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6th International Conference on Advances in Robotics, AIR 2023

作者： Talukdar, Upasana Paul, Prasun Computer Sc. and Engineering Indian Institute of Technology Guwahati India Computer Science and Engineering Indian Institute of Information Technology Guwahati India

ISBN: (纸本)9781450399807

Brain fog, characterized by confusion, lack of attention, and mental clarity, is one of the main reasons that lead to the downgrading of performance in the learning process. In recent years, online education has gained quite popularity. Analysis of the cognitive states of the students in real-time during online classes is challenging as immediate and prompt feedback from students in online education is less feasible as compared to that of classroom education. This paper presents an EEG-based Passive Brain-computer Interface (pBCI) framework for identifying the cognitive states of students during online video learning. The framework detects three cognitive states: confusion, attention, and mediation while watching online course videos using EEG data. The paper first presents rigorous experimental analysis to identify the confusion levels, followed by proposing a methodology based on kernel partial least square algorithm to predict the attention and mediation levels. The paper also explores the dominant features associated with confusion levels and the correlation between the three cognitive states. © 2023 ACM.

关键词： Students

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PRF: deep neural network compression by systematic pruning of redundant filters

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Neural Computing and Applications 2024年第33期36卷 20607-20616页

作者： Sarvani, C.H. Ghorai, Mrinmoy Basha, S. H. Shabbeer Computer Vision Group Indian Institute of Information Technology Andhra Pradesh Sri City Chittoor517646 India School of Computer Science and Engineering RV University Karnataka Bengaluru560059 India

In deep neural networks, the filters of convolutional layers play an important role in extracting the features from the input. Redundant filters often extract similar features, leading to increased computational overhead and larger model size. To address this issue, a two-step approach is proposed in this paper. First, the clusters of redundant filters are identified based on the cosine distance between them using hierarchical agglomerative clustering (HAC). Next, instead of pruning all the redundant filters from every cluster in single-shot, we propose to prune the filters in a systematic manner. To prune the filters, the cluster importance among all clusters and filter importance within each cluster are identified using the 1-norm based criterion. Then, based on the pruning ratio filters from the least important cluster to the most important ones are pruned systematically. The proposed method showed better results compared to other clustering-based works. The benchmark datasets CIFAR-10 and ImageNet are used in the experiments. After pruning 83.92% parameters from VGG-16 architecture, an improvement over the baseline is observed. After pruning 54.59% and 49.33% of the FLOPs from ResNet-56 and ResNet-110, respectively, both showed an improvement in accuracy. After pruning 52.97% of the FLOPs, the top-5 accuracy of ResNet-50 drops by only 0.56 over ImageNet. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep neural networks

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Event-Driven Attention Network:A Cross-Modal Framework for Efficient Image-Text Retrieval in Mass Gathering Events

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computers, Materials & Continua 2025年第5期83卷 3277-3301页

作者： Kamil Yasen Heyan Jin Sijie Yang Li Zhan Xuyang Zhang Ke Qin Ye Li School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Information and Software Engineering University of Electronic Science and Technology ofChinaChengdu611731China Kashi Institute of Electronics and Information Industry Kashi844508China

Research on mass gathering events is critical for ensuring public security and maintaining social ***,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering *** believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and ***,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective *** address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first *** image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval *** local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model *** overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event *** calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval *** validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent *** conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of *** the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,w

关键词： Mass gathering events image-text retrieval attention mechanism

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Robust and Interpretable Vector Borne Disease Prediction in IoT-Enabled Edge Networks

Robust and Interpretable Vector Borne Disease Prediction in ...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Doulani, Khushbu Sharma, Samarth Adhikari, Mainak Vijayalata, Y. Indian Institute of Information Technology Lucknow Department of Computer Science Lucknow India Indian Institute of Information Technology Lucknow Department of Computer Science and Artificial Intelligence Lucknow India School of Data Science Indian Institute of Science Education and Research Thiruvananthapuram Thiruvananthapuram India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350364866

Vector-borne diseases have created critical world-wide public health hazards that require innovative approaches for prevention and management. Within the context of IoT-enabled edge networks, this study offers a unique framework to build a reliable and straightforward distant prediction model for vector-borne diseases. The suggested strategy incorporates the use of edge computing capabilities to predict the onset and spread of diseases caused by vectors by using real-time data streams from mobile applications. Employing widely used techniques from machine learning, including interpretable models, our system not only achieves high prediction accuracy but also provides insight into the most important factors influencing the spread of disease. Moreover, this approach overcomes the resource constraints essential to edge networks, ensuring the prediction model's dependability and scalability. © 2024 IEEE.

关键词： Prediction models

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Exploring image data association: A hybrid mining approach

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Multimedia Tools and Applications 2025年第9期84卷 5725-5740页

作者： Parashar, Nishtha Tiwari, Akhilesh Gupta, Rajendra Kumar Department of Computer Science and Engineering Madhav Institute of Technology & Science M.P. Gwalior India Department of Information Technology Madhav Institute of Technology & Science M.P. Gwalior India

In this paper, a new approach for mining image association rules is presented, which involves the fine-tuned CNN model, as well as the proposed FIAR and OFIAR algorithms. Initially, the image transactional database is generated using feature vectors obtained from the fine-tuned CNN architecture. The proposed FIAR algorithm is used to generate hash-indexed image association rules, which are further optimized using the proposed OFIAR algorithm. This methodology combines the strengths of the CNN model to extract histogram features from images, the FIAR algorithm to efficiently mine frequent image itemsets, and the OFIAR algorithm to optimize image association rules. The proposed methodology can be used to discover hidden relationships among images, leading to new insights in image processing and analysis. Efficient results were obtained with a minimum support of 0.50 and a minimum confidence of 0.50. Experiments were performed on the fruits image dataset consisting of 2618 images from six different classes, and the results show that image mining is feasible and can produce strong optimized image association rules that can be further used for classification purposes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Association rules

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An Anchor-Aware Graph Autoencoder Fused with Gini Index Model for Link Prediction

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SN computer science 2024年第6期5卷 745页

作者： Kumar, Shambhu Bisht, Dinesh Jain, Arti Computer Science & amp Engineering and IT Jaypee Institute of Information Technology Department of Mathematics Jaypee Institute of Information Technology

Link prediction stands as a crucial network challenge, garnering attention over the past decade, with its significance heightened by the escalating volume of network data. In response to the pressing need for swift research focus, this study introduces an innovative approach—the Anchor-aware Graph Autoencoder integrated with the Gini Index (AGA-GI)—aimed at gathering data on the global placements of link nodes within the link prediction framework. The proposed methodology encompasses three key components: anchor points, node-to-anchor paths, and node embedding. Anchor points within the network are identified by leveraging the graph structure as an input. The determination of anchor positions involves computing the Gini indexes (GI) of nodes, leading to the generation of a candidate set of anchors. Typically, these anchor points are distributed across the network structure, facilitating substantial informational exchanges with other nodes. The location-based similarity approach computes the paths between anchor points and nodes. It identifies the shortest path, creating a node path information function that incorporates feature details and location similarity. The ultimate embedding representation of the node is then formed by amalgamating attributes, global location data, and neighbourhood structure through an auto-encoder learning methodology. The Residual Capsule Network (RCN) model acquires these node embeddings as input to learn the feature representation of nodes and transforms the link prediction problem into a classification task. The suggested (AGA-GI) model undergoes comparison with various existing models in the realm of link prediction. These models include Attributes for Link Prediction (SEAL), Embeddings, Subgraphs, Dual-Encoder graph embedding with Alignment (DEAL), Embeddings and Spectral Clustering (SC), Deep Walk (DW), Graph Auto-encoder (GAE), Variational Graph Autoencoders (VGAE), Graph Attention Network (GAT), and Graph Conversion Capsule Link (G

关键词： Capsule network Ideal Anchor Point Link prediction Path Estimation Portion Path information function Residual module Shortest path distance

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IVIDNet: Intelligent iris vitality detection via weighted prediction score level fusion

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Multimedia Tools and Applications 2023年第29期82卷 45959-45981页

作者： Verma, Palak Selwal, Arvind Sharma, Deepika Department of Computer Science and Engineering Indian Institute of Technology Jammu PO-Nagrota Jammu181221 India Department of Computer Science and Information Technology Central University of Jammu Samba181143 India

Iris recognition is one of the most widely used human authentication mechanism that has gained huge popularity due to its security and efficiency. However, recently attackers have breached the security of these systems via synthetically generated fake iris traits using a variety of artefacts such as paper printouts, video attacks, and cosmetic lenses. To countermeasure these attacks, an anti-deception sub-system is instilled between sensor and feature extractor modules of the system. These sub-systems intelligently measure the vitality of the presented iris traits and are popularly known as iris vitality detection (IVID) or iris anti-spoofing techniques. Recently, the deep learning-based models are widely deployed for iris anti-spoofing approaches, but these methods result in additional training overhead as well as require larger training dataset. In this paper, we expound a novel iris vitality detection network (IVIDNet) that employs the robust features of two pre-trained deep convolutional neural networks (DCNN). The extracted features are then integrated via a new weighted score-level fusion technique. To demonstrates the efficacy of the proposed technique, experimental analysis is carried out on benchmark iris liveness detection Notre Dame 2017 and Notre Dame cosmetic lenses dataset (NDCLD) 2015. The proposed IVIDNet exhibits excellent performance in the known attack scenario with an average classification error rate (ACER) of 0.60%. Moreover, IVIDNet shows superior performance in the unseen attacks scenario (i.e. cross-database) with an ACER of 9.72%. In overall, the proposed model outperforms other similar iris anti-spoofing approaches and efficient in terms of computational overhead. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Biometrics

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Relating Muscle Activity and Mouse Sensitivity in FPS Game Players Using Surface EMG Signals 5

Relating Muscle Activity and Mouse Sensitivity in FPS Game P...

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5th International Conference on Biomedical engineering, IBIOMED 2024

作者： Prajapati, Vaibhav Turlapaty, Anish C. Sarma, Himangshu Ghorai, Mrinmoy Indian Institute of Information Technology Dept. of Electronics and Communication Engineering Sri City India Indian Institute of Information Technology Dept. of Computer Science and Engineering Sri City India

ISBN: (纸本)9798350389265

In the domain of first-person shooter (FPS) gaming, mouse sensitivity is crucial for performance. This paper presents an analysis showing the correlation between specific muscle activity and mouse movement. We analyzed mouse movements from twelve subjects with varying sensitivity levels. A new dataset electromyography analysis of human activity database 8 (EMAHA-DB8), is introduced which involved four mouse designs and two game modes: Medium (normal movement) and Hard (aggressive movement). Sensitivity settings were standardized using effective dots per inch (eDPI), calculated as Mouse DPI multiplied by in-game sensitivity. Hand muscle activity was measured using five Noraxon Ultium wireless surface electromyography (sEMG) sensors with Ag/AgCl electrodes. Various regression models, including Random Forest, Support Vector Regressor, K-Nearest Neighbors Regressor, MLP Regressor, and Gradient Boost Regressor, were used to predict eDPI from muscle movement patterns. Sequential feature selection identified the top ten features, highlighting the most significant muscles for predicting sensitivity. The results demonstrate that the Extensor Carpi Radialis (ECR) is the most active muscle, followed by the Extensor Digitorum (ED), while playing the chosen FPS game. These findings give insights for ergonomic and personalized equipment design to enhance gaming experience and performance. © 2024 IEEE.

关键词： Decision trees

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A Message Digest Based Secured APUF Model to Prevent Machine Learning Attacks 4

A Message Digest Based Secured APUF Model to Prevent Machine...

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4th International Conference on computer, Communication, Control and information technology, C3IT 2024

作者： Das, Prasenjit Giri, Chandan Computer Science and Engineering Academy of Technology Adisaptagram Hooghly India Indian Institute of Engineering Science and Technology Department of Information Technology Shibpur Howrah India

ISBN: (纸本)9798350389128

In the era of the Internet of Things (IoT), cloud computing huge amounts of data are generated by machines, humans and it is communicated over the internet. We need a stringent security to protect information as well as the devices also from the intruder. The electronic devices such as RFID, smart card and different sensors are used in different applications like smart automation and health monitoring but they are small and resource limited. The devices are placed in public places and exposed to the adversary for physical and cloning attacks. Hardware-based security using Physical unclonable function (PUF) overcomes such a problem but it is vulnerable to machine learning attacks with high precision. A variety of PUF designs have been proposed by the different researchers to provide energy-efficient, cost-effective and machine learning attack re-sistant such as Arbiter PUF(APUF), double APUF, XOR-PUF and multi-PUF(M-PUF) but either they have low reliability or they are not fully machine learning attack resistant. In this paper, we propose a message digest based APUF (MD-PUF) model which is energy-efficient, cost-effective and better machine learning attack resistant compare to other APUF models. We have added a message digest module which changes the input challenges to an arbitrary random challenges its make the intruder confuse to predict the response for unknown challenges and then it is fed into the APUF. We design the message digest module using columnar transposition which requires a very less amount hardware cost of n-bit registers for n-stages PUF. We mathematically prove that the prediction probability of MD-PUF is less than the classical APUF. We evaluate and compare the quality metrics of MD-PUF and APUF. The prediction accuracy of the proposed model is less compare to APUF against the machine learning algorithm LR, SVM and using evolutionary strategy CMA-ES. © 2024 IEEE.

关键词： Smart cards

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Crop Yield Prediction Using Multimodal Meta-Transformer and Temporal Graph Neural Networks

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 545-553页

作者： Sarkar, Somrita Dey, Anamika Pradhan, Ritam Sarkar, Upendra Mohan Chatterjee, Chandranath Mondal, Arijit Mitra, Pabitra Indian Institute of Technology Kharagpur Centre for Computational and Data Sciences Kharagpur721302 India Indian Institute of Technology Kharagpur Department of Industrial and Systems Engineering Kharagpur721302 India Indian Institute of Technology Kharagpur Department of Agricultural and Food Engineering Kharagpur721302 India Indian Institute of Technology Patna Department of Computer Science and Engineering Patna801106 India Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India

Crop yield prediction is a crucial task in agricultural science, involving the classification of potential yield into various levels. This is vital for both farmers and policymakers. The features considered for this task are diverse, including weather, soil, and historical yield data. Recently, plant images captured in different modalities, such as red-green-blue, infrared, and multispectral bands, have also been utilized. Most of these data are inherently temporal. Integrating such multimodal and temporal data is advantageous for yield classification. In this work, a deep learning framework based on meta-transformers and temporal graph neural networks has been proposed to achieve this goal. Meta-Transformers allow the modeling of multimodal interactions, while temporayel graph neural networks enable the utilization of time sequences. Experimental results on the publicly available EPFL multimodal dataset demonstrate that the proposed framework achieves a high classification accuracy of nearly 97%, surpassing other state-of-the-art models, such as long short-term memory networks, 1-D convolutional neural networks, and Transformers. In addition, the proposed model excels in accuracy metrics, with a precision of approximately 98%, an F1-Score of 91%, and a recall of 94% in crop yield prediction. © 2024 IEEE.

关键词： Distribution transformers

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