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1st International Conference on data science and artificial intelligence, DSAI 2023

作者： Soingern, Nutapol Sinsamersuk, Akraradet Chatnuntawech, Itthi Silpasuwanchai, Chaklam School of Engineering and Technology Data Science and Artificial Intelligence Asian Institute of Technology Pathum Thani Thailand National Science and Technology Development Agency National Nanotechnology Center Pathum Thani Thailand

ISBN: (纸本)9789819979684

Motor imagery classification using electroencephalogram (EEG) signals is an important research topic that has been extensively studied in the field of brain-computer interfaces (BCIs). However, due to the limited amount of available data, overfitting is a common problem, especially when using a deep-learning classifier. One way to address this is by performing data augmentation. In this paper, we investigate the efficacy of the diffusion model as a data augmentation method for motor imagery classification. We evaluated the diffusion method by comparing it with commonly-used EEG data augmentation techniques namely such as Noise Addition, Fourier Transform Surrogates, Frequency Shift, and SmoothTimeMask. The result shows that the diffusion method outperformed other methods in terms of classification accuracy by 17.49%. The Kullback-Leibler (KL) divergence is used for assessing the similarity between the training set (with and without augmentation) and validation set, thus showing the effectiveness of the diffusion approach compared to other techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Electroencephalography

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Abnormal Load Patterns Detection in Smart Grids Using Temporal Convolutional Neural Network Based Gated Recurrent Units Networks with Multi-Head Temporal Attention 4

Abnormal Load Patterns Detection in Smart Grids Using Tempor...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Balassem, Zayd Srikanteswara, Ramya Esakki Madura, E. Anjaneyulu, Madugula Devi, M. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science India Gokaraju Rangaraju Institute of Engineering and Technology Department of Artificial Intelligence and Machine Learning Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350352931

In recent era, the advancement in smart grid technologies and the integration of renewable energy sources have modernized the power distribution landscape. The efficiency and stability of overall system is ensured by the controlling of power converters. In existing, Support Vector Machine (SVM) is utilized but it assumes the normal data follows Gaussian distribution which is not be the same for smart grid data. In this research, Temporal Convolutional Neural Network based Gated Recurrent Units Networks with Multi-Head Temporal Attention (TCN-GRU-MTA) is proposed. Initially, the input signals are considered from Smart Grid Stability (SGS) dataset. Then, the signals are further preprocessed with autoencoders to reduce noise and to improve quality of signals. The Siamese Neural Networks (SNN) feature extractor efficiently learns relevant features from smart grid data which are indicative of abnormal behavior. After that, the SNN is introduced for extracting features from preprocessed data. Next, the proposed TCN-GRU-MTA is utilized to detect the abnormal load patterns in smart grid networks. From the results, the proposed-established better results including accuracy (99.10%), precision (89.98%), recall (99.05%) and F1-score (95.20%) when compared to existing Quadratic SVM. © 2024 IEEE.

关键词： Smart power grids

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Deformable Dual Graph Aggregation Transformer Convolutional Networks with Spider Wasp Optimizer for Ovarian Tumor Classification Using Magnetic Resonance Imaging

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Biomedical Materials and Devices 2025年 1-19页

作者： Shanmugaveni, V. Jotheeswari, M. Abarnaswara, R. VijayaKumar, M. Manojanani, A. Department of Computer Science and Engineering (Cyber Security) Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Computer Science and Engineering PSNA College of Engineering and Technology Tamil Nadu Dindigul India Department of Artificial Intelligence & Data Science Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Computer Science and Engineering RVS Technical Campus Tamil Nadu Coimbatore India

The diagnosis of Ovarian Tumor (OT) remains a significant challenge as there is presently no practical non-invasive technique to determine true benign or malignant lesions before treatment. This study proposes a unique Deformable Dual Graph Aggregation Transformer Convolutional Network (DeDGATCNet) with Spider Wasp Optimizer (SWO) for the classification of ovarian tumors acquired from magnetic resonance imaging (MRI). In contrast to traditional deep learning models, DeDGATCNet + SWO directly integrates bending graph-based forming transformer convolutional learning and improves representation for better classification. The SWO is combined to optimize the parameters of the network, thereby improving its performance. The procedures include pre-processing the MRI data from the study of ovarian tumor TCGA-OV datasets with an Adaptive Bilateral Texture Filter (ABTF) for removing artifacts and normalization of intensity, accompanied by a segment using a Masked-Attention Mask Transformer (MAMT). Feature extraction is achieved through a One-Dimensional Quaternion Discrete Fourier Transform (1D-QDFT), which captures features from both the spatial and frequency domains. DeDGATCNet + SWO performance is way ahead and sets a new accuracy record of 99.9% for state-of-the-art models. This research introduces a new type of hybridization between deformable graph transformers, quaternion-based spectral analysis, and nature-inspired optimization techniques, setting non-invasive OT at a new height for classification and improving early diagnosis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Adaptive bilateral texture filter Deformable dual graph aggregation transformer convolutional networks Masked-attention mask transformer One-dimensional quaternion discrete fourier transform

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GridStemLayNet - A Document Layout Understanding Model 2

GridStemLayNet - A Document Layout Understanding Model

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2nd International Conference on Computational intelligence and Network Systems, CINS 2024

作者： Kumar, Sanjith R. Raj, Pranav H. Thomas, Kevin Lemuel R. Karthi, M. Kalpana, B.N. Artificial Intelligence and Data Science St. Joseph’s Institute of Technology Chennai India Computer Science and Engineering St. Joseph’s College of Engineering Chennai India Information Technology St. Joseph’s Institute of Technology Chennai India

ISBN: (纸本)9798331504106

Document Layout Understanding and Analysis solves the real-time problem of observing and contextualizing Layout and textual characteristics of a document. Document pre-trained models have proven to be very effective on various tasks in Document AI. However, for the document layout analysis (DLA) task, existing document pre-trained models, even those pretrained in a multi-modal fashion, usually are large and rely on complex visual features and just try to segment or contextualize high-level layouts but does not capture low level textual features. This research proposes a model that encompasses both layout features and textual features. To fully leverage multi-modal information, it uses Global Invariance and exploits efficient training techniques to learn better representation for DLA and textual properties. This research proposes a vision transformer based approach with Stem Attention mechanism. For the purpose of this research a new dataset called DTFiL will also be introduced for this specific problem. ©2024 IEEE.

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Investigation of Control Algorithm for Electromechanical Vehicle Active Anti-Roll Bar

Investigation of Control Algorithm for Electromechanical Veh...

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2023 International Conference on Electrical engineering and Informatics, ICEEI 2023

作者： Zhang, Zien Rahman, Abdul Hadi Abd Zulkarnain, Noraishikin Center for Artificial Intelligence Technology Faculty of Information Science & Technology Selangor Bangi Malaysia Faculty of Engineering & Built Environment Department of Electrical Electronic & Systems Engineering Selangor Bangi Malaysia

ISBN: (纸本)9798350381290

The present paper aims to address the issue of improving the roll stability of vehicles by employing an active anti-roll bar (AARB) controller. The AARB controller is established using MATLAB/SIMULINK based on the principle of Proportional-Integral-Derivative (PID) Controller, and the CARSIM software is used to build a testing environment. The evaluation is conducted through specific indicators of vehicle roll stability. A comparative simulation was conducted on the control system by changing the vehicle speed. The joint simulation of SIMULINK and CARSIM software verified the feasibility and correctness of the established model and control system. The simulation results analyzed using MATLAB software show that the AARB control system can improve the vehicle's stability. The significance of the AARB control algorithm for other active safety controllers in vehicles was also briefly explained. We highlight the future research direction of the control algorithm for the AARB controller on the importance and impact of intelligent algorithms on future research. © 2023 IEEE.

关键词： Controllers

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Multi-View Dynamic Kernelized Evidential Clustering

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2435-2450页

作者： Jinyi Xu Zuowei Zhang Ze Lin Yixiang Chen Weiping Ding the Software Engineering Institute East China Normal UniversityShanghai 200062 Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China IEEE the School of Automation Northwestern Polytechnical UniversityXi’an 710072China the School of Artificial Intelligence and Computer Science Nantong UniversityNantong 226019 the Faculty of Data Science City University of MacaoMacao 999078China

It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering *** solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping *** offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective *** on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.

关键词： Evidential clustering imprecision characterizing kernel technique multi-view clustering

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Apple Leaf Disease Identification Using Resnet50

Apple Leaf Disease Identification Using Resnet50

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Karthigaiveni, S. Archana, R. Ravi, Vasanth Khatoon, P Salma Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India St. Joseph's Institute of technology Department of Artificial intelligence and Data science OMR Chennai India Jain Deemed to be University Faculty of Engineering and Technology Department of Computer Science and Engineering Jain Global Campus India Mlr Institute of Technology Department of CSE-DS Hyderabad India

ISBN: (纸本)9798331543617

Apple leaf disease is a primary issue limiting apple yield and quality. The standard diagnostic approach requires considerable time for illness detection, causing farmers to frequently miss the optimal period for prevention and treatment. The detect of leaf diseases through leaf imagery is a vital study area in computer vision, with the primary objective being to devise an effective method for representing images of sick leaves. The efficient diagnosis and categorization of plant diseases constitutes a significant research aim in agriculture. This paper proposes a novel deep learning, ResNet. To evaluate the efficacy of the ResNet model, a number of comparative experiments were performed with leading transfer learning methodologies. This demonstrates that the proposed method is both possible and successful. © 2024 IEEE.

关键词： CNN Diseases Leaf Resnet Transfer Learning

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Molecular simulations for carbon dioxide capture in silica slit pores

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Materials Today: Proceedings 2023年 102卷 194-202页

作者： Kumar, Mukesh Sengupta, Angan Kummamuru, Nithin B. Department of Chemical Engineering Indian Institute of Technology Rajasthan Jodhpur 342030 India Sustainable Energy Air & Water Technology Department of Bioscience Engineering University of Antwerp Groenenborgerlaan 171 Antwerpen 2020 Belgium School of Artificial Intelligence and Data Science Indian Institute of Technology Rajasthan Jodhpur 342030 India

In present work, we have performed the Grand Canonical Monte Carlo (GCMC) simulations to quantify CO2 capture inside porous silica at high operating temperatures of 673.15 K and 873.15 K;and over a operating pressure range of 500 kPa – 4000 kPa that are methane steam reforming process parameters. Related chemical potential values at these thermodynamic conditions are obtained from the bulk phase simulations in the Canonical ensemble in conjunction with Widom's insertion technique, where the CO2 has been accurately represented by TraPPE force field. Present structure of the porous silica is a single slit pore geometry of various heights (H = 20 Å, 31.6 Å, 63.2 Å and 126.5 Å), dimensions in which possible vapour-liquid equilibria for generic square well fluids has been reported in literature. Estimation of the pore-fluid interactions show a higher interaction between silica pore and adsorbed CO2 compared to the reported pore-fluid interactions between homogeneous carbon slit pore and adsorbed CO2;thus resulting in an enhancement of adsorption inside silica pores of H = 20 Å and H = 126.5 Å, which are respectively 3.5 times and 1.5 times higher than that in homogeneous carbon slit pores of same dimensions and at 673.15 K and 500 kPa. Estimated local density plots indicate the presence of structured layers due to more molecular packing, which confirms possible liquid-like and vapour-like phase coexistence of the supercritical bulk phase CO2 under confinement. © 2023

关键词： Carbon dioxide

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Prevention of Security Attacks at Wireless Network Layers using Machine Learning Techniques 18

Prevention of Security Attacks at Wireless Network Layers us...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Ramisetty, Sowjanya Ghantasala, G. S. Pradeep Gupta, Gaurav Sonia Kg Reddy College of Engineering & Technology Department of Computer Science and Engineering Hyderabad India Alliance University Department of Computer Science and Engineering Bengaluru India Shoolini University Yogananda School of Artificial Intelligence Computers and Data Sciences Solan India

ISBN: (纸本)9789380544519

Wireless sensor networks have great potential for use in flood control, weather forecasting systems, the military, and the healthcare industry. A WSN's nodes are connected to one another and share information. When sending data between nodes, data security is essential. In the world of networking, security is a top priority. data travels along a path when it is transferred from one location to another, increasing the likelihood that a rogue node would enter the network. Detecting malicious nodes within a network presents a formidable challenge, particularly due to external assaults on data packets during their transmission between nodes. Hackers take advantage of weaknesses to manipulate and alter data as it travels from the source node to the destination node. This paper looked at machine learning algorithm-based preventive measures and layer-level security challenges. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Machine Learning Security Security attacks Wireless Sensor Networks

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Differentially Private Spiking Variational Autoencoder 27th

Differentially Private Spiking Variational Autoencoder

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yadav, Srishti Pundhir, Anshul Goyal, Tanish Raman, Balasubramanian Kumar, Sanjeev Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Roorkee India Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India Department of Mathematics Indian Institute of Technology Roorkee Uttarakhand Roorkee India

ISBN: (纸本)9783031783531

Spiking Neural Networks (SNNs) are poised to lead the next generation of artificial intelligence, offering energy efficiency and performance on par with traditional neural networks. With these advantages, SNNs are finding widespread applications across various domains. One significant area of interest is image generation using deep learning models like Variational Autoencoders (VAE). However, like other deep learning models, SNNs demand substantial training data to achieve desired outcomes, raising concerns about data privacy. Our pioneering contribution is the introduction of a Differentially Private Spiking Variational Autoencoder (DP-SVAE) for image generation and reconstruction. DP-SVAE employs standard Differentially Private Stochastic Gradient Descent (DP-SGD) to ensure privacy preservation. Additionally, we have evaluated the models against various adversarial attacks to highlight the importance of differential privacy. We comprehensively analyze the proposed model through extensive experimentation across publicly available benchmark datasets. This pioneering study marks the first exploration of privacy considerations in SNN-based VAEs and will catalyze further research in this domain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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