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

作者： Nair, Lekshmi S Cheriyan, Jo Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India SAINTGITS College of Engineering Department of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798350370249

Complex networks, characterised by intricate structures arising from the relationships and interactions among their constituent elements, play a pivotal role in various domains such as social networks, biological systems, and technological infrastructures. The evolution and dynamics of complex networks are analysed for various applications such as link prediction, information diffusion, and recommendation systems. Recent research has focused on the study of structural motifs and the probabilistic analysis of motifs to capture the dynamics of the network. The motifs, considered building blocks of the larger network structure, are recurrent patterns in the network. This study focuses on the different probabilistic models built on triads, which are sets of three interconnected nodes. We investigate six different probability distributions, including Poisson, Gamma, Exponential, Power Law, Gaussian, Lorentzian, and Gaussian Mixture Models (GMMs) that can be applied in the complex network to capture the insight of the connectivity pattern. The applications of this research extend to refining network models, improving simulations, and enhancing our understanding of the likelihood of observing distinct patterns within complex social networks. © 2024 IEEE.

关键词： Poisson distribution

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A novel dominating set and centrality based graph convolutional network for node classification

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Multimedia Tools and Applications 2025年第14期84卷 12979-13004页

作者： Garg, Neeraj Garg, Sneha Jadeja, Mahipal Meena, Yogesh Kumar Gopalani, Dinesh Chauhan, Ganpat Singh Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur Rajasthan Jaipur302017 India Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Jaipur Rajasthan Jaipur302017 India Department of Information Technology School of IT Manipal University Jaipur Rajasthan Jaipur302034 India

To learn and analyze graph-structured data, Graph Neural Networks (GNNs) have emerged as a powerful framework over traditional neural networks, which work well on grid-like or sequential structure data. GNNs are particularly designed to record dependencies and relations in large and complicated networks such as social networks, molecular structures, citation networks and recommendation systems. The main idea behind GNN is the message-passing mechanism that allows sharing of information between nodes and edges in a graph network, enabling the model to learn about graph structures and perform well even with limited training data. GNNs have three different architectures: Graph Convolutional Network (GCN), GraphSAGE, Graph Attention Model (GAT) and other hybrid models. In this paper, we have proposed novel GCN models namely dominating Set with Degree Centrality based GCN (DS_DC_GCN) and dominating Set with Betweenness Centrality based GCN (DS_BWC_GCN) model, for node classification task. In the traditional GNN models, training nodes are selected randomly. In our proposed models, we first identify a subset of the nodes with the help of a dominating set (D) of the underlying network. Then we consider final training nodes (say D′) from set D based on the centrality scores (degree or betweenness). We give preference to nodes having high centrality scores for training. To evaluate the performance of the proposed models, we have used three standard citation network datasets - Cora, Citeseer and Pubmed. Our models obtain improved node classification performance as compared to other state-of-the-art-methods. The consideration of high centrality nodes of dominating set in training helps to improve the feature availability in the underlying computational graphs, which in turn improves learning and hence, model performance. These models also have the potential to offer better results for other network tasks, including link prediction and community detection. © The Author(s), under

关键词： Graph neural networks

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Task Offloading and Resource Management for IIoT with Satellite-Terrestrial Integrated Computing Power Network Based on D3QN

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IEEE Internet of Things Journal 2025年第12期12卷 21770-21783页

作者： Li, Meng Li, Meihui Wang, Kan Yu, F. Richard Wang, Zhuwei Si, Pengbo Beijing University of Technology School of Information Science and Technology Beijing100124 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

The management of computing resources through the computing power network (CPN) has gradually become a focal point of research. With the development of the 6th generation (6G) mobile networks, some promising technologies such as satellite-terrestrial integrated network (STIN) and smart endogenous network driven by artificial intelligence (AI) are increasingly being applied in Industrial Internet of Things (IIoT). However, several issues in current studies are worthy of attention: 1) the large number of devices powered by battery in IIoT, 2) the complex communication environments, 3) the finite computing resources for task data processing. To cope with these challenges, a satellite-terrestrial integrated computing power network (STICPN) framework is introduced in this article. Within this framework, a task offloading link selection scheme is proposed, which minimizes the delay and the consumption of energy. The task offloading optimization problem is modeled as a Markov Decision Process (MDP). Meanwhile, deep reinforcement learning (DRL) algorithm is employed to adapt to the dynamic states of environment. Specifically, a Dueling Double Deep Q Network (D3QN) is used to make optimal decisions and delay as well as energy consumption can be reduced significantly. Moreover, the D3QN-based scheme extends the usage time of IIoT devices. The simulation results indicate that the proposed scheme outperforms the comparison schemes significantly. © 2025 IEEE.

关键词： Energy utilization

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Low Cost Active Learning Framework for Short Answer Scoring 20th

Low Cost Active Learning Framework for Short Answer Scoring

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20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Hori, Tatsuya Yamauchi, Koichiro Graduate school of Engineering Department of Computer Science Chubu University Kasugai Japan

ISBN: (纸本)9789819600250

Short answer scoring (SAS) that automatically scores learner’ answers based on given rubric. Because SAS uses different rubrics for different prompts, it is necessary to generate training data for each prompt, which is time-consuming and labor-intensive. We propose a human-in-the-loop framework that achieves SAS with a minimal amount of training data through active data creation and efficient training. We fine-tuned the Sentence BERT model with a small dataset by using SetFit (Lewis Tunstall, et al. (2022).) and then apply active learning (AL) to an unlabeled data pool. The grader scores the data selected by AL, creating new training data that are then used to further fine-tune the models. For the experiments, we used six prompts from a Japanese SAS dataset. Our experimental results demonstrate that the proposed method outperforms previous studies in most cases. We also examined the potential challenges when applying this framework in practical scenarios. This framework significantly reduces the graders’ time and effort by enabling automatic scoring with minimal training data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Active learning

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Holistic Analysis and Development of a Pregnancy Risk Detection Framework: Unveiling Predictive Insights Beyond Random Forest

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SN computer science 2024年第8期5卷 1074页

作者： Irfan, Neha Zafar, Sherin Hussain, Imran Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

This paper presents a cutting-edge framework for predicting psychological health risks in pregnant women, supported by robust analytics and a user-friendly application interface. Utilizing a dataset of 1504 postpartum women, state-of-the-art machine learning algorithms, particularly Random Forest, achieved an impressive accuracy score of 0.7508. This underscores the framework's effectiveness in identifying psychological health risks with high precision. Beyond traditional accuracy metrics, the study adopts a comprehensive approach to performance evaluation, incorporating precision, recall, and F1 score to provide a nuanced understanding of classifier performance, essential for informed decision-making in healthcare settings. The primary goal is to establish a seamless computerized prediction pathway, enabling healthcare providers to proactively address mental well-being in pregnant women. The framework encompasses several key stages, including meticulous data collection, rigorous preprocessing, strategic feature selection, and algorithmic selection. Advanced data preprocessing techniques, such as outlier removal and null value elimination, were employed to enhance data quality and reliability. Feature selection focused on identifying pivotal attributes for precise prediction of psychological health risks, optimizing model efficacy. A distinguishing aspect of this research is its emphasis on user-centric application development. The bespoke Women's Mental Health Tracker, crafted using Python's Tkinter library, boasts a user-friendly interface with personalized recommendations, weekly progress tracking, access to a rich resource library, community support, reminders, and notifications. This empowers pregnant women to manage their mental well-being proactively with ease and confidence. Attribute analysis highlights critical psychological health indicators, including feelings of sadness, irritability, sleep disturbances, concentration issues, overeating, and anxiety. Wh

关键词： Feature selection Maternal mental health Predictive analytics Pregnancy risk detection Proactive mental health management Psychological health risks User- friendly application interface Women’s Mental Health Tracker

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Innovative Video Classification Method based on Deep Learning Approach

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Recent Patents on engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham science Publishers.

关键词： Convolutional neural networks

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Performance Analysis of Existing SDN Load Balancing Controller Using Mininet 1

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International Conference on Advanced engineering, Technology and Applications, ICAETA 2024

作者： Baniya, Pashupati Nand, Parma Bhushan, Bharat Hameed, Alaa Ali Jamil, Akhtar Agrawal, Atul Department of Computer Science and Engineering School of Engineeringand Technology IEC College of Engineering and Technology Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology ITS Engineering College Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul34396 Turkey Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

ISBN: (数字)9783031709241

ISBN: (纸本)9783031709234

Due to an increase in the load of network, load balancing service, i.e., a service that gives an equal volume of each task assignment to each of the servers in data centers, it is usually performed by the specialized hardware. Networks have to adjust due to the load and also as a result of dealing with faults and complexity which are provided in the implementation and configuration process. Network engineers or administrators must be aware of selecting controllers according to the network requirement, which can lead to performance issues, scalability challenges, reliability and fault tolerance and many more problems in the network. To tackle this problem, this research analyses four different controllers in an experiment: In-band, open flow, OVS, and remote controller, which are the key aspects. The paper shows how these controllers, which are detected through a comprehensive evaluation of network performance attributes (e.g., Throughput and Jitter), affect the overall system’s performance. This information provides beneficial information for the researchers and practitioners who wish to make informed choices whenever they select suitable controls for specific network needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Remote control

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A Deep-Siamese CNN-based Approach for Identifying Mycosphaerella Coffeeicola Disease in Coffee Foliage 8

A Deep-Siamese CNN-based Approach for Identifying Mycosphaer...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Selvanarayanan, Raveena Rajendran, Surendran Jayaraman, Venkatesh Sathish Kumar, P.J. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

Mycosphaerella Coffeeicola (Black Spot Disease) is the most destructive disease in coffee leaves and affects global production. Diseased coffee leaves can adversely affect coffee quality, discolor, distort, or rot the coffee leaves, and impair the coffee beans' cherry, taste, and aroma. The disease can also affect the quality of the coffee beans, leading to lower market value. The management of the Coffee Production department of India controls the disease through a combination of cultural practices, chemical control, and the use of resistant coffee varieties. In the existing work, using single-layer perceptron was trained and validated with 100 epochs to obtain an accuracy of 9 5. 7 1 %. In the piece above of work, an infected leaf is accurately diagnosed using deep learning-based models by employing the Deep-Siamese-based CNN Approach, recognized by a deep Siamese Neural network architecture which is also called a Twin Neural network with high illness recognition precision and simplified hyper-parameters to maximize description capacity while avoiding overfitting concerns, using a network with high sickness detection precision and minimal hyper-parameters. The prototype was tested in the lab on natural plants for known sickness instances, and the results matched the model validation datasets. When compared to several deep-learning models, the suggested structure attained an accuracy of 9 6. 8 9 %. © 2024 IEEE.

关键词： Convolutional neural networks

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Learning-based joint UAV trajectory and power allocation optimization for secure IoT networks

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Digital Communications and Networks 2022年第4期8卷 415-421页

作者： Dan Deng Xingwang Li Varun Menon Md Jalil Piran Hui Chen Mian Ahmad Jan School of Information Engineering Guangzhou Panyu PolytechnicGuangzhou410630China School of Physics and Electronic Information Engineering Henan Polytechnic UniversityJiaozuo454150China Department of Computer Science and Engineering SCMS School of Engineering and TechnologyIndia Department of Computer Science and Engineering Sejong UniversitySouth Korea Department of Computer Science Abdul Wali Khan University MardanPakistan

Non-Orthogonal Multiplex Access(NOMA)can be deployed in Unmanned Aerial Vehicle(UAV)networks to improve spectrum *** to the broadcasting feature of NOMA-UAV networks,it is essential to focus on the security of the wireless *** paper focuses on maximizing the secrecy sum rate under the constraint of the achievable rate of the legitimate *** tackle the non-convexity optimization problem,a reinforcement learning-based alternative optimization algorithm is ***,with the help of successive convex approximations,the optimal power allocation scheme with a given UAV trajectory is obtained by using convex optimization ***,through plenty of explorations of the wireless environment,the Q-learning networks approach the optimal location transition strategy of the UAV,even without the wireless channel state information.

关键词： Unmanned aerial vehicle(UAV) NOMA Reinforcement learning Secure communications Deep Q-learning

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Customer Segmentation Analysis Using Clustering Algorithms 3rd

Customer Segmentation Analysis Using Clustering Algorithms

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3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Reddy, Biyyapu Sri Vardhan Rishikeshan, C.A. Dagumati, VishnuVardhan Prasad, Ashwani Singh, Bhavya School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819939312

Customer segmentation has been deployed as a prudent marketing strategy by companies to ensure that their investments are less risky and more judicious. Segmenting customers helps the companies to divide the customers into groups that reflect similarity and maximize the value of each customer to the business. The main goal of this research is to use a machine learning clustering approach called K-means clustering to accomplish consumer segmentation. Besides, the research work is also focused on performing exploratory data analysis on the given dataset. To group the customers into clusters, a K-means clustering algorithm is performed on the customer dataset. To achieve optimization and validation of the clusters, popular heuristic, interpretation, and approximation methods have been included in this paper. Further, for analyzing and visualizing the important facets of the customer dataset and the operation of the K-means algorithm, the paper presents some colorful and informative representations. The implementation of this research work has been done in the R programming language. The outcome of this work includes visualizing the segments of the mall customers in the form of clusters based on their spending scores and annual incomes. Furthermore, a better customer segmentation could be achieved by taking product reviews and customer feedback into consideration. Nevertheless, customer segmentation remains a prospective topic for many researchers and companies due to dynamic customer behavior. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： K-means clustering

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