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

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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Thai Conversational Chatbot Classification Using BiLSTM and data Augmentation 1st

Thai Conversational Chatbot Classification Using BiLSTM and ...

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1st International Conference on data Science and Artificial Intelligence, DSAI 2023

作者： Lhasiw, Nunthawat Tanantong, Tanatorn Sanglerdsinlapachai, Nuttapong Thammasat Research Unit in Data Innovation and Artificial Intelligence Department of Computer Science Faculty of Science and Technology Thammasat University Pathum Thani Thailand Strategic Analytics Networks with Machine Learning and AI Research Team National Electronics and Computer Technology Center Pathum Thani Thailand

ISBN: (纸本)9789819979684

Chatbot platforms, e.g., Facebook and Line, have revolutionized human interaction in the digital age. In order to develop an automatic chatbot classification, there are several challenges especially for Thai chat messages. Conversational messages are usually short and ambiguous. Therefore, it is difficult to find a dataset for constructing an effective classification model. To address the limited size of the dataset, data augmentation techniques can be possibly applied. data augmentation involves generating synthetic messages by applying various transformations to existing data samples while preserving their original meaning. In this study, the size and diversity of the dataset is increased by two methods, i.e., text augmentation using word2vec from Thai2Fit and English-Thai machine translation models proposed by VISTEC. Based on the augmented messages, a Deep learning technique, BiLSTM, is used to construct a chatbot classification model. The experimental obtained results demonstrate that data augmentation can help to increase the classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Classification (of information)

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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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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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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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Training for the Unexpected: Enhancing Driver Preparedness Through Hazard Awareness

SSRN

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SSRN 2025年

作者： Kuipers, Jorrit Mulder, Max Voskes, Maran Green Dino BV Netherlands Department of Control & Operations Faculty of Aerospace Engineering Delft University of Technology Netherlands Department of Learning Data Analytics and Technology Faculty of Behavioural Management and Social Sciences University of Twente Netherlands

BackgroundCar driving education in the Netherlands is transforming, and a national curriculum has been introduced to produce safer drivers. This National Curriculum Driver Training B proposes using driving simulators to support practical training and includes hazard examinations to reinforce safe driving, which will be obligatory in Europe. This study aimed to deepen the understanding of the relationships between hazard training and testing on driving simulators and supervised (pre-licensed) and unsupervised (post-licensed) driving. This study builds on Kuipers, De Winter, and Mulder's (2023) research, investigating the relationships between personal characteristics, pre-licence-accompanied driving, self-reported post-licence driving behaviour, and driving performance scores during simulator lessons. Methods We comprehensively analysed simulator data and insights from a questionnaire completed by Dutch car drivers (total n = 2,879) who underwent simulator training between 2008 and 2023. We compared the driving performance of respondents who received hazard training (HT) with the performance of a control group who did not receive HT training in four driving phases: simulator training, on-road training, examination, and post-licensed driving. ResultsWe found distinct differences in performance. Simulator scores, on-road training hours, and test attempts differ significantly. HT increased practical driving education efficiency by 12.4% and reinforced the development of a safe driving style that educational experts recognised and rewarded. In contrast to this positive effect during education, we found no significant effects when driving unsupervised. Results show that whereas drivers think they are sufficiently proficient after obtaining their license, a real improvement in safety performance becomes visible after the first twelve months. This confirms the general finding that young drivers are the most vulnerable to accident involvement in the first year of driving. Re

关键词： Curricula

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Facial Expression Recognition Using Multi-Block Deep CNN 5

Facial Expression Recognition Using Multi-Block Deep CNN

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Priyadarshini, V. Srinivasulu Reddy, U. Reddy Chirra, Venkata Rami Mrudula, M. Suneetha, M. National Institute of Technology Dept. of Computer Science & Engineering Andhra Pradesh India Machine Learning & Data Analytics Lab National Institute of Technology Dept. of Computer Applications Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Malla Reddy College of Engineering Department of CSE Telangana India Independent Researcher Amaravati India

ISBN: (纸本)9798331528539

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Stroke Home Rehabilitation Approach Using Mobile Application Based on PostNet Machine learning Model 23

Stroke Home Rehabilitation Approach Using Mobile Application...

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7th International Conference on Medical and Health Informatics, ICMHI 2023

作者： Das, Utpal Chandra Le, Ngoc Thien Benjapolakul, Watit Vitoonpong, Timporn Pluempitiwiriyawej, Charnchai Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Chulalongkorn University Bangkok10330 Thailand Department of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

ISBN: (纸本)9798400700712

Stroke is a significant cause of mortality and disability globally, with its occurrence in the human brain and motor function being linked to various parts of the human body. Stroke victims often experience disabilities or mobility problems in affected body parts, either on one or both sides of the body. Physiotherapy exercises are the primary treatment and medication for stroke patients, necessitating daily monitoring by a physiotherapist. However, this approach is expensive, and rehabilitation centers and physicians are scarce. Numerous research studies have been conducted to address this issue from various perspectives. This study proposes a Convolutional Neural Network (CNN) based pose net machine learning (ML) model for stroke home rehabilitation using pose detection and classification with a skeleton base model and human pose estimation drawing. We trained our ML model using different human pose images for this model. The tested accuracy of our CNN model is 100% for our exercise pose image test case. Later we build and mobile application for remote rehabilitation. Tested the application with ten different subjects, achieving a 98% for elbow extension, and in the case of elbow flexion and Normal position of balancing both sides, all the persons achieved a 100 % accuracy rate under a laboratory environment. © 2023 ACM.

关键词： Patient treatment

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CROSS-technology GENERALIZATION IN SYNTHESIZED SPEECH DETECTION: EVALUATING AST MODELS WITH MODERN VOICE GENERATORS

arXiv

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arXiv 2025年

作者： Ustinov, Andrew Yordanov, Matey Kuchma, Andrei Bychkov, Mikhail Department of Machine Learning and Data Analytics ITMO University Russia AI Innovation Hub ANTEI Limited Hong Kong Department of Integrative Systems and Design Hong Kong University of Science and Technology AI Innovation Hub ANTEI Limited Hong Kong

This paper evaluates the Audio Spectrogram Transformer (AST) architecture for synthesized speech detection, with focus on generalization across modern voice generation technologies. Using differentiated augmentation strategies, the model achieves 0.91% EER overall when tested against ElevenLabs, NotebookLM, and Minimax AI voice generators. Notably, after training with only 102 samples from a single technology, the model demonstrates strong cross-technology generalization, achieving 3.3% EER on completely unseen voice generators. This work establishes benchmarks for rapid adaptation to emerging synthesis technologies and provides evidence that transformer-based architectures can identify common artifacts across different neural voice synthesis methods, contributing to more robust speech verification systems. © 2025, CC BY-NC-ND.

关键词： Spectrographs

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A Smart Electronic Wheelchair Design Using Artificial Intelligence and Intelligent Sensors Association 2

A Smart Electronic Wheelchair Design Using Artificial Intell...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Balamurugan, R. Kanchana, D. Anni Princy, B. Jaisudha, J. Malathi, P. Glory, E. Ajk College of Arts and Science Department of Artificial Intelligence and Machine Learning Tamil Nadu Coimbatore India Srm Institute of Science and Technology Department of Computer Applications Tamil Nadu Chennai India Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Tamil Nadu Chennai India Simats Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India Simats Department of Biosciences Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350306415

Since the beginning of the seventeenth century, wheelchairs have been used to transport hospital patients and those with mobility impairments. The power of people is what spreads wheelchairs. Wheelchair users propel themselves forward using their upper bodies or, more commonly, require the assistance of another person to propel them. In this work, we examine how to build an autonomous electric wheelchair for the disabled at the lowest possible cost without sacrificing quality of design or control. This research presents the planning and development of an AI-enhanced electric wheelchair that can be controlled automatically to climb stairs, making it accessible to those with mobility impairments. The chair's controls allow the user to move it in any direction and raise the landing and staircases to varying heights. The chair's DC power source (battery used) may be recharged, allowing it to function for an extended period of time. In addition, a safety system was created that monitors and provides feedback on the user's pulse rate, as well as the wheelchair's functioning state (including acceleration, climate, and inclination position). This proposed AI based Wheelchair (AIWC) design is suitable for any aged people and the proposed model is cross-validated with the conventional wheelchair design using mobile based operations handling system called Mobile-Wheelchair (MWC) to test the efficiency of the proposed design in clear manner. © 2024 IEEE.

关键词： Accelerator AI Artificial Intelligence Batteries DC Power Intelligent Sensors Mobile-Wheelchair MWC Wheelchair

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