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Noise filtering approach to improve handwritten digit recognition using customized CNN for Cerebral Palsy individuals

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The European Physical Journal Special Topics 2025年 1-19页

作者： Muthureka, K. Srinivasulu Reddy, U. Janet, B. Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology-Tiruchirappalli Tiruchirappalli India Centre of Excellence (CoE) in Artificial Intelligence National Institute of Technology-Tiruchirappalli Tiruchirappalli India Information Processing Lab Department of Computer Applications Tiruchirappalli India

Automatic recognition of handwritten digits in people with Cerebral Palsy (CP) is a serious challenge that requires advancements in data preparation for improved predictive accuracy. The disorganized digit pixel patterns caused by CP people’ innate motor coordination problems result in the classification errors due to the noise and outlier. Despite the fact that many handwritten digit databases for normal people are available, none expressly cater to those with shaky hands/CP dataset. This highlights the importance of an efficient pre-processing method for mitigating variances in CP individuals’ digit pixel patterns. A total of 43,000 images from 43 CP people were collected and organized into 10 different folders for data labeling. The authors used the K-Means Based Noise Removal (KMNR) approach to remove noise and outliers from a CP and tested it on a customized CNN. The KMNR model achieved an accuracy of 98.86%, compared to 93.54% without it. The F1-score values for state-of-the-art models without KMNR ranged from 85.00% to 97.00%, but after integrating KMNR, they increased to 92.00% to 98.00%. The precision values ranged from 85.00% to 97.00% in the absence of KMNR, but increased to 91.00% to 98.00% in the presence of KMNR. The recall values ranged from 85.00% to 97.00% without KMNR, but increased to 87.00% to 98.00% with KMNR.

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Optical Character Recognition (OCR) in Handwritten Characters Using Convolutional Neural Networks to Assist in Exam Reader System 2

Optical Character Recognition (OCR) in Handwritten Character...

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2nd International Conference on Advancement in Computation and Computer Technologies, InCACCT 2024

作者： Lekshmy, P.L. Velmurugan, S. Kumari, Indra Kayalvili, S. Teja Sree, B. Karthik Kumar, P. LBS Institute of Technology for Women Department of Computer Science and Engineering Kerala India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Machine Learning Data Research Applied AI Daejeon Korea Republic of Department of Artificial Intelligence Tamilnadu Erode India S.R.K.R. Engineering College Department of Information Technology Andhra Pradesh Chinaamiram Bhimavaram India Coimbatore India

ISBN: (纸本)9798350371314

This work aimed to develop a character recognition method to facilitate the correction of answer cards in the Multiprova software through the development of a response card analysis flow that would culminate in the recognition of letters written by students and automatic correction of the tests. To isolate and identify the answers written on the answer cards, image segmentation techniques were used based on fixed marks printed on the cards. To recognize the letters and numbers written on the cards, trained three convolutional neural networks (for digits, letters and true or false). The results achieved (98.84% accuracy for digit CNN, 98.38% accuracy for letter CNN and 99.89% accuracy for true or false CNN) point out a great average success rate. © 2024 IEEE.

关键词： Convolutional neural networks

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Insect classification and detection in field crops using modern machine learning techniques

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Information Processing in Agriculture 2021年第3期8卷 446-457页

作者： Thenmozhi Kasinathan Dakshayani Singaraju Srinivasulu Reddy Uyyala Machine Learning and Data Analytics Lab Centre of Excellence in Artificial IntelligenceDepartment of Computer ApplicationsNational Institute of TechnologyTiruchirappalliTiruchirappalli 620015Tamil NaduIndia

The agriculture sector has an immense potential to improve the requirement of food and supplies healthy and nutritious *** insect detection is a challenging task for farmers as a significant portion of the crops are damaged,and the quality is degraded due to the pest *** insect identification has the drawback of requiring well-trained tax-onomists to identify insects based on morphological features *** were conducted for classification on nine and 24 insect classes of Wang and Xie dataset using the shape features and applying machine learning techniques such as artificial neural net-works(ANN),support vector machine(SVM),k-nearest neighbors(KNN),naive bayes(NB)and convolutional neural network(CNN)*** paper presents the insect pest detec-tion algorithm that consists of foreground extraction and contour identification to detect the insects for Wang,Xie,Deng,and IP102 datasets in a highly complex *** 9-fold cross-validation was applied to improve the performance of the classification *** highest classification rate of 91.5%and 90%was achieved for nine and 24 class insects using the CNN *** detection performance was accomplished with less com-putation time for Wang,Xie,Deng,and IP102 datasets using insect pest detection *** comparison results with the state-of-the-art classification algorithms exhibited considerable improvement in classification accuracy,computation time perfor-mance while apply more efficiently in field crops to recognize the *** results of classification accuracy are used to recognize the crop insects in the early stages and reduce the time to enhance the crop yield and crop quality in agriculture.

关键词： Crop pest classification Crop insect detection Image processing Machine learning Image segmentation

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Attention based 1-D CNN system for detection of Freezing of gait (FOG) in Parkinsons Patients

Attention based 1-D CNN system for detection of Freezing of ...

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International Conference on Computing and Networking technology (ICCNT)

作者： D Pradeep Kumar B J Sowmya Ashvin Vincent Aravindh Pandiyan Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bangalore India Machine Learning Engineer Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

One common clinical symptom seen in Parkinson’s disease (PD) patients is freezing of gait (FOG). It manifests as an irregular gait, marked by abrupt, involuntary stopping of movement during gait episodes. FOG entails a major negative effect on PD patients’ quality of life. In this work, we analyze the movement data that is collected using the lower-back 3D accelerometer worn by the patients during FOG episodes. By employing machine learning methods to classify this data, we aim to accurately detect FOG trends. In particular, we focus on Deep learning-based techniques, specifically an attention-based 1D Convolutional Neural Network (1D-CNN), due to their capacity to extract intricate features from unprocessed sensor input. Our results indicate that the proposed model achieves AUC scores of 0.9032 with respect to the TDCSFOG dataset and 0.8682 for the DeFOG dataset, for a combined average AUC of 0.8857. Mean Average Precision (mAP) scores for TDCSFOG and DeFOG reach 0.8388 and 0.779, respectively, for an average of 0.8089, showing the ability of the model to predict FOG occurrences over time. These findings suggest techniques based on Deep learning enhance FOG detection, allowing clinicians to more effectively tailor treatments and care strategies for Parkinson’s patients, thus enhancing their ability to move and overall well-being.

关键词： Deep learning Accelerometers Three-dimensional displays Accuracy Predictive models Feature extraction Transformers Convolutional neural networks Forecasting Diseases

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Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics 38

Probabilistic Decomposed Linear Dynamical Systems for Robust...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Yenho Mudrik, Noga Johnsen, Kyle A. Alagapan, Sankaraleengam Charles, Adam S. Rozell, Christopher J. Machine Learning Center Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States Coulter Dept. of Biomedical Engineering Emory University Georgia Institute of Technology United States Department of Biomedical Engineering Mathematical Institute for Data Science Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University United States

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model's ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. © 2024 Neural information processing systems foundation. All rights reserved.

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A CNN-Transformer for Classification of Longitudinal 3D MRI Images - A Case Study on Hepatocellular Carcinoma Prediction

arXiv

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arXiv 2025年

作者： Nolte, Jakob Guichelaar, Maureen M.J. Bouman, Donald E. van den Berg, Stéphanie M. Haeri, Maryam Amir Department of Gastroenterology and Hepatology Medisch Spectrum Twente Enschede Netherlands Department of Radiology Medisch Spectrum Twente Enschede Netherlands Department of Learning Data Analytics and Technology University of Twente Enschede Netherlands

Longitudinal MRI analysis is crucial for predicting disease outcomes, particularly in chronic conditions like hepatocellular carcinoma (HCC), where early detection can significantly influence treatment strategies and patient prognosis. Yet, due to challenges like limited data availability, subtle parenchymal changes, and the irregular timing of medical screenings, current approaches have so far focused on cross-sectional imaging data. To address this, we propose HCCNet, a novel model architecture that integrates a 3D adaptation of the ConvNeXt CNN architecture with a Transformer encoder, capturing both the intricate spatial features of 3D MRIs and the complex temporal dependencies across different time points. HCCNet utilizes a two-stage pre-training process tailored for longitudinal MRI data. The CNN backbone is pre-trained using a self-supervised learning framework adapted for 3D MRIs, while the Transformer encoder is pre-trained with a sequence-order-prediction task to enhance its understanding of disease progression over time. We demonstrate the effectiveness of HCCNet by applying it to a cohort of liver cirrhosis patients undergoing regular MRI screenings for HCC surveillance. Our results show that HCCNet significantly improves predictive accuracy and reliability over baseline models, providing a robust tool for personalized HCC surveillance. The methodological approach presented in this paper is versatile and can be adapted to various longitudinal MRI screening applications. Its ability to handle varying patient record lengths and irregular screening intervals establishes it as an invaluable framework for monitoring chronic diseases, where timely and accurate disease prognosis is critical for effective treatment planning. © 2025, CC BY-SA.

关键词： Patient treatment

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GeoAgriGuard: AI-Driven Pest and Disease Management with Remote Sensing for Global Food Security

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 409-422页

作者： Sharada, K. Choudhary, Shailee Lohmor Harikrishna, T. Dixit, Rinku Sharma Suman, Sanjay Kumar Ayyappa Chakravarthi, M. Bhagyalakshmi, L. Department of Computer Science and Engineering GITAM (Deemed to Be University) AP Visakhapatnam India Department of Artificial Intelligence and Machine Learning New Delhi Institute of Management Delhi India Department of AIML Annamacharya University Andhra Pradesh Rajampet India Department of ECE St. Martin’s Engineering College Telangana Secunderabad India Departmentt of CSE-Data Science KSR Institute of Technology and Science KKR & AP Guntur India Department of ECE Rajalakshmi Engineering College TN Chennai India

Agricultural productivity is increasingly threatened by the growing frequency and severity of pest and disease outbreaks, exacerbated by climate change and evolving environmental conditions. The GeoAgriGuard AI-Driven Pest and Disease Management System integrates remote sensing technologies with advanced AI models to address pest and disease outbreaks in agriculture. Utilizing multi-spectral and hyper-spectral satellite and drone imagery, the system processes crop health data through comprehensive preprocessing steps, including radiometric, geometric, and atmospheric corrections. Vegetation indices such as NDVI and NDWI further enhance the data quality, ensuring accurate detection of crop stress. A hybrid deep learning ensemble, combining models like ResNet, Transformer, DenseNet, and AutoEncoders, processes this data to predict and classify pest and disease occurrences. The proposed model achieved a remarkable accuracy of 97.81%, significantly outperforming existing models such as SVM (82.45%) and CNN (90.12%). By integrating these AI predictions with GIS-based spatial analysis, the system generates real-time risk maps, enabling farmers to take timely and localized actions. Performance metrics, including precision (96.72%), recall (95.83%), and F1-score (96.27%), demonstrate the system’s reliability in real-world applications. GeoAgriGuard provides a scalable, data-driven solution for enhancing crop protection and improving global food security. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： AI-driven agriculture Crop disease Deep learning Food security GIS NDVI Pest detection Precision agriculture Remote sensing Satellite imagery

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An Analysis of Light Weight Symmetric Encryption Algorithms for Secure data Transmission in IoT

An Analysis of Light Weight Symmetric Encryption Algorithms ...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Sanjay Kumar C. S. Pillai Department of CSE (Artificial Intelligence & Machine Learning) Don Bosco Institute of Technology Bengaluru India Department of CSE-Data Science ACS College of Engineering Bengaluru India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

The Internet of Things (IoT) consists of a network of resource-constrained devices, sensors, and machines that are interconnected and communicate via the internet. However, these devices often struggle while handling complex attacks during data encryption and after the encrypted data is transmitted. Cipher algorithms are therefore critical to the data security process because they handle the growing complexities in the applications of data environments. This review aims to present the most effective and efficient symmetric cipher algorithms, which use the same components as the ciphers. It demonstrates how the current cipher has developed into a lightweight encryption by lowering the number and impact of each component. Thus, it becomes clearer how the strength of these components varies between symmetric and asymmetric ciphers dependent on their size. Finally, several efficient lightweight symmetric cipher algorithms are selected, and the security, performance, and resource needs of each algorithm's components are reviewed.

关键词： Ciphers Reviews Encryption Sensors Complexity theory Internet of Things data communication Computational intelligence

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Identifying general mechanism shifts in linear causal representations 24

Identifying general mechanism shifts in linear causal repres...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Tianyu Chen Kevin Bello Francesco Locatello Bryon Aragam Pradeep Ravikumar Department of Statistics and Data Sciences University of Texas at Austin Booth School of Business University of Chicago and Machine Learning Department Carnegie Mellon University Institute of Science and Technology Austria Booth School of Business University of Chicago Machine Learning Department Carnegie Mellon University

ISBN: (纸本)9798331314385

We consider the linear causal representation learning setting where we observe a linear mixing of d unknown latent factors, which follow a linear structural causal model. Recent work has shown that it is possible to recover the latent factors as well as the underlying structural causal model over them, up to permutation and scaling, provided that we have at least d environments, each of which corresponds to perfect interventions on a single latent node (factor). After this powerful result, a key open problem faced by the community has been to relax these conditions: allow for coarser than perfect single-node interventions, and allow for fewer than d of them, since the number of latent factors d could be very large. In this work, we consider precisely such a setting, where we allow a smaller than d number of environments, and also allow for very coarse interventions that can very coarsely change the entire causal graph over the latent factors. On the flip side, we relax what we wish to extract to simply the list of nodes that have shifted between one or more environments. We provide a surprising identifiability result that it is indeed possible, under some very mild standard assumptions, to identify the set of shifted nodes. Our identifiability proof moreover is a constructive one: we explicitly provide necessary and sufficient conditions for a node to be a shifted node, and show that we can check these conditions given observed data. Our algorithm lends itself very naturally to the sample setting where instead of just interventional distributions, we are provided datasets of samples from each of these distributions. We corroborate our results on both synthetic experiments as well as an interesting psychometric dataset. The code can be found at https://***/TianyuCodings/iLCS.

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The Role of Blockchain technology in Supply chain Management for data Security 3

The Role of Blockchain Technology in Supply chain Management...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Birari, Dipika R. Rathod, Vijay U. Dandavate, Aarti Shelke, Nilesh Kumar, Ranjit Kulkarni, Masira M. S Army Institute of Technology Department of Information Technology Pune India G H Raisoni College of Engineering and Management Department of Artificial Intelligent and Machine Learning Wagholi Pune India Dhole Patil College of Engineering Department of Computer Engineering Pune India Symbiosis International Deemed University Symbiosis Institute of Technology Department of Computer Science and Engineering Nagpur Campus Pune India Ajeenkya D Y Patil University Department of Computer Engineering Pune India G H Raisoni College of Engineering and Management Department of Cyber Security and Data Science Wagholi Pune India

ISBN: (纸本)9798350354348

In a traditional supply chain system, the process of producing raw materials so that a product may be delivered to a customer is a manual operation with insufficient data and transaction security. The entire process is time consuming and requires a lot of effort. The undivided process is generally ineffective and unreliable for consumers. The data security, authenticity, the inclusion of will significantly improve the time management and transaction procedures of conventional supply chain management systems. data is shielded from illegal access via the ground-breaking, decentralized technology known as blockchain. After smart contracts are put into place, the complete supply chain management (SCM) will be satisfied with the customer. When the mediator is contracted, which is possible in various ways, the plan becomes more reliable. This article places particular emphasis on lowering third party involvement in the supply chain system and enhancing data security. Conventional supply chain management techniques have several serious shortcomings. Some of them include a lack of traceability, difficulties maintaining product safety and quality, a failure to track and manage inventories in stores and warehouses, escalating supply chain costs, and so on. The reduction of third-party involvement in the supply chain system and the improvement of data security are the main goals of this article. This enhances process accessibility, effectiveness, and timeliness overall. They will feel safer throughout the payment procedure, which is the main benefit. Yet in this investigation, a smart contract and a peer-to-peer encrypted system were both used. There are a few additional features as well. © 2024 IEEE.

关键词： Smart contract

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