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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Meng, Xiangmao Wang, Keming Xiang, Ju Wang, Wenkang School of Computer Science Xiangtan University Xiangtan China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9798350386226

Detecting protein complexes holds paramount importance in elucidating cellular organization and protein functionalities. Over the past decade, numerous approaches have centered their attention on the topological intricacies of protein-protein interaction (PPI) networks, yet these have often fallen short in harnessing the full spectrum of biological information inherent in proteins. To bridge this gap, we introduce a novel methodology, designated NE-DPC, which integrates both the topological landscape of PPI networks and the attribute profiles of proteins to facilitate the identification of protein complexes. Initially, we fuse the static PPI network with protein attributes through advanced network embedding techniques. Subsequently, we construct a cosine similarity matrix grounded on these embedded vectors, capturing the intricate relationships among proteins. Lastly, we employ a core-attachment strategy to pinpoint protein complexes. The results reveal that NE-DPC outperforms cutting-edge methods, demonstrating its effectiveness and potential in advancing protein complex detection. © 2024 IEEE.

关键词： attribute information network embedding protein complex protein-protein interaction network

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AR Qaida: AR Assisted Multi-level Interactive Framework for Smart Learning of Early to Primary School Children 17

AR Qaida: AR Assisted Multi-level Interactive Framework for ...

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17th International Conference on Open Source Systems and Technologies, ICOSST 2023

作者： Naz, Zubaira Azam, Ayesha Khan, Muhammad Usman Ghani Al-Khwarizmi Institute of Computer Science University of Engineering and Technology Lahore Pakistan University of Engineering and Technology Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350381320

Integration of technology into education improves teaching and learning. Augmented Reality (AR) technology has received a lot of attention due to its vast potential for a variety of applications. Many subjects can be difficult to convey in a classroom or comprehend from a 2D textbook for school-Aged children. This is because school-Aged children need a lot of attention to become more enthusiastic and satisfied in their classes. AR applications with educational elements provide an exciting and unique learning experience. This is accomplished by blurring the boundaries between the real world they are in and the virtual world they perceive on the screen. This paper proposes an AR-based e-learning framework 'AR Qaida' involving 3D interaction to meet specific goals for learning divided into levels and subjects, aiming to provide interactive, immersive learning for early-primary school children. This framework consists of 5 levels, each aiming at a specific subject: English alphabet learning (Level 1), Urdu letters (Level 2), Mathematics (Level 3), Solar System Visualization (Level 4), and Frog Dissection (Level 5) for early to primary school children's education (age 5-14). This framework was developed utilizing Vuforia marker-based AR and Unity 3D, deployed on mobile phones. We further conducted a study on 20 participants, using the SUS survey. Younger children were assisted in using the framework and filling out the survey. AR Qaida framework achieved a SUS score of 73.85 demonstrating that the framework is useful and acceptable. We did a t-Test to identify how gender affected the results. A two-Tailed p-value of 0.9793 indicates that the observed difference in means is not statistically significant. It is inferred from the t-Test results that there is no substantial disparity between genders in this study. © 2023 IEEE.

关键词： Visualization

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Dynamic Distributed Model Compression for Efficient Decentralized Federated Learning and Incentive Provisioning in Edge Computing Networks

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6293-6314页

作者： Asheralieva, Alia Niyato, Dusit Wei, Xuetao Southern University of Science and Technology Department of Computer Science and Engineering 1088 Xueyuan Avenue Shenzhen China Nanyang Technological University College of Computing and Data Science 50 Nanyang Avenue Singapore639798 Singapore

We study decentralized federated learning (DFL) in edge computing networks where edge nodes (ENs) collaboratively train their artificial intelligence (AI) models in a serverless manner without sharing local data. We consider the following critical DFL challenges: i) scarce bandwidth resources of ENs;ii) dynamic, heterogeneous edge environment;iii) incentive provisioning and complex tradeoffs between the DFL performance and training costs. To resolve these challenges, we develop a new model compression method where ENs utilize dynamic, non-identical compression rates to improve the communication efficiency of DFL under time-varying, heterogeneous resource constraints. We show that our method can be formulated as a graphical Markov potential game where ENs act as players deciding on their compression factors and the number of data samples used for model updates. Each EN is incentivized to participate in DFL through rewards based on the EN's contribution to training. We prove that our game has a dominant pure-strategy Nash equilibrium (NE) maximizing its potential function and propose a dynamic distributed compression algorithm in which each EN can find its dominant strategy independently. We show that this algorithm converges to the Pareto-optimal NE, representing the most efficient solution of our game enhancing the DFL performance with minimal costs. © 2002-2012 IEEE.

关键词： Federated learning

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Text Phishing Detection System using Random Forest Algorithm 3

Text Phishing Detection System using Random Forest Algorithm

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Rajoju, Radhika Sathvika, Vangala Smaran, Guthikonda Narayana Sai Tejashwini, Chettipelli Reddy, Gangasani Aditya Kakatiya Institute of Technology & Science Dept of Cse Warangal India Computer Science and Engineering Kakatiya Institute of Technology & Science Warangal India

ISBN: (纸本)9798350375190

This research paper presents a comprehensive exploration of advanced text classification methodologies, specifically tailored for the intricate task of distinguishing phishing emails from legitimate ones. Employing cutting-edge natural language processing techniques, our study meticulously pre-processes the dataset, ensuring optimal refinement through meticulous steps such as null value elimination, text normalization, punctuation removal, and stop-word elimination. By harnessing sophisticated feature engineering techniques like CountVectorizer and TF-IDF Transformer, we seamlessly convert textual data into numerical representations, enabling our machine learning algorithms to operate with precision and efficiency. The experimentation phase rigorously evaluates a diverse range of classifiers, including Naive Bayes, Decision Trees, Logistic Regression, Random Forest, AdaBoost and KNN. Remarkably, our findings highlight Random Forest as the standout performer, boasting an impressive accuracy rate of 96%. This exceptional performance underscores the algorithm's efficacy in accurately discerning between phishing attempts and genuine communications. As such, we advocate for the widespread adoption of Random Forest as the preferred model for email classification tasks, offering unparalleled accuracy and robustness essential for real-world deployment in cybersecurity contexts. © 2024 IEEE.

关键词： Adaptive boosting

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High-Throughput Independent Component Analysis Processor for Full Duplex Systems

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IEEE Journal on Selected Areas in Communications 2023年第9期41卷 2821-2832页

作者： Cheng, Jen-Hao Chang, Tien-Min Shen, Chung-An Fouda, Mohammed E. Eltawil, Ahmed M. National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan University of California at Irvine Electrical Engineering and Computer Science Department IrvineCA92697 United States King Abdullah University of Science and Technology Electrical and Computer Engineering Program Thuwal23955 Saudi Arabia

This paper presents the algorithm and very-large-scale integration (VLSI) architecture of a high-throughput and highly efficient independent component analysis (ICA) processor for self-interference cancellation (SIC) in in-band full-duplex (IBFD) systems. This is the first VLSI architecture reported in the literature based on the state-of-the-art entropy bound minimization (EBM) approach. A novel ICA algorithm is presented in this paper with momentum gradient descent optimization. Simulation results show that the number of iterations for the proposed algorithm is significantly reduced compared to the conventional ICA algorithms. Furthermore, a novel early-distribution estimation scheme is proposed in the designed ICA processor to compute multiple distribution functions with low latency and low complexity. The processing flow and the efficiency for the hardware utilization are specifically designed so that the processing speed is maximized with minimum employment of hardware components. The proposed ICA processor is designed and implemented based on the application-specific-integrated circuit (ASIC) flow. The post-layout estimations show that compared with the conventional EBM-based scheme, the proposed design improves the throughput and efficiency by 30x. In addition, compared to prior designs shown in the literature, the proposed ICA processor also demonstrates a significant enhancement in terms of throughput and efficiency. © 1983-2012 IEEE.

关键词： VLSI circuits

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Pruning Optimization of Deep Learning Model Based on Generalization Estimation

Pruning Optimization of Deep Learning Model Based on General...

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2024 International Conference on Artificial Intelligence, Deep Learning and Neural Networks, AIDLNN 2024

作者： Li, Xuanzhe Ke, Wenjun Wen, Wantao Liu, Shaotong Sheng, Xiaojuan School of Computer Science and Engineering Southeast University China Beijing Institute of Computer Technology and Application China

ISBN: (纸本)9798331520816

Existing research has shown that deep learning models contain considerable redundancy, requiring compression optimization to eliminate unnecessary parameters and improve computational efficiency. In model compression through pruning, many existing methods often use loss values to guide the identification of sub-network models. However, low training loss does not necessarily indicate good generalization ability. Consequently, reducing model parameters while preserving strong generalization performance has become a key area of research. Addressing the challenge of ensuring strong generalization performance of submodels after optimization through deep learning model pruning, we propose a channel-structured generalization estimation model. The model architecture incorporates Fully Connected Neural Networks (FCNN) and Gated Recurrent Units (GRU) as modules for channel feature extraction. Additionally, we introduce a differentiable pruning algorithm based on generalization estimation. Experimental results demonstrate the capability of the channel-structured generalization estimation model in predicting the generalization gap via regression. Furthermore, the proposed differentiable pruning algorithm effectively safeguards the generalization capability of sub-models. We conduct pruning experiments on NiN-structured convolutional neural network models on two datasets, CIFAR-10 and SVHN. Comparative experimental results demonstrate the effectiveness of this approach: achieving a 0.72% accuracy improvement over the original model on CIFAR-10;and a 0.12% accuracy improvement over the base model on SVHN while reducing FLOPs by 50.2%, validating the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Contrastive Learning

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Gauging Deep Learning Archetypal Effectiveness in Haematological Reclamation

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SN computer science 2024年第7期5卷 963页

作者： Pradeep kumar, B.P. Ravikumar, J. Dept. of Computer Science and Design Atria Institute of Technology VTU Karnataka Bangalore India Dept. of Computer Science and Engineering Atria Institute of Technology VTU Karnataka Bangalore India

This paper project an system for reclamation of medical images in an effective way for the extraction and retrieving of data from large archives is critical. This study aims to improve haematological image reclamation by combining visual-based image reclamation (VBIR) approaches with the GLCM algorithm, specifically for characteristics related to the prostate gland. While previous research has focused on computer-aided methods for Gleason grading, our technique focuses on early-stage feature reclamation across 14 paired glands. The experimental findings, including Bicubic Interpolation The resolution Enrichment with a standard deviation of 12.37, kurtosis of 122.83, and skewness of 2.92, demonstrate improved reclamation performance. Our suggested methodology uses a handmade learning approach that includes critical components such as GLCM contrast and correlation. This extensive feature extraction step includes descriptors for morphology, texture, fractals, and contextual information, which are then subjected to in-depth statistical analysis to identify significant features. Using an RNN, our technique improves performance somewhat but significantly when compared to alternatives, with time required for execution offering insights into the intricate interaction of stages and the effect of sample variability. These findings not only encourage more research, but also help to advance standard reviews in haematological analysis of images. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gland reclamation Gray-level co-occurrence matrix (GLCM) Haematological Haematological image Prostate gland

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AMBU-BOT: Effective Accident Preventive System Using IOT

AMBU-BOT: Effective Accident Preventive System Using IOT

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Suganya, P. Varalakshmi, I. Priyadharshini, S. Pradeepa, R. Sathvika, A. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India St. Joseph's College of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331510022

This paper intends to propose a new robot which overcomes the hurdles of an AED(Automated External Defibrillator) at the nearest Ambubot: A robot, ambulance robot designed and developed that carries an AED for assisting lay rescuers to save patients' lives, as well as Time taken before the ambulance is located in the case of a sudden cardiac arrest. The working of Ambubot could go searching for the victims and also obedient to the human instructions. Population is growing daily, and with it, so is the risk of heart disease also increasing. Even though traffic on our roads is normal for people, it is crucial to admit heart attack victims to hospitals in case of emergency because it can be easy for them to receive an automated external defibrillator at such a young age. Its independent purpose is to save the sufferer. © 2024 IEEE.

关键词： Defibrillators

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Analyzing Cross-Layer Design Approaches for Quality of Service in Wireless Networks 15

Analyzing Cross-Layer Design Approaches for Quality of Servi...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shreenidhi, H.S. Acharjya, Kalyan Saraswat, Nidhi Jain School of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & technology Uttar Pradesh India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350370249

Analyzing move-Layer layout procedures for satisfactory of provider (Qu 's) in Wi-Fi networks is of widespread importance to enhance network overall performance. Go-Layer optimization integrates distinct layers consisting of the physical, medium get right of entry to control (MAC), and community layers. Those layers are combined to successfully model the stop-to-cease network performance for each Wi-Fi and wired systems. Go-layer optimization can be used to improve the Qu's via numerous mechanisms, which include aid allocation, scheduling, adaptive modulation, and energy manipulate. These optimization techniques can be used to implement several Qu's-oriented functions together with safety in opposition to congestion, resilience to node and link failures, and adaptive visitors engineering. In this paper, we analyses numerous procedures to move-layer optimization that are appropriate for adapting the Qu's to dynamic situations in Wi-Fi networks. We talk the advantages and downsides of each method, and offer a complete overview on the modern-day studies trends. Eventually, we advise a framework for further research in this region. © 2024 IEEE.

关键词： Adaptive Optimization Performance Procedures Satisfactory Successfully Techniques

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Sentiment Analysis on Covid-19 data Using BERT Model

Sentiment Analysis on Covid-19 data Using BERT Model

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： ArunKarthi, T. Shanthi, S. Nirmaladevi, K. Ashwinth, K. Dev, R.B. Rajkumar, N. Computer Science And Engineering Kongu Engineering College Erode India Psna College Of Engineering And Technology Department Of Computer Applications Tamilnadu Dindigul India

ISBN: (纸本)9798350371567

In a world where microblogging has become an integral part of modern life., the sudden outbreak of COVID-19 swiftly infiltrated public opinion on various social networking platforms. With individuals adhering to social distancing measures and self-isolation protocols to combat the coronavirus, expressing their fears and concerns on social media became their primary outlet. In order to obtain a thorough comprehension of the pandemic's effects on individuals' lives and the environments in which they live, we must recognize the critical function of social media as a repository of emotions. This paper delves into the realm of sentiment analysis, focusing on the opinions disseminated through Twitter. Our primary dataset comprises these tweets, which we meticulously processed and converted into tokens for analysis. We performed fine-tuning and added an additional classifier layer to BERT, a pre-trained model, in order to classify these feelings into three categories: positive, negative, and neutral. Subsequently, we scrutinized BERT's performance in carrying out this task. Impressively, the BERT model demonstrated an outstanding accuracy rate of 94 percent on our dataset, supplemented by commendable scores in various evaluation metrics. © 2024 IEEE.

关键词： Sentiment analysis

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