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Graph CA: Learning From Graph Counterfactual Augmentation for Knowledge Tracing

IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2108-2123页

作者： Xinhua Wang Shasha Zhao Lei Guo Lei Zhu Chaoran Cui Liancheng Xu School of Information Science and Engineering Shandong Normal UniversityJinan 250358China School of Computer Science and Technology Shandong University of Finance and EconomicsJinan 250014China

With the popularity of online learning in educational settings, knowledge tracing(KT) plays an increasingly significant role. The task of KT is to help students learn more effectively by predicting their next mastery of knowledge based on their historical exercise sequences. Nowadays, many related works have emerged in this field, such as Bayesian knowledge tracing and deep knowledge tracing methods. Despite the progress that has been made in KT, existing techniques still have the following limitations: 1) Previous studies address KT by only exploring the observational sparsity data distribution, and the counterfactual data distribution has been largely ignored. 2) Current works designed for KT only consider either the entity relationships between questions and concepts, or the relations between two concepts, and none of them investigates the relations among students, questions, and concepts, simultaneously, leading to inaccurate student modeling. To address the above limitations,we propose a graph counterfactual augmentation method for knowledge tracing. Concretely, to consider the multiple relationships among different entities, we first uniform students, questions, and concepts in graphs, and then leverage a heterogeneous graph convolutional network to conduct representation *** model the counterfactual world, we conduct counterfactual transformations on students’ learning graphs by changing the corresponding treatments and then exploit the counterfactual outcomes in a contrastive learning framework. We conduct extensive experiments on three real-world datasets, and the experimental results demonstrate the superiority of our proposed Graph CA method compared with several state-of-the-art baselines.

关键词： Contrastive learning counterfactual representation graph neural network knowledge tracing

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An Enhanced RNN-LSTM Model for Fundus Image Classification to Diagnose Glaucoma

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SN computer science 2024年第5期5卷 1-17页

作者： Veena, H.N. Patil, Kavita K. Vanajakshi, P. Ambore, Anilkumar Gowda, Naveen Chandra Department of Computer Science and Engineering SJB Institute of Technology Bengaluru India Department of Information Science and Engineering Global Academy of Technology Bengaluru India Department of Information Science and Engineering Vivekananda Institute of Technology Bengaluru India School of CSE REVA University Bengaluru India School of Computer Science and Engineering REVA University Bengaluru India

In the current medical implications, one of the leading ocular diseases is Glaucoma which majorly damage the Optic Nerve Head (ONH) of the eye retina. The intraocular pressure of the eye leads to glaucoma, which may lead to complete or partial vision loss. Regular screening and early detection is the only solution to avoid further vision loss. Due to the laborious and manual procedure in diagnosis, an automatic system is needed to diagnose glaucoma. This paper presents a novel method using deep learning-based RNNLSTM classification model to develop an automatic approach to predict and classify the images to be as healthy or glaucomatous. The RNN and LSTM with dense, dropout, and batch normalization layers are used for training and testing the proposed prediction model. The RNN model is used for training the model and to overcome the problems that occur during training, the LSTM model is applied to increase the performance of the model. The proposed model achieved an accuracy of 97.4%, specificity of 97.9% and sensitivity of 97%in classifying the images. We have made use of the DRISHTI-GS database for training and testing the proposed model. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Glaucoma Long short-term memory (LSTM) Recurrent neural network (RNN)

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Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

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computers, Materials & Continua 2024年第6期79卷 4433-4448页

作者： Tingting Su Jia Wang Wei Hu Gaoqiang Dong Jeon Gwanggil School of Computer Science and Technology Xinjiang UniversityUrumqi830046China School of Cyber Science and Engineering Jinling Institute of Technology CityNanjing210000China College of Information Technology Incheon National UniversityIncheon22012Korea

Along with the progression of Internet of Things(IoT)technology,network terminals are becoming continuously more *** has been widely applied in various scenarios,including urban infrastructure,transportation,industry,personal life,and other socio-economic *** introduction of deep learning has brought new security challenges,like an increment in abnormal traffic,which threatens network *** feature extraction leads to less accurate classification *** abnormal traffic detection,the data of network traffic is high-dimensional and *** data not only increases the computational burden of model training but also makes information extraction more *** address these issues,this paper proposes an MD-MRD-ResNeXt model for abnormal network traffic *** fully utilize the multi-scale information in network traffic,a Multi-scale Dilated feature extraction(MD)block is *** module can effectively understand and process information at various scales and uses dilated convolution technology to significantly broaden the model’s receptive *** proposed Max-feature-map Residual with Dual-channel pooling(MRD)block integrates the maximum feature map with the residual *** module ensures the model focuses on key information,thereby optimizing computational efficiency and reducing unnecessary information *** results show that compared to the latest methods,the proposed abnormal traffic detection model improves accuracy by about 2%.

关键词： Abnormal network traffic deep learning residual network multi-scale feature extraction max-feature-map

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A privacy-preserving deep learning framework for highly authenticated blockchain secure storage system

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Multimedia Tools and Applications 2024年第36期83卷 84299-84329页

作者： Haque, Sheikh Moeen Ul Sofi, Shabir Ahamd Sholla, Sahil Department of Information Technology NIT Srinagar Jammu and Kashmir Hazratbal190006 India Department of Computer Science and Engineering Islamic University of Science and Technology Jammu and Kashmir Awantipora192122 India

To ensure the privacy, integrity, and security of the user data and to prevent the unauthorized access of data by illegal users in the blockchain storage system is significant. Blockchain networks are widely used for the authentication of data between the data user and the data owner. However, blockchain networks are vulnerable to potential privacy risks and security issues concerned with the data transfer and the logging of data transactions. To overcome these challenges and enhance the security associated with blockchain storage systems, this research develops a highly authenticated secure blockchain storage system utilizing a rider search optimized deep Convolution Neural Network(CNN) model. The architecture integrates the Ethereum blockchain, Interplanetary File System (IPFS), data users, and owners, in which the Smart contracts eliminate intermediaries, bolstering user-owner interactions. In tandem, blockchain ensures immutable transaction records, and merging IPFS with blockchain enables off-chain, distributed storage of data, with hash records on the blockchain. The research accomplishes privacy preservation through six-phase network development: system establishment, registration, encryption, token generation, testing, and decryption. Parameters for secure transactions are initialized, user registration provides genuine user transaction credentials, and encryption guarantees data security, employing optimized Elliptic Curve Cryptography (ECC). Further, the optimized ECC algorithm is developed utilizing a novel rider search optimization that utilizes search and rescue characteristics of human, and rider characteristics for determining the shorter key lengths. Token generation involves issuing digital tokens on a blockchain platform, followed by testing using a deep CNN classifier to detect anomalies and prevent unauthorized data access during the test phase. The decryption of data is conducted for registered users. The developed rider search optimized deep CN

关键词： Blockchain

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Mobility-Aware Power Control and User Scheduling for Downlink V2I Networks 100

Mobility-Aware Power Control and User Scheduling for Downlin...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Wang, Yujie Zhou, Momiao Sun, Yanshi Wang, Kan Hefei University of Technology School of Computer Science and Information Engineering Hefei China Xi'an University of Technology Faculty of Computer Science and Engineering Xi'an China

ISBN: (纸本)9798331517786

Vehicle-to-infrastructure (V2I) network is a new paradigm of wireless system with special topology where roadside units (RSUs) are linearly deployed along the roadside and vehicles linearly move on the road. For such system, some classical problems would have new formulations and solutions. We in this paper investigate the joint power control and user scheduling problem for a multi-cell downlink V2I network, the objective of which is to maximize the sum-rate of the network under the signal-to-interference-plus-noise ratio (SINR) constraint of each V2I link. Considering the high mobility of vehicles, the objective function is set as the mean of the sum-rate over a sequence of time slots. For ease of handling, we first decouple the problem into multiple separate subproblems based on the linear distribution of RSUs. Then we employ the quadratic transform technique for fractional programming (FP) to transform the mixed integer nonlinear programming (MINLP) subproblems into convex problems, and obtain the solutions with Branch and Bound method. Finally the validity of our proposed algorithm is verified by numerical simulations. © 2024 IEEE.

关键词： Quadratic programming

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Stairdepth: a novel staircase detection through depth maps generated by depth anything V2

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International Journal of information technology (Singapore) 2025年第4期17卷 2075-2086页

作者： Shekar, Avire Laxmi Chandra Chandrika, Mukkolla Bhuvana Naidu, Vakkalagadda Hemanth Muppalaneni, Naresh Babu Department of Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing Kurnool Kurnool India

Detection of a staircase is an important task in the fields of both assistive technology and autonomous navigation with an aim to enable substantial improvement in safety and accessibility for both those with limited vision and those with mobility problems. Classic methods often perform poorly under challenging conditions, such as low lighting, irregular architectural arrangements, or occluded views. Whereas the integration of physical depth sensors has increased detection accuracy, this integration introduces increased complexity, cost and greater maintenance issues. Our work proposes a novel architecture capable of directly estimating depth maps from color images using a pre-trained model without relying on physical sensors for depth estimation. It detects the staircase efficiently, including the classification of stair lines into concave or convex, with the classification of surfaces as a tread or riser, hence localizing staircases accurately. Our proposed methodology demonstrated exceptional results, with 83.2% Precision, 90.8% Recall, and 72.2% Intersection over Union on stair line classification and 97.1% Pixel Accuracy and 82.9% Mean Pixel Accuracy on stair surfaces by outperforming the efficacy of current approaches pertaining to stair line and surface classification. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Depth image Segmentation Stair surfaces Staircase detection StairDepth Stairline

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Appearance and motion-based unusual crowd events detection using multiple moving objects

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Multimedia Tools and Applications 2024年第42期83卷 90177-90189页

作者： Babu, K. Atlas, L. Godlin Sreeji, S. Arjun, K.P. Rasmi, A. Senthilvel, P. Gururama Computational Intelligence SRM Institute of Science & Technology Chengalpet Kattankulathur603203 India Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering Gitam University Karnataka Bengaluru India Information Science and Engineering RRIT Bangalore India Saveetha University Chennai602 105 India

Unusual crowd analysis is an important problem in surveillance video due to their features cannot be extracted efficiently on the crowd scenes. To overcome this challenge, this paper introduced the appearance and motion based unusual events detection method by using multiple moving objects. This method includes appearance and motion based computation, and moving objects trajectory analysis. By using these all techniques, the unusual crowds and their activities can be obtained by analyzing moving objects size and the distance among multiple moving objects. Two benchmark datasets such as PETS 2009 and UMN are used for the experimental results. Also, several existing algorithms are compared with the proposed method to evaluate the effectiveness of the proposed work. The comparative analysis results proved that the proposed method achieved best accuracy for unusual crowd events. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： And trajectory analysis Appearance based analysis Distance based analysis Unusual crowd events detection

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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua science and technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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African Bison Optimization Algorithm:A New Bio-Inspired Optimizer with engineering Applications

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computers, Materials & Continua 2024年第10期81卷 603-623页

作者： Jian Zhao Kang Wang Jiacun Wang Xiwang Guo Liang Qi School of Science University of Science and Technology LiaoningAnshan114051China Team of Artificial Intelligence Theory and Application University of Science and Technology LiaoningAnshan114051China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Information and Control Engineering College Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China

This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological *** is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and *** foraging behavior prompts the bison to seek a richer food source for *** bison find a food source,they stick around for a while by bathing *** jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating *** eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent *** above behaviors are translated into ABO by mathematical *** assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with *** findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.

关键词： Optimization metaheuristics African bison optimization engineering problems

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Automatic Emotion Detection from Facial Features Extraction and Machine Learning Models

Automatic Emotion Detection from Facial Features Extraction ...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Shankar, Raghav Singh Kirar, Bhupendra Upadhyay, Pratham Jee Mishra, Shree Govind Indian Institute of Information Technology Computer Science and Engineering Bhopal India Indian Institute of Information Technology Electronics and Communications Engineering Bhopal India Information Technology Indian Institute of Information Technology Bhopal India

ISBN: (纸本)9798331529833

In the domain of Interactive Systems (IS), machine learning (ML) emerges as a profound, user-friendly, and accurate approach to enhancing emotional intelligence in digital systems. This research work focuses on facial emotion detection, an important but often-neglected aspect of affective computing. Models used for the assessment include Multinomial Naïve Bayes (MNB), Decision Trees (DT), K-Nearest Neighbours (KNN), Random Forest (RF), Support Vector Machines (SVMs), XG Boost (XGB), and Logistic Regression (LR). By employing the FER2013 dataset and MediaPipe's (MP's) facial recognition technology, this research extracts 478 3D facial landmarks to test. The best Acc in emotion detection is found using LR with an accuracy of 65.57% followed by XGB with an accuracy of 61.24%. This study thus will help as a practical application toward making improvements in IS in matters regarding health, marketing, or even security. © 2025 IEEE.

关键词： Adversarial machine learning

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