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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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The EmpkinS-EKSpression Reappraisal Training Augmented With Kinesthesia in Depression: One-Armed Feasibility Study

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JMIR Formative Research 2025年 9卷 e65357页

作者： Keinert, Marie Schindler-Gmelch, Lena Rupp, Lydia Helene Sadeghi, Misha Richer, Robert Capito, Klara Eskofier, Bjoern M. Berking, Matthias Department of Clinical Psychology and Psychotherapy Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Machine Learning and Data Analytics Lab Department of Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Translational Digital Health Group Institute of AI for Health Helmholtz Zentrum Muenchen Neuherberg Germany

Background: Harboring dysfunctional depressogenic cognitions contributes to the development and maintenance of depression. A central goal of cognitive behavioral therapy (CBT) for depression is to invalidate such cognitions via cognitive reappraisal (CR). However, relatively low remission rates and high dropout rates in CBT demonstrate the need for further improvement. Potentially, the effects of CBT could be enhanced by addressing not only dysfunctional depressogenic cognitions but also body states associated with depression. This may be done, for example, by systematically pairing the invalidation of depressogenic cognitions with the performance of antidepressive kinesthesia. Objective: This study aimed to examine the feasibility and clinical potential of a smartphone-based cognitive restructuring task that required users to deliberately perform antidepressive kinesthesia in conjunction with the rejection of depressogenic statements and the affirmation of antidepressive statements. This feasibility study was conducted as a precursor to a large-scale randomized controlled trial. Methods: In total, 10 healthy participants engaged in a single 90-120-minute session of smartphone-based CR training. During the training, they completed 2 phases in which they were required to reject 20 depressogenic and affirm 20 antidepressive statements, respectively. Diagnostic assessments were conducted 1 week (T1) before and directly prior (T2) to the training, and again directly posttraining (T3) and at a 2-week follow-up posttraining (T4). Feasibility outcomes assessed at T3 included intervention safety recorded by study therapists, compliance, technical feasibility, usability assessed using the Short Version of the User Experience Questionnaire (UEQ-S), and acceptability assessed using the UEQ-S and self-developed items. Preliminary clinical potential was evaluated via single-item ratings of current depressed and positive mood assessed continuously during the training. Feasibility

关键词： cognitive reappraisal depression facial expression kinesthesia mobile phone smartphone-based intervention

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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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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

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Estimating fish weight growth in aquaponic farming through machine learning techniques 3

Estimating fish weight growth in aquaponic farming through m...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Kumar, Purushottam Tiwari, Pranav Srinivasulu Reddy, U. Machine Learning & Data Analytics Lab CoE in Artificial Intelligence National Institute of Technology Trichy India Indian Institute of Information Technology Tiruchirappalli Computer Science and Engineering Trichy India National Institute of Technology Machine Learning & Data Analytics Lab Department of Computer Applications Trichy India

ISBN: (纸本)9798350338607

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won't be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of Artificial intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions © 2023 IEEE.

关键词： Fish

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Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

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Journal of Ambient intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： A. Dharshan Purushottam Kumar S. Ravimaran U Srinivasulu Reddy Department of Artificial Intelligence and Data Science Saranathan College of Engineering Trichy India CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

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CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation

arXiv

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arXiv 2023年

作者： Sun, Shoukun Xian, Min Xu, Fei Capriotti, Luca Yao, Tiankai Machine Intelligence and Data Analytics Lab Department of Computer Science University of Idaho United States Idaho National Laboratory United States

The click-based interactive segmentation aims to extract the object of interest from an image with the guidance of user clicks. Recent work has achieved great overall performance by employing feedback from the output. However, in most state-of-theart approaches, 1) the inference stage involves inflexible heuristic rules and requires a separate refinement model, and 2) the number of user clicks and model performance cannot be balanced. To address the challenges, we propose a click-based and mask-guided interactive image segmentation framework containing three novel components: Cascade-Forward Refinement (CFR), Iterative Click Loss (ICL), and SUEM image augmentation. The CFR offers a unified inference framework to generate segmentation results in a coarse-to-fine manner. The proposed ICL allows model training to improve segmentation and reduce user interactions simultaneously. The proposed SUEM augmentation is a comprehensive way to create large and diverse training sets for interactive image segmentation. Extensive experiments demonstrate the state-of-the-art performance of the proposed approach on five public datasets. Remarkably, our model reduces by 33.2%, and 15.5% the number of clicks required to surpass an IoU of 0.95 in the previous state-of-the-art approach on the Berkeley and DAVIS sets, respectively. © 2023, CC BY.

关键词： Image segmentation

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Gaitmap - An Open Ecosystem for IMU-based Human Gait Analysis and Algorithm Benchmarking

TechRxiv

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TechRxiv 2023年

作者： Küderle, Arne Ullrich, Martin Roth, Nils Ollenschläger, Malte Ibrahim, Alzhraa Moradi, Hamid Richer, Robert Seifer, Ann-Kristin Zürl, Matthias Sîmpetru, Raul Herzer, Liv Prossel, Dominik Kluge, Felix Eskofier, Bjoern Friedrich-Alexander-Universität Erlangen-Nürnberg Machine Learning and Data Analytics Lab Department of Artificial Intelligence in Biomedical Engineering Germany

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Estimating fish weight growth in aquaponic farming through machine learning techniques

Estimating fish weight growth in aquaponic farming through m...

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Intelligent Technologies (CONIT), International Conference on

作者： Purushottam Kumar Pranav Tiwari U Srinivasulu Reddy CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Computer Science and Engineering Indian Institute of Information Technology Tiruchirappalli Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won’t be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of Artificial intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions

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