Learning Classifiers Research Articles

Opportunities and limits of image-based plant stress phenotyping: detecting plant salt stress status using machine learning techniques

Abstract Plant stress phenotyping is a useful tool to facilitate targeted interventions and optimize management practices of plants growing in stressful conditions. However, various technical challenges still need to be overcome, though, and there is a clear need for investigating functional traits that can be used as a proxy for stress prediction, especially for abiotic stresses. This experimental work leveraged machine learning classification models to detect salt stress in two populations of a non-model species, combining image-based approaches (i.e. both manual and automated) and minimal morpho-physiological/biochemical analyses. A small set of specific features, combining malondialdehyde content with other non-destructive image-derived traits, such as Chroma Difference and Chroma Ratio indices, was able to distinguish non-stressed from stressed plants (2-class model; precision: 0.91) as well as stress intensity (3-class model; precision: 0.84). This flexible approach can be adapted to different plant developmental stages and leaf shape/morphology. In the future, the robustness and reliability of the models should be tested in other species and other abiotic stresses, such as drought.

Assessing large multimodal models for one-shot learning and interpretability in biomedical image classification.

Image classification plays a pivotal role in analyzing biomedical images, serving as a cornerstone for both biological research and clinical diagnostics. We demonstrate that large multimodal models (LMMs), like GPT-4, excel in one-shot learning, generalization, interpretability, and text-driven image classification across diverse biomedical tasks. These tasks include the classification of tissues, cell types, cellular states, and disease status. LMMs stand out from traditional single-modal classification approaches, which often require large training datasets and offer limited interpretability.

Predicting and Evaluating Water Quality using Machine Learning in Maharashtra, India

Water quality is critical for public health and environmental sustainability, necessitating effective monitoring to prevent contamination. In this study, we focus on predicting and evaluating water quality in Maharashtra, India, using machine learning techniques. Groundwater contamination in Maharashtra is a significant issue due to poor waste management, yet research in this area is limited. Traditional water quality monitoring methods involve complex calculations based on fixed parameters, which can lead to errors. This study aims to streamline the monitoring process by identifying the most significant features, thereby saving time, money, and energy. We calculated the Water Quality Index (WQI) using the Weighted Arithmetic Mean method, analyzing data from 2012 to 2022 from the National Water Monitoring Program in India. The analysis identified three key parameters, BOD, pH, and Fecal Coliform, as most correlated with the WQI. Machine learning techniques, including regression and classification, were employed to predict WQI and Water Quality Classification (WQC). The results indicate that Polynomial Regression and Ridge Regression achieved high accuracy in predicting the WQI, while the Decision Tree classifier excelled in WQC classification. This research demonstrates the potential of machine learning to enhance water quality monitoring, offering a cost-effective solution for managing water resources in Maharashtra.

Transformer-based representation learning and multiple-instance learning for cancer diagnosis exclusively from raw sequencing fragments of bisulfite-treated plasmacell-free DNA.

Early cancer diagnosis from bisulfite-treated cell-free DNA (cfDNA) fragments requires tedious data analytical procedures. Here, we present a deep-learning-based approach for early cancer interception and diagnosis (DECIDIA) that can achieve accurate cancer diagnosis exclusively from bisulfite-treated cfDNA sequencing fragments. DECIDIA relies on transformer-based representation learning of DNA fragments and weakly supervised multiple-instance learning for classification. We systematically evaluate the performance of DECIDIA for cancer diagnosis and cancer type prediction on a curated dataset of 5389 samples that consist of colorectal cancer (CRC; n = 1574), hepatocellular cell carcinoma (HCC; n = 1181), lung cancer (n = 654), and non-cancer control (n = 1980). DECIDIA achieved an area under the receiver operating curve (AUROC) of 0.980 (95% CI, 0.976-0.984) in 10-fold cross-validation settings on the CRC dataset by differentiating cancer patients from cancer-free controls, outperforming benchmarked methods that are based on methylation intensities. Noticeably, DECIDIA achieved an AUROC of 0.910 (95% CI, 0.896-0.924) on the externally independent HCC testing set in distinguishing HCC patients from cancer-free controls, although there was no HCC data used in model development. In the settings of cancer-type classification, we observed that DECIDIA achieved a micro-average AUROC of 0.963 (95% CI, 0.960-0.966) and an overall accuracy of 82.8% (95% CI, 81.8-83.9). In addition, we distilled four sequence signatures from the raw sequencing reads that exhibited differential patterns in cancer versus control and among different cancer types. Our approach represents a new paradigm towards eliminating the tedious data analytical procedures for liquid biopsy that uses bisulfite-treated cfDNA methylome.

Deep learning models for the prediction of acute postoperative pain in PACU for video‐assisted thoracoscopic surgery

BackgroundPostoperative pain is a prevalent symptom experienced by patients undergoing surgical procedures. This study aims to develop deep learning algorithms for predicting acute postoperative pain using both essential patient details and real-time vital sign data during surgery.MethodsThrough a retrospective observational approach, we utilized Graph Attention Networks (GAT) and graph Transformer Networks (GTN) deep learning algorithms to construct the DoseFormer model while incorporating an attention mechanism. This model employed patient information and intraoperative vital signs obtained during Video-assisted thoracoscopic surgery (VATS) surgery to anticipate postoperative pain. By categorizing the static and dynamic data, the DoseFormer model performed binary classification to predict the likelihood of postoperative acute pain.ResultsA total of 1758 patients were initially included, with 1552 patients after data cleaning. These patients were then divided into training set (n = 931) and testing set (n = 621). In the testing set, the DoseFormer model exhibited significantly higher AUROC (0.98) compared to classical machine learning algorithms. Furthermore, the DoseFormer model displayed a significantly higher F1 value (0.85) in comparison to other classical machine learning algorithms. Notably, the attending anesthesiologists' F1 values (attending: 0.49, fellow: 0.43, Resident: 0.16) were significantly lower than those of the DoseFormer model in predicting acute postoperative pain.ConclusionsDeep learning model can predict postoperative acute pain events based on patients' basic information and intraoperative vital signs.

Clinic, CT radiomics, and deep learning combined model for the prediction of invasive pulmonary aspergillosis

BackgroundInvasive pulmonary aspergillosis (IPA) is a serious fungal infection. However, current diagnostic methods have limitations. The purpose of this study was to use artificial intelligence to achieve a more accurate diagnosis of IPA.MethodsTotally 263 patients (148 cases of IPA, 115 cases of non-IPA) were retrospectively enrolled from a single institution and randomly divided into training and test sets at a ratio of 7:3. Clinic-radiological independent risk factors for IPA were screened using univariate analysis and multivariate logistic regression analysis, after which a clinic-radiological model was constructed. The optimal radiomics features were extracted and screened based on CT images to construct the radiomics label score (Rad-score) and radiomics model. The optimal DL features were extracted and screened using four pre-trained convolutional neural networks, respectively, followed by the construction of the DL label score (DL-score) and DL model. Then, the radiomics-DL model was constructed. Finally, the combined model was constructed based on clinic-radiological independent risk factors, the Rad-score, and the DL-score. LR was adopted as the classifier. Receiver operating characteristic (ROC) curves were drawn, and the areas under the curve (AUC) were calculated to evaluate the efficacy of each model in predicting IPA. Additionally, based on the best-performing model on the LR classifier, four other machine learning (ML) classifiers were constructed to evaluate the predictive value for IPA.ResultsThe AUC of the clinic-radiological model for predicting IPA in the training and test sets was 0.845 and 0.765, respectively. The AUC of the radiomics-DL and combined models in the training set was 0.871 and 0.932, while in the test set was 0.851 and 0.881, respectively. The combined model showed better predictive performance than all other models. DCA showed that taking 0.00–1.00 as the threshold, the clinical benefit of the combined model was higher than that of all other models. Then, the combined model was trained on four other machine learning classifiers, all of which achieved AUC values above 0.80 in the test set, showing good performance in predicting IPA.ConclusionClinic, CT radiomics, and DL combined model could be used to predict IPA effectively.

Handwritten Digit Recognition by Deep Learning

This paper evaluates recent advances in handwritten digit recognition models, focusing on strategies developed and deployed in practical applications. The project utilizes both traditional and deep learning approaches, employing architectures such as Convolutional Neural Networks (CNNs). This paper explores the comparative performance of various models, discusses their deployment in real-world scenarios, and highlights future prospects for enhancing handwritten digit recognition technology. Keywords: Handwritten Digit Recognition, Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), AlexNet, Deep Learning for Image Classification, Neural Networks, Image Preprocessing, Data Augmentation, Feature Extraction, Image to Grayscale Conversion, Image Normalization, Training and Validation, Accuracy Metrics, Model Evaluation, Transfer Learning, Classification Algorithms, Real- Time Processing, Computer Vision, Image Recognition.

Transforming Motor Imagery Analysis: A Novel EEG Classification Framework Using AtSiftNet Method

This paper presents an innovative approach for the Feature Extraction method using Self-Attention, incorporating various Feature Selection techniques known as the AtSiftNet method to enhance the classification performance of motor imaginary activities using electrophotography (EEG) signals. Initially, the EEG signals were sorted and then denoised using multiscale principal component analysis to obtain clean EEG signals. However, we also conducted a non-denoised experiment. Subsequently, the clean EEG signals underwent the Self-Attention feature extraction method to compute the features of each trial (i.e., 350×18). The best 1 or 15 features were then extracted through eight different feature selection techniques. Finally, five different machine learning and neural network classification models were employed to calculate the accuracy, sensitivity, and specificity of this approach. The BCI competition III dataset IV-a was utilized for all experiments, encompassing the datasets of five volunteers who participated in the competition. The experiment findings reveal that the average accuracy of classification is highest among ReliefF (i.e., 99.946%), Mutual Information (i.e., 98.902%), Independent Component Analysis (i.e., 99.62%), and Principal Component Analysis (i.e., 98.884%) for both 1 and 15 best-selected features from each trial. These accuracies were obtained for motor imagery using a Support Vector Machine (SVM) as a classifier. In addition, five-fold validation was performed in this paper to assess the fair performance estimation and robustness of the model. The average accuracy obtained through five-fold validation is 99.89%. The experiments’ findings indicate that the suggested framework provides a resilient biomarker with minimal computational complexity, making it a suitable choice for advancing Motor Imagery Brain–Computer Interfaces (BCI).

Decision Transformer-Based Efficient Data Offloading in LEO-IoT.

Recently, the Internet of Things (IoT) has witnessed rapid development. However, the scarcity of computing resources on the ground has constrained the application scenarios of IoT. Low Earth Orbit (LEO) satellites have drawn people's attention due to their broader coverage and shorter transmission delay. They are capable of offloading more IoT computing tasks to mobile edge computing (MEC) servers with lower latency in order to address the issue of scarce computing resources on the ground. Nevertheless, it is highly challenging to share bandwidth and power resources among multiple IoT devices and LEO satellites. In this paper, we explore the efficient data offloading mechanism in the LEO satellite-based IoT (LEO-IoT), where LEO satellites forward data from the terrestrial to the MEC servers. Specifically, by optimally selecting the forwarding LEO satellite for each IoT task and allocating communication resources, we aim to minimize the data offloading latency and energy consumption. Particularly, we employ the state-of-the-art Decision Transformer (DT) to solve this optimization problem. We initially obtain a pre-trained DT through training on a specific task. Subsequently, the pre-trained DT is fine-tuned by acquiring a small quantity of data under the new task, enabling it to converge rapidly, with less training time and superior performance. Numerical simulation results demonstrate that in contrast to the classical reinforcement learning approach (Proximal Policy Optimization), the convergence speed of DT can be increased by up to three times, and the performance can be improved by up to 30%.

Combined Method Comprising Low Burden Physiological Measurements with Dry Electrodes and Machine Learning for Classification of Visually Induced Motion Sickness in Remote-Controlled Excavator

The construction industry is actively developing remote-controlled excavators to address labor shortages and improve work safety. However, visually induced motion sickness (VIMS) remains a concern in the remote operation of construction machinery. To predict the occurrence and severity of VIMS, we developed a prototype system that acquires multiple physiological signals with different mechanisms under a low burden and detects VIMS from the collected data. Signals during VIMS were recorded from nine healthy adult males operating excavator simulators equipped with multiple displays and a head-mounted display. Light gradient-boosting machine-based VIMS detection binary classification models were constructed using approximately 30,000 s of time-series data, comprising 23 features derived from the physiological signals. These models were validated using leave-one-out cross-validation on seven participants who experienced severe VIMS and evaluated through area under the curve (AUC) scores. The mean receiver operating characteristic curve AUC score was 0.84, and the mean precision–recall curve AUC score was 0.71. All features were incorporated into the models, with saccade frequency and skin conductance response identified as particularly important. These trends aligned with subjective assessments of VIMS severity. This study contributes to advancing the use of remote-controlled machinery by addressing a critical challenge to operator performance and safety.

CAM: a novel aid system to analyse the coloration quality of thick blood smears using image processing and machine learning techniques.

Battling malaria's morbidity and mortality rates demands innovative methods related to malaria diagnosis. Thick blood smears (TBS) are the gold standard for diagnosing malaria, but their coloration quality is dependent on supplies and adherence to standard protocols. Machine learning has been proposed to automate diagnosis, but the impact of smear coloration on parasite detection has not yet been fully explored. To develop Coloration Analysis in Malaria (CAM), an image database containing 600 images was created. The database was randomly divided into training (70%), validation (15%), and test (15%) sets. Nineteen feature vectors were studied based on variances, correlation coefficients, and histograms (specific variables from histograms, full histograms, and principal components from the histograms). The Machine Learning Matlab Toolbox was used to select the best candidate feature vectors and machine learning classifiers. The candidate classifiers were then tuned for validation and tested to ultimately select the best one. This work introduces CAM, a machine learning system designed for automatic TBS image quality analysis. The results demonstrated that the cubic SVM classifier outperformed others in classifying coloration quality in TBS, achieving a true negative rate of 95% and a true positive rate of 97%. An image-based approach was developed to automatically evaluate the coloration quality of TBS. This finding highlights the potential of image-based analysis to assess TBS coloration quality. CAM is intended to function as a supportive tool for analyzing the coloration quality of thick blood smears.

Sarcasm Detection Based on Sentiment Analysis of Audio Corpus Using Deep Learning

With the rapid progress in digitalization, hundreds of services and applications have begun to depend on human-machine communication, primarily voice-based. As technology advances, it has become increasingly imperative to develop systems that can discern sophisticated emotions from humans, like sarcasm. Despite research that has been conducted on the detection and analysis of speech signals to try and understand emotion, the detection of sarcasm, which is often not overtly expressed, is quite far from something that has received the attention it deserves in recent years. It plays a very important role in any utterance, searching to identify the psychology, mood, or even health behind it. Hence, it does give interactions with humans a more human-like flavor while raising the feeling of understanding human emotions. This article tries to identify sarcasm in speech using audio data derived from real-world, spontaneous, monolingual corpora. In this classification process, a deep learning classifier known as CNN-LSTM model is used. Results of the study showed that a robust combination between feature extraction through convolutional layers and sequential learning through LSTM layers can identify intricate speech patterns in sarcasm. Experimented and validated in terms of performance, this model shows the model to be very good at discriminating between sarcastic and no sarcastic speech, thus promoting it for more extensive use in various applications related to sentiment analysis and in human-computer interaction applications. The results show that this system provides far much closer approximations to human interaction styles, but its impact would extend into a wide area from customer services, monitoring mental health, and even AI-driven communication systems. It is a monolingual and spontaneous corpus that will open the gates for further work and development down the line and possibly extend to multilingual and more diversified speech contexts, thus fine-tuning accuracy in practical scenarios of sarcasm detection.

Pavement Distress Detection Based on the Staged Deep Learning Classifier Approach

Pavement crack detection is a critical task that is essential to ensure the safety of roads and highways. Accurate detection and classification of pavement cracks can help transportation agencies to identify potential maintenance needs and plan appropriate interventions. In this project, we propose a staged deep learning classifier approach for pavement crack detection to provide accurate and comprehensive information about the pavement condition. In this proposed pavement crack detection model, there are four main phases: pre-processing, feature extraction, feature selection, and crack detection. Firstly, raw images are pre-processed through median filtering and histogram equalization for noise removal and contrast enhancement. Next, features such as the improved local binary pattern, gray-level co-occurrence matrix, and gray-level run length matrix are extracted from the pre-processed images. The optimal features are selected using a hybrid optimization model, the tuna customized honey badger optimization algorithm, which combines the honey badger algorithm and tuna swarm optimization. This new hybrid optimization model will enhance the search strategy for solving optimization problems. The loss function of an augmented convolutional neural network (A-CNN) is modified to compute the root mean square error instead of the entropy-based loss function. The final detected outcomes (presence or absence of cracks) are acquired from the A-CNN.

Meningkatkan Hasil Belajar PAKBP pada Materi Manusia Berkembang Berkat Peran Sesama Menggunakan Model Problem Based Learning di Kelas VII SMP Negeri 1 Batang Kuis

This study aims to improve the learning outcomes of seventh-grade students on the material "Human Development Through the Roles of Others" using the Problem-Based Learning (PBL) model at SMP Negeri 1 Batang Kuis. This research employed a Classroom Action Research design conducted in two cycles. Each cycle consisted of four stages: planning, action, observation, and reflection. The research subjects were 7 students from the seventh-grade class at SMP Negeri 1 Batang Kuis. Data collection techniques were carried out through tests (formative tests) and non-tests (observations). The results of the study in cycle I showed an average score of 68.14 and a classical learning completeness percentage of 72.42%. In cycle II, the average score increased to 84.85. The average student attendance in cycle I was 100% and remained 100% in cycle II. Student involvement in learning in cycle I was 66.28% (high) and increased to 76.50% (very high) in cycle II. It can be concluded that the PBL model can improve the learning outcomes and activities of students on the material "Human Development Through the Roles of Others" in the seventh-grade class at SMP Negeri 1 Batang Kuis.

Federated deep learning models for detecting RPL attacks on large-scale hybrid IoT networks

With the rapid spread of the Internet of Things (IoT), smart applications and services become increasingly crucial, making them an easily accessible source of personally identifiable information. Over the last few years, the use of machine learning in securing routing layers, particularly routing protocol for low-power and lossy networks (RPL), has become fundamental in ensuring successful routing and privacy preservation as a crucial consideration among edge nodes. In recent works, training of collected data on a central server has increased concerns regarding data privacy. Consequently, decentralized learning is currently a solution for privacy preservation. It has gained popularity in IoT networks in which the models are trained on hybrid data located in edge nodes and enable global decision-making without sharing global data, causing high communication costs during weight updates. We propose a federated learning of routing protocol (Fed-RPL)-based gated recurrent unit (GRU) model for decentralized training rounds and quantization method (Q-8bit) to decrease the number of weight updates that can significantly mitigate the communication overhead and maintain the local model with high accuracy. Meanwhile, the ensemble unit aggregates the updates and selects the best local model to enhance the global model accuracy. Our experiments show that Fed-RPL outperforms classical machine learning (ML) methods in privacy-preserving edge data, significantly reduces the communication cost in non-IID scenarios, and achieves higher detection accuracy than recent FL approaches.

Efficient Deep Learning Methods for Detecting Road Accidents by Analyzing Traffic Accident Images

Speed is one of the major factors in car crashes. Many lives could have been saved if emergency services had been alerted to the disaster and arrived in time. For the sake of protecting valuable human lives, an effective automatic accident detection system with prompt reporting of the accident scene to emergency services is essential. Therefore, this research proposes some effective Deep Learning techniques that properly recognize the incidence of accidents. The paper introduces two different techniques for image classification, with a particular focus on distinguishing between accident and non-accident images. The dataset used for the proposed model is taken from Kaggle, which is a collection of CCTV images. The first approach is a hybridized TL-ML method that employs transfer learning techniques that use different pre-trained versions of convolutional neural networks to extract features from image datasets. These extracted features are then fed into various machine learning classifiers to categorize the images as either Accident or Non-accident. To make the final decision, a voting classifier is utilized to choose the best classification outcome from the set of previously employed machine learning classifiers. In the second method, a modified Convolutional Neural Network (CNN) called SpinalNet is adopted. The performance of these models was evaluated by comparing them with each other and with a customized CNN base model. SpinalNet consistently surpassed the other models in terms of Precision, Recall, F1-Score, and Accuracy, demonstrating its outstanding capabilities.

Deep Learning Enhanced CNN with Bio-Inspired Techniques and BCE For Effective Lung Nodules Detection & Classification For Accurate Diagnosis

Objectives: To propose a new hybrid deep learning model to boost lung nodule detection and classification in combination with a bioinspired method for efficient hyper-parameter optimization to maximize true positive and nodule discrimination rates. Methods: In order to extract the intrinsic and complex features from different lung nodules, deep learning-based enhanced CNN (ECNN) is used. Segmentation masks are generated using Mask-RCNN to isolate and capture the ROIs, which serve as input to CNN. Prior to segmentation and extraction, data cleaning, transformation, and augmentation is done. Hyper-parameter optimization is done using a bio-inspired differential evolution method, which helps to identify the learning rate and number of layers to achieve optimal performance. We utilize BCE to quantify the performance of classification tasks. A large-scale CT-DICOM image dataset with 25,1135 images is used for this research work, collected from the cancer imaging archive, which has a clinically proven record of 355 instances with detailed image analysis, tumor location, and bounding boxes with a resolution of 512x512 pixels. 175794 images are used for training, and 75341 images are used for testing and validation. The performance of the new system is assessed using MATLAB, where results are compared with existing models such as SVM-WSS, GCPSO-PNN, and 3D-DLCNN models. Findings: The newly suggested ECNN with DE Bio-inspired model boosts the performance of lung nodule detection and classification with 94.7% accuracy, 93.8% sensitivity, 94.5% specificity, 93.4% F1 score, 92.4% dice coefficient, 0.1 Log Loss and AUC-ROC with 0.93 TPR and 0.07 FPR. Novelty: The novel method presents an advanced computational model using deep learning and bio-inspired algorithms for robust classification of lung nodules, which significantly improves the early diagnosis. This CAD model overcomes the limitations of the existing approaches SVM-WSS, GCPSO-PNN, and 3D-DLCNN in terms of segmentation, classification, error detection, and hyper-parameter tuning. Keywords: Lung Nodules, Deep Learning, Disease Classification, Image Processing, ECNN- DE Classifier, CT-DICOM Dataset

Hybridization of Machine Learning Models for Alzheimers Disease Classification

Alzheimer's disease (AD), is a gradual cognitive decline and memory impairment. It is a major health concern worldwide. Despite intensive research efforts, accurate and early diagnosis remains difficult to achieve, largely due to the complexity of AD pathology and the absence of definitive biomarkers. Existing diagnostic approaches often rely on costly and invasive procedures, leading to delays in diagnosis and treatment initiation, and limiting the effectiveness of therapeutic interventions. To overcome these issues, this work suggests a novel approach for AD classification using EEG signals. EEG signals offer a non-invasive and cost-effective means of assessing brain activity, making them an attractive candidate for biomarker discovery and disease classification. The proposed work integrates preprocessing, feature extraction, and classification methodologies to accurately differentiate between AD, normal/healthy states, and Frontotemporal Dementia (FTD). The proposed solution begins with Sequential Savitzky-Golay filtering (SEQ-SG) to enhance the quality of EEG signals by reducing noise and enhancing relevant features. Subsequently, an Improved Principal Component Analysis (IPCA) approach is employed for feature extraction, incorporating feature scaling using StandardScaler to ensure uniform contribution from all features. Finally, classification is achieved using a hybrid approach named HMLCAD (Hybridization of Machine Learning for Classification of Alzheimer's Disease), which combines Random Forest and Gradient Boosting through a voting classifier ensemble. This methodology offers a promising framework for accurate and early detection of AD, enabling timely intervention and improved patient outcomes.

Development and validation of machine-learning models for monitoring individual behaviors in group-housed broiler chickens

Animals' individual behavior is commonly monitored by live or video observation by a person. This can be labor intensive and inconsistent. An alternative is the use of machine learning-based computer vision systems. The objectives of this study were to 1) develop and optimize machine learning model frameworks for detecting, tracking and classifying individual behaviors of group-housed broiler chickens in continuous video recordings; and 2) use an independent dataset to evaluate the performance of the developed machine leaning model framework for individual poultry behaviors differentiation. Forty-two video recordings from 4 different pens (total video duration = 1,620 min) were used to develop and train multiple models for detecting and tracking individual birds and classifying 4 behaviors: feeding, drinking, active, and inactive. The optimal model framework was used to continuously analyze an external set of 11 videos (duration = 326 min), and the second-by-second behavior of each individual broiler was extracted for the comparison of human observation. After comparison of model performance, the YOLOv5l, out of 5 detection models, was selected for detecting individual broilers in a pen; the osnet_x0_25_msmt17, out of 4 tracking algorithms, was selected to track each detected bird in continuous frames; and the Gradient Boosting Classifier, out of 12 machine learning classifiers, was selected to classify the 4 behaviors. Most of the models were able to keep previously assigned individual identifications of the chickens in limited amounts of time, but lost the identities throughout an examination period (≥4 min). The final framework was able to accurately predict feeding (accuracy = 0.895) and drinking time (accuracy = 0.9) but subpar for active (accuracy = 0.545) and inactive time (accuracy = 0.505). The algorithms employed by the machine learning models were able to accurately detect feeding and drinking behavior but still need to be improved for maintaining individual identities of the chickens and identifying active and inactive behaviors.

ARP spoofing detection using machine learning classifiers: an experimental study

ARP spoofing detection using machine learning classifiers: an experimental study