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An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of Computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Circular RNAs (circRNAs) RNA binding proteins Convolutional neural network Temporal convolutional network Encoder-decoder network

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Improving agility in projects using machine learning algorithm

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Multimedia Tools and Applications 2024年第38期83卷 85987-86005页

作者： Varun, Janani Karthika, R.A. Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India

All the software products developed will need testing to ensure the quality and accuracy of the product. It makes the life of testers much easier when they can optimize on the effort spent and predict defects for the upcoming modules in the Agile era. The functionality being discussed in this paper is to predict the defects using Random Forest Algorithm. Predictive analytics draws on information from the past to create forecasts about the outcomes of future events. Product team always have the difficulty in delivering the product as per schedule. As we are in the agile era, the requirement keeps changing and team is unsure on upcoming releases. Prediction helps the team to focus on the complex and error prone modules in upcoming releases. The Predictive analytics model designed, can predict defects with an accuracy rate of 88% with the help of historical data. By predicting, testers can focus on the module where there are a greater number of defects predicted by the model and left shift the delivery. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Predictive analytics

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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Mapping the Landscape of Generative AI in Network Monitoring and Management

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2441-2472页

作者： Bovenzi, Giampaolo Cerasuolo, Francesco Ciuonzo, Domenico Monda, Davide Di Guarino, Idio Montieri, Antonio Persico, Valerio Pescape, Antonio Italy DIETI and IMT School for Advanced Studies Lucca Italy University of Verona Department of Computer Science Italy

Generative Artificial Intelligence (GenAI) models such as LLMs, GPTs, and Diffusion Models have recently gained widespread attention from both the research and the industrial communities. This survey explores their application in network monitoring and management, focusing on prominent use cases, as well as challenges and opportunities. We discuss how network traffic generation and classification, network intrusion detection, networked system log analysis, and network digital assistance can benefit from the use of GenAI models. Additionally, we provide an overview of the available GenAI models, datasets for largescale training phases, and platforms for the development of such models. Finally, we discuss research directions that potentially mitigate the roadblocks to the adoption of GenAI for network monitoring and management. Our investigation aims to map the current landscape and pave the way for future research in leveraging GenAI for network monitoring and management. © 2004-2012 IEEE.

关键词： Generative adversarial networks

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Data Collection and Preprocessing Management for Sugarcane Leaf Disease Identification using by Spatial Relation-Assisted Contrast Enhancement (SRCE) 2

Data Collection and Preprocessing Management for Sugarcane L...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Akhil, S. Durga, R. Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (纸本)9798331504960

Background: Sugarcane, an essential commodity for the global sugar industry, is very vulnerable to several illnesses that significantly impact its output and quality. Methods: The study uses Spatial Relation Assisted Contrast Enhancement (SRCE) to enhance contrast in images of harmed sugarcane leaves. SRCE selectively enhances contrast, allowing for better differentiation between healthy and diseased areas. It focuses on local neighborhoods, highlighting critical symptoms while preserving leaf structure and texture. The enhanced images were analyzed using machine learning classifiers to assess their effectiveness in disease detection. Result: The results demonstrated that SRCE significantly improved the detection accuracy compared to traditional contrast enhancement techniques. This method aids in early disease identification and supports precision agriculture practices by enabling timely interventions. The results of SRCE of CII, LIF, SSIM, and PSNR values are evaluated. Conclusion: Our findings suggest that SRCE can be a valuable agricultural monitoring and management tool, ultimately contributing to increased sugarcane yield and sustainability. © 2024 IEEE.

关键词： CII disease LIF PSNR SRCE SSIM sugarcane leaf

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Feasible Load Balancing for Webserver in Cloud Environment using Energy Efficient Maximal Support Priority Scheduling Approach 2

Feasible Load Balancing for Webserver in Cloud Environment u...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Rajasingh, V.M. Daniel Durga, R. Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (纸本)9798331504960

Task management and energy efficiency have grown increasingly crucial in cloud computing environments. Effective job scheduling is essential to maximizing resource usage and overall efficiency in cloud computing. However, ensuring energy efficiency is a critical challenge in task scheduling. Traditional task-scheduling methods are often costly and require more adaptability and efficiency. To solve this problem, we propose an Energy Efficient Maximal Support Priority Scheduling Approach (EEMSPS), which schedules tasks in a virtual cloud environment. Furthermore, the Task Completion Time (TCT) algorithm is utilized to calculate the task length and time. Additionally, the Scheduling Based Artificial Bee Colony (SBABC) method is employed to organize the task and reduce data processing time. Lastly, the EEMSPS algorithm assesses the load balancing in the cloud. Additionally, energy efficiency, task execution time, and energy consumption are used to evaluate the proposed method's performance compared to traditional techniques. Therefore, the energy efficiency performance is 96.4% for task scheduling in the cloud. © 2024 IEEE.

关键词： Cloud platforms

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Predicting and Associating of Spectral Feature Nutritional Intake Level using Bayesian Kernel Machine Regression (BKMR) Method 2

Predicting and Associating of Spectral Feature Nutritional I...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Jeyanthi, P. Durga, R. Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (纸本)9798331504960

Children's nutrition is an essential determinant of their development, so there is a need to forecast their food consumption adequately. This work presents a model for assessing children's nutritional intake levels from a machine-learning approach. The data cleaning step deals with missing values, outliers, and data inconsistencies to produce high-quality data for analysis from the dataset. Feature selection applies the Level-based Long Short-Term Memory (LLSTM) that captures temporal dependencies in the children's eating patterns. We use Bayesian Kernel Machine Regression (BKMR) for classification since it is not limited to linear relations between food intake and different factors that could influence them. Compared with the other traditional methods, the accuracy of the proposed method is 96.2%. The framework discussed here is located in the field of child nutrition, focusing mainly on the issue of meeting children's basic nutritional needs. This work proves that the probability of nutrition intake can be predicted by the proposed framework, which can help to make better recommendations on children's diet and, therefore, on their future health. © 2024 IEEE.

关键词： BKMR children nutrition data cleaning LLSTM nutritional intake levels

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Clinical Insight: Comparative Analysis of Deep Learning Models for Disease Prediction across Multifaceted Datasets 3

Clinical Insight: Comparative Analysis of Deep Learning Mode...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Binu, Shiiu K. Shanthi, C. Vels Institute Of Science Technology & Advanced Studies Department Of Computer Science Chennai India

ISBN: (纸本)9798350318609

This research introduces an advanced model architecture for chronic disease detection, leveraging highly developed artificial intelligence algorithms. The prototype combines the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) procedure, showcasing significant advancements in predictive capabilities. The proposed hybrid approach, integrating CNN and LSTM with forwarding selection, is adopted for disease presence prediction. Employing a tenfold cross-validation strategy and optimizing with 200 training cycles, the model demonstrates a meticulous training process. Learning rate control plays a pivotal role, and a rate of 0.01 is chosen for optimal learning. The model is applied to datasets related to cancer, heart, kidney, and liver diseases, among other diseases, with feature selection based on information gain. Seven cutting-edge classification algorithms-RF, SVM, KNN, CNN, and CNN-LSTM-are used in a thorough examination. The outcome showcases the superiority of the proposed CNN-LSTM model with notable accuracy rates-98% and 99%, respectively, for chronic kidney disease prediction. © 2024 IEEE.

关键词： Long short-term memory

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Cyber Attack Detection Using Deep Multi-agent Reinforcement Learning with Beth Dataset

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SN computer science 2025年第5期6卷 1-16页

作者： Manikandan, A. Rajan, S. Deepa Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies VISTAS Tamil Nadu Chennai India

The Internet of Things (IoT) transforms connectivity, enabling devices to share data seamlessly via the cloud or wireless networks. However, IoT systems remain vulnerable to sophisticated cyberattacks, and traditional detection methods often fail due to inadequate fine-tuning and ineffective feature extraction. This study addresses these challenges by proposing a novel cyberattack detection framework based on deep multi-agent reinforcement learning (MARL). The framework employs the deep deterministic policy gradient (DDPG) algorithm to enhance the adaptive capabilities of multiple agents within an IoT network environment. A newly curated dataset, BETH, is introduced, encompassing diverse cyberattack scenarios specifically designed for training and evaluating MARL-based systems. Through deep reinforcement learning, the framework autonomously learns to detect and mitigate known and emerging threats. To assess the robustness and generalizability of the proposed model, a fivefold cross-validation process was conducted. Experimental results reveal that the proposed approach achieves an exceptional detection accuracy of 99.95%, demonstrating its ability to adapt to dynamic attack patterns while minimizing error rates. The findings highlight the framework’s potential to improve network security significantly, advancing the field of intelligent, autonomous cyber defence for IoT systems. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anomaly detection agents (ADA) BETH dataset Collaboration and communication buffer (CCB) Cyber-attack detection Deep deterministic policy gradient algorithm Internet of Things (IoT) Intrusion detection agents (IDA) Multi-agent reinforcement learning (MARL)

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Enhanced Multi-Layer Bat Optimization(EMBO) Approach for Data Acquisition and Security Management in Web Platform 2

Enhanced Multi-Layer Bat Optimization(EMBO) Approach for Dat...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Siva, V.R. Durga, R. Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (纸本)9798331504960

Intrusion Detection Systems (IDS) have grown to be essential to network and computer security. However, it complicates the training phase and makes it time-consuming to detect vulnerabilities in data sharing on the network. To solve this problem, we introduced a Multi-Layer Perceptron Neural Network (MLPNN) technique in data science to classify malicious and non-malicious network traffic. In addition, we preprocess the data by normalizing the mean original value using the Z-score Min-Max Scaling (ZSMMS) technique. In addition, we select the best feature by analyzing the weight and bias values with the Improved Bat Optimization (IBO) algorithm. After that, we propose the MLPNN technique to classify network traffic and improve accuracy. Finally, the Optimal Padding advanced Encryption Standard (OPAES) algorithm will be implemented to enhance the security of the website. Additionally, by producing an IDS model and using metrics such as accuracy, recall, time complexity, and security performance, the website's security performance can be improved to 95.7%. © 2024 IEEE.

关键词： Network intrusion

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