEE.

关键词： automation information substantially summaries techniques

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CEMA-LSTM:Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets

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computer Modeling in engineering & Sciences 2023年第4期135卷 45-64页

作者： Zhiyun Yang Qi Liu HaoWu Xiaodong Liu Yonghong Zhang School of Computer and Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK School of Automation Nanjing University of Information Science TechnologyNanjing210044China

Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy *** relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation,where radar echo maps were used to predict their consequent moment,so as to recognize potential severe convective weather ***,these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation,due to the size limitation of convolution filter,lack of global feature,and less attention to features from previous *** address the problems,this paper proposes a CEMA-LSTM recurrent unit,which is embedded with a Contextual Feature Correlation Enhancement Block(CEB)and a Multi-Attention Mechanism Block(MAB).The CEB enhances contextual feature correlation and supports its model to memorize significant features for near-future prediction;the MAB uses a position and channel attention mechanism to capture global features of radar *** practical radar echo datasets were used involving the FREM and CIKM 2017 *** quantification and visualization of comparative experimental results have demonstrated outperformance of the proposed CEMA-LSTMover recentmodels,e.g.,PhyDNet,MIM and PredRNN++,*** particular,compared with the second-rankedmodel,its average POD,FAR and CSI have been improved by 3.87%,1.65%and 1.79%,respectively on the FREM,and by 1.42%,5.60%and 3.16%,respectively on the CIKM 2017.

关键词： Radar echo extrapolation attention mechanism long short-term memory deep learning

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Kidney Tumor Detection Using MLflow, DVC and Deep Learning 2

Kidney Tumor Detection Using MLflow, DVC and Deep Learning

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2nd IEEE International Conference on Advances in information technology, ICAIT 2024

作者： Usha, M.G. Shreya, M.S. Supreeth, S. Shruthi, G. Pruthviraja, Dayananda Chavan, Pundalik School of Computer Science Engineering REVA University Bangalore India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9798350383867

Kidney tumors are a global health concern, necessitating precise detection for effective treatment and better patient outcomes. This paper presents a novel approach that combines deep learning with Data Version Control (DVC) and MLflow frameworks to revolutionize kidney cancer detection. Deep learning, a subset of artificial intelligence, shows great promise in interpreting complex patterns in medical imaging data. Utilizing convolutional neural networks (CNNs), our model analyzes radiographic images to identify subtle indicators of renal malignancies with unprecedented accuracy and efficiency. Incorporating DVC ensures seamless management of large imaging datasets, promoting collaboration and reproducibility across research endeavors. Additionally, MLflow streamlines the experimentation process, enabling systematic evaluation of model performance metrics and hyperparameters. Through meticulous logging and visualization of experimentation results, our framework facilitates informed decision-making, leading to the selection of optimal models for kidney cancer detection. This comprehensive approach signifies a significant advancement in oncological diagnostics, offering a holistic solution to the challenges posed by kidney cancer. By merging deep learning with DVC and MLflow, our methodology heralds a transformative paradigm shift in cancer detection, poised to enhance clinical outcomes and elevate patient care globally. ©2024 IEEE.

关键词： Convolutional neural networks

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FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring

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Journal of computer Science & technology 2023年第5期38卷 970-984页

作者：胡春雨忽丽莎袁林陆佃杰吕蕾陈益强 Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 25000China Institute of Information Technology Hebei University of Economics and BusinessShijiazhuang 050061China School of Information Science and Engineering Shandong Normal UniversityJinan 250358China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power ***,most wearable health data is distributed across dfferent organizations,such as hospitals,research institutes,and companies,and can only be accessed by the owners of the data in compliance with data privacy *** first challenge addressed in this paper is communicating in a privacy-preserving manner among different *** second technical challenge is handling the dynamic expansion of the federation without model *** address the first challenge,we propose a horizontal federated learning method called Federated Extremely Random Forest(FedERF).Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model *** on FedERF,we present a federated incremental learning method called Federated Incremental Extremely Random Forest(FedIERF)to address the second technical *** introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model *** experiments show that FedERF achieves comparable performance with non-federated methods,and FedIERF effectively addresses the dynamic expansion of the *** opens up opportunities for cooperation between different organizations in wearable health monitoring.

关键词： federated learning incremental learning random forest wearable health monitoring

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Fruit Quality Detection using Convolutional Neural Network-based Cosine Annealing Learning Rate

Fruit Quality Detection using Convolutional Neural Network-b...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Desai, Swapnil Rajendra Joshi, Manuj Mane, Pradip Suresh Computer Engineering Pacific Academy of Higher Education and Research Udaipur India Information Technology Vasantdada Patil College of Engg. and Visual Arts Mumbai India

ISBN: (纸本)9798350375442

Fruits are essential foods consumed in day-to-day lives of most people and are primarily recommended as a source of nutrient supply by experts and nutritionists. computer vision technologies employ numerous approaches for fruit detection. However, detecting fruit quality remains difficult because of the variations in size, ripeness, and surface defects which result in them looking subtly similar. So, this research proposes a Convolutional Neural Network-based Cosine Annealing Learning Rate (CNN-CALR) to detect fruit quality effectively. CNN-CALR adapts LR dynamically, enhancing the convergence by minimizing it with a cosine curve. This increases the model's training stability and results in better performance. Initially, the Fruit 360 dataset is applied to analyze the model's performance. Data augmentation is applied in the preprocessing stage to increase the dataset size. Then, a ResNet50 is employed to extract the features while CNN-CALR is employed for the detection of fruit quality effectively. The proposed CNN-CALR achieves a high accuracy of 99.92% when compared to the existing methods, Cascaded Adaptive Network-based Fuzzy Inference System (Cascaded-ANFIS) and DenseNet-201. © 2024 IEEE.

关键词： Convolutional neural networks

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