Automatic Detection System Research Articles

Predicting food adulterants in milk using Support Vector Machine (SVM)

Milk adulteration is a significant global issue, particularly in emerging nations, where inadequate monitoring and unhygienic conditions prevail. The adulteration of milk with various chemicals, such as urea, water, skimmed milk powder, sugar, and detergent, poses serious health risks, including heart problems, diarrhea, CNS disorders, irritation, and gastrointestinal disorders. Traditional detection methods are labor-intensive and require sophisticated equipment, which limits their practical application. This study aims to develop a machine learning-based approach to detect milk adulteration using attributes like Solids- Not-Fat (SNF), fat, Corrected Lactometer Reading (CLR), Total Solids (TS), temperature, and protein content. Various machine learning models were employed and evaluated for their performance, including Logistic Regression, Decision Trees, SVM, and Random Forests. The findings demonstrate that machine learning can effectively identify adulteration types, providing a foundation for the dairy industry’s practical and automated detection systems. This research comprehensively reviews common milk adulterants and highlights advanced detection methods to ensure milk quality and safety.

Machine vision system for automatic defect detection of ultrasound probes

Machine vision system for automatic defect detection of ultrasound probes

Ensemble of hybrid model based technique for early detecting of depression based on SVM and neural networks

The prevalence of depression has increased dramatically over the last several decades: it is frequently overlooked and can have a significant impact on both physical and mental health. Therefore, it is crucial to develop an automated detection system that can instantly identify whether a person is depressed. Currently, machine learning (ML) and artificial neural networks (ANNs) are among the most promising approaches for developing automated computer-based systems to predict several mental health issues, such as depression. This study propose an ensemble of hybrid model-based techniques that aims to build a strong detection model that considers many psychological and sociodemographic characteristics of an individual to detect whether a person is depressed. Support vector machines (SVM) and multilayer perceptrons (MLP) are the two fundamental methods used to construct the suggested ensemble approach. The hybrid DeprMVM served as a meta-learner. In this study, the hybrid DeprMVM is a level-1 learner, whereas the SVM and MLP networks are level-0 learners. After the classifiers are trained and tested at level 0, their outputs are based on both the independent and dependent variables in the new data set that was used to train the meta-classifier. The training data class imbalance was reduced by applying the synthetic minority oversampling technique (SMOTE) and cluster sampling together, which improved the accuracy for detecting depression. Additionally, it can effectively reduce the risk of over-fitting from simply duplicating data points. To further confirm the effectiveness of the proposed method, various performance evaluation metrics were calculated and compared with previous studies conducted on this specific dataset. In conclusion, among all the techniques for identifying depression, the suggested ensemble approach had the best accuracy, at 99.39%, and an F1-score of 99.51%.

Performance verification of Siemens Atellica DL IM1600 automatic immune analyzer detection system: A-TPO and A-TG.

To study the performance of the Siemens Atellica DL IM1600 automatic immune analyzer detection system for thyroid peroxidase antibodies (A-TPO) and anti-thyroglobulin antibodies (A-TG). Perform performance verification based on CNAS and CLSI related documents, including precision, accuracy, reference interval, and linearity. In precision validation, intra-batch imprecision coefficient of variation values of A-TG and A-TPO were 3.60%, 5.40%, and 3.20%, 2.20%, inter-batch imprecision coefficient of variation values were 5.40%, 4.40%, and 5.00%, 2.50%, all within the specified range. In the accuracy verification, the A-TG comparison values were -10.94%, -1.49%, -1.14%, -7.78%, -3.28%, and the A-TPO values were 21.20%, 6.42%, 14.63%, 12.40%, and 6.55%, The absolute values were all lower than the comparison requirements. The reference interval verifies that the A-TG and A-TPO test results of health patients were within the normal reference interval of 0 to 60 U/mL. In the linear range validation, A-TG linear regression y = 1.0145x - 10.093, correlation coefficient R2 = 0.9963 > 0.95, A-TPO linear regression y = 0.9985x - 18.172, correlation coefficient R2 = 0.9954 > 0.95, indicating linear linearity. The performance verification of Siemens Atellica DL IM1600 automatic immune analyzer detection system for A-TPO and A-TG was satisfactory, suitable for clinical application.

Deep transfer learning with improved crayfish optimization algorithm for oral squamous cell carcinoma cancer recognition using histopathological images

Oral Squamous Cell Carcinoma (OSCC) causes a severe challenge in oncology due to the lack of diagnostic devices, leading to delays in detecting the disorder. The OSCC diagnosis through histopathology demands a pathologist expert because the cellular presentation is variable and highly complex. Existing diagnostic approaches for OSCC have specific efficiency and accuracy restrictions, highlighting the necessity for more reliable techniques. The increase of deep neural networks (DNN) model and their applications in medical imaging have been instrumental in disease diagnosis and detection. Automatic detection systems using deep learning (DL) approaches show tremendous promise in investigating medical imagery with speed, efficiency, and accuracy. In terms of OSCC, this system allows the diagnostic method to be streamlined, facilitating earlier diagnosis and enhancing survival rates. Automatic analysis of histopathological image (HI) can assist in accurately detecting and identifying tumorous tissue, reducing diagnostic turnaround times and increasing the efficacy of pathologists. This study presents a Squeeze-Excitation with Hybrid Deep Learning for Oral Squamous Cell Carcinoma Recognition (SEHDL-OSCCR) on HIs. The presented SEHDL-OSCCR technique mainly focuses on detecting oral cancer (OC) using hybrid DL models. The bilateral filtering (BF) technique is initially used to remove the noise. Next, the SEHDL-OSCCR technique employs the SE-CapsNet model to recognize the feature extractors. An improved crayfish optimization algorithm (ICOA) technique is utilized to improve the performance of the SE-CapsNet model. At last, the classification of the OSCC technique is performed by employing a convolutional neural network with a bidirectional long short-term memory (CNN-BiLSTM) model. The simulation results obtained using the SEHDL-OSCCR technique are investigated using a benchmark medical image dataset. The experimental validation of the SEHDL-OSCCR technique illustrated a greater accuracy outcome of 98.75% compared to recent approaches.

Influence of rotation speed and frequency on the decision of Columba livia domestica (homing pigeon) to cross the rotor-swept area of paper blades mimicking a wind turbine

Abstract To reduce bird collisions with wind turbines, automatic detection systems have been developed to slow the blades down when a bird is approaching. We experimentally tested whether blade rotational speed (i.e., number of rotations per min) and frequency (i.e., number of times a blade passes a point per min) affected the decision time (i.e., time to take-off), path choice (i.e., the position in the aviary), and decision to cross the rotor-swept area in Columba livia domestica (rock dove [domestic variety]; aka homing pigeon; hereafter, pigeon). We used a homemade device with paper blades, mimicking the movement of wind turbine blades. We adjusted the paper blade dimensions and achromatic contrast with the background to match the visual capabilities of pigeons, increasing the probability of detection. Pigeons were less likely to cross the rotor-swept area at higher speeds and frequencies, independent of their decision time. When pigeons crossed the rotor-swept area (43 out of 160 trials), 63% collided with the blades, regardless of blade speed or frequency. Pigeons chose to avoid the rotor-swept area after they had travel half the distance to the wind turbine. Pigeons were not better able to avoid the rotor-swept area when blades were rotating at low speed and/or frequency and often collided with the blades. Thus, slowing blades to a low rotational speed may not reduce collisions with some species and a complete turbine shutdown may be necessary. The feasibility and economic costs of regular complete shutdowns after the deceleration triggered by the automatic detection systems need further investigation.

Combining deep learning methods and rule-based systems for automatic parking space detection

This paper presents an Automatic Parking Space Detection (APSD) algorithm designed to reduce traffic in cities while offering an information system of available parking zones. The main aim of such a system lies in its ability to identify parking spaces in a distributed manner, achieved by installing multiple APSD systems across a fleet of vehicles. This fleet, during its regular operations, communicates the availability of parking spaces to a centralized information system. Our methodology employs a rule-based system that seamlessly integrates a variety of neural networks for different specific tasks. These tasks include depth estimation, road segmentation, and vehicle detection. This approach would fall into a modular category instead of an end-to-end solution, using the Málaga Urban Dataset in the experiments. We present a preliminary experiment for parameter settings and an ablation study to quantify each subsystem contribution to the results. The proposed system achieves a parking space detection F1 score of 0.726.

Detection and Early Warning of Duponchelia fovealis Zeller (Lepidoptera: Crambidae) Using an Automatic Monitoring System

In traditional pest monitoring, specimens are manually inspected, identified, and counted. These techniques can lead to poor data quality and hinder effective pest management decisions due to operational and economic limitations. This study aimed to develop an automatic detection and early warning system using the European Pepper Moth, Duponchelia fovealis (Lepidoptera: Crambidae), as a study model. A prototype water trap equipped with an infrared digital camera controlled using a microprocessor served as the attraction and capture device. Images captured by the system in the laboratory were processed to detect objects. Subsequently, these objects were labeled, and size and shape features were extracted. A machine learning model was then trained to identify the number of insects present in the trap. The model achieved 99% accuracy in identifying target insects during validation with 30% of the data. Finally, the prototype with the trained model was deployed in the field for result confirmation.

Vertical back movement of cows during locomotion: detecting lameness with a simple image processing technique.

This research paper proposes a simple image processing technique for automatic lameness detection in dairy cows under farm conditions. Seventy-five cows were selected from a dairy farm and visually assessed for a reference/real lameness score (RLS) as they left the milking parlor, while simultaneously being video-captured. The method employed a designated walking path and video recordings processed through image analysis to derive a new computerized automatic lameness score (ALDS) based on calculated factors from back arch posture. The proposed automatic lameness detection system was calibrated using 12 cows, and the remaining 63 were used to evaluate the diagnostic characteristics of the ALDS. The agreement and correlation between ALDS and RLS were investigated. ALDS demonstrated high diagnostic accuracy with 100% sensitivity and specificity and was found to be 100% accurate with a perfect agreement (ρc = 1) and strong correlation (r = 1, P < 0.001) for lameness detection in binary scores (lame/non-lame). Moreover, the ALDS had a strong agreement (ρc = 0.885) and was highly correlated (r = 0.840; 0.796-1.000 95% confidence interval, P < 0.001) with RLS in ordinal scores (lameness severity; LS1 to LS5). Our findings suggest that the proposed method has the potential to compete with vision-based lameness detection methods in dairy cows in farm conditions.

Deep Learning-Based Decision Support System for Automatic Detection and Grading of Surface Corrosion on Galvanized Steel Sheets

Corrosion in the sheets produced leads to significant material losses, including the loss of resources, capital, labor, energy and knowledge. Corrosion control is significant for sheets produced and sent to customers in iron and steel factories. Surface corrosion testing of produced sheets and the accurate detection of corrosion levels are of great importance. The corrosion detection process for sheets in steel factories is performed visually with the naked eye. This is a subjective and time-consuming method. Identifying corrosion damage by visual detection and accurately determining the type and extent of corrosion requires expertise. Wrong decisions at this stage lead to losses during the production phase. Therefore, there is a need for systems that can automate this process and make it human-independent. In this study, a decision support system was designed to automatically detect the level of corrosion in galvanized sheets using convolutional neural networks. The average accuracy of the system is 97.5%, the average precision is 0.98, the average recall is 1 and the average F1 score is 0.99. The results we obtained indicate that a successful system has been developed for the detection and determination of corrosion levels. The high performance of the convolutional neural network models used for corrosion detection supports the practical applicability of the developed system. This system will increase the reliability and efficiency of industrial processes by enabling the accurate and automatic classification of corrosion. This system, which meets a significant need in this area for industrial organizations, reduces production costs and also makes the corrosion detection process more consistent and faster.

Automated Detection of Neurodevelopmental Disorders Using Face-to-Face Mobile Technology Among Typically Developing Greek Children: Randomized Controlled Trial.

Neurodevelopmental disorders (NDs) are characterized by heterogeneity, complexity, and interactions among multiple domains with long-lasting effects in adulthood. Early and accurate identification of children at risk for NDs is crucial for timely intervention, yet many cases remain undiagnosed, leading to missed opportunities for effective interventions. Digital tools can help clinicians assist and identify NDs. The concept of using serious games to enhance health care has gained attention among a growing group of scientists, entrepreneurs, and clinicians. This study aims to explore the core principles of automated mobile detection of NDs in typically developing Greek children, using a serious game developed within the SmartSpeech project, designed to evaluate multiple developmental domains through principal component analysis (PCA). A total of 229 typically developing children aged 4 to 12 years participated in the study. The recruitment process involved open calls through public and private health and educational institutions across Greece. Parents were thoroughly informed about the study's objectives and procedures, and written consent was obtained. Children engaged under the clinician's face-to-face supervision with the serious game "Apsou," which assesses 18 developmental domains, including speech, language, psychomotor, cognitive, psychoemotional, and hearing abilities. Data from the children's interactions were analyzed using PCA to identify key components and underlying principles of ND detection. A sample of 229 typically developing preschoolers and early school-aged children played the Apsou mobile serious game for automated detection of NDs. Performing a PCA, the findings identified 5 main components accounting for about 80% of the data variability that potentially have significant prognostic implications for a safe diagnosis of NDs. Varimax rotation explained 61.44% of the total variance. The results underscore key theoretical principles crucial for the automated detection of NDs. These principles encompass communication skills, speech and language development, vocal processing, cognitive skills and sensory functions, and visual-spatial skills. These components align with the theoretical principles of child development and provide a robust framework for automated ND detection. The study highlights the feasibility and effectiveness of using serious games for early ND detection in children. The identified principal components offer valuable insights into critical developmental domains, paving the way for the development of advanced machine learning applications to support highly accurate predictions and classifications for automated screening, diagnosis, prognosis, or intervention planning in ND clinical decision-making. Future research should focus on validating these findings across diverse populations integrating additional features such as biometric data and longitudinal tracking to enhance the accuracy and reliability of automated detection systems. ClinicalTrials.gov NCT06633874; https://clinicaltrials.gov/study/NCT06633874. RR2-https://doi.org/10.3390/signals4020021.

A Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classification.

The correct diagnosis and early treatment of respiratory diseases can significantly improve the health status of patients, reduce healthcare expenses, and enhance quality of life. Therefore, there has been extensive interest in developing automatic respiratory disease detection systems. Most recent methods for detecting respiratory disease use machine and deep learning algorithms. The success of these machine learning methods depends heavily on the selection of proper features to be used in the classifier. Although metaheuristic-based feature selection methods have been successful in addressing difficulties presented by high-dimensional medical data in various biomedical classification tasks, there is not much research on the utilization of metaheuristic methods in respiratory disease classification. This paper aims to conduct a detailed and comparative analysis of six widely used metaheuristic optimization methods using eight different transfer functions in respiratory disease classification. For this purpose, two different classification cases were examined: binary and multi-class. The findings demonstrate that metaheuristic algorithms using correct transfer functions could effectively reduce data dimensionality while enhancing classification accuracy.

FESTA: A Polygon-Based Approach for Extracting Relevant Structures from Free Energy Surfaces Obtained in Molecular Simulations.

An easy-to-use script named FESTA (Free Energy Surface Trajectory Analysis) is provided to streamline the extraction of equilibrium structures from free energy maps obtained with enhanced sampling simulation techniques and their associated trajectories. This approach efficiently identifies relevant structures by automatically selecting minima on the free energy surface using a connected-component labeling algorithm and extracts them utilizing a Shapely-polygon-based trajectory analysis process. The script is general and portable; it incorporates an automatic periodicity detection system; and multiprocessing is utilized to leverage all available computational resources for enhanced efficiency. The effectiveness of the proposed polygon-based approach is demonstrated through comparison with a naïve and largely inefficient loop-based script and its application across three distinct systems for benchmarking purposes.

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.

A retinal detachment based strabismus detection through FEDCNN

Ocular strabismus, a common condition in the present generation is an absolute risk factor for amblyopia and blinding premorbid visual loss. Despite the availability of new optometry tools with eye-tracking data, the issues persist in attaining accuracy and reliability in diagnosing strabismus. These two concerns are specifically accommodated in this study by the proposed novel approach that involves CNNs with eye-tracking datasets from subjects. The presented work aims to improve the accuracy of diagnostics in ophthalmology utilizing the integration of the further proposed algorithms into an automatic strabismus detection system. For this purpose, the proposed FedCNN model combines the CNN with eXtreme Gradient Boosting (XGBoost) and uses the Gaze deviation (GaDe) images to capture dynamic eye movements. This method tries to make the feature extraction as accurate as possible in its best working state to enhance the diagnosis precision. The model proves to be accurate, reaching 95.2%, which is even more prominent because of the more or less detailed connection layer of the CNN, which is used for the selection of features designated for such tasks of strabismus recognition. The presented method has the potential of shifting the approach to diagnosing diseases of the eyes in more or less half of the patients.

Clinical Validation of Deep Learning for Segmentation of Multiple Dental Features in Periapical Radiographs.

Periapical radiographs are routinely used in dental practice for diagnosis and treatment planning purposes. However, they often suffer from artifacts, distortions, and superimpositions, which can lead to potential misinterpretations. Thus, an automated detection system is required to overcome these challenges. Artificial intelligence (AI) has been revolutionizing various fields, including medicine and dentistry, by facilitating the development of intelligent systems that can aid in performing complex tasks such as diagnosis and treatment planning. The purpose of the present study was to verify the diagnostic performance of an AI system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on periapical radiographs. A dataset comprising 1000 periapical radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists. A strong correlation (R > 0.5) was observed between AI perception and observers 1 and 2 in carious teeth (0.7-0.73), implants (0.97-0.98), restored teeth (0.85-0.89), teeth with fixed prosthesis (0.92-0.94), and missing teeth (0.82-0.85). The automatic detection by the AI system was comparable to the oral radiologists and may be useful for automatic identification in periapical radiographs.

Aircraft automatic detection on Station7hz interface using signal processing and artificial intelligence - ACOUDET - First method

The global project is to design a automatic noise event detection and classification system in real time using signal processing and artificial intelligence (AI). The first phase focuses on transport noise (aircrafts, trains, road vehicles, etc.). Secondly, the plan is to extend detection to construction site noise and any natural or life sources. This paper presents the first method developed to detect audible aircrafts pass-by. It focused on the development of a prototype detection algorithm, its results in situ conditions, and insights on future developments to create a robust aircraft and train noise detection system. The data collection method, aircraft audio signal particularities and a first detection method are described. The robustness of the detections is evaluated and limitation induced by real time detection are discussed. Thanks to the proprietary monitoring system Station-7hz. it has been possible to detect aircrafts in different types of sound environments in a straightforward manner, showing better results at night due to a quieter environment. Future developments based on a hybrid algorithm include AI models for audibility assessment and unwanted event filtering during aircraft pass-by. Aircraft's presence is detected via radar data and the detection of maximum acoustic levels correlated with aircraft parameters.

Computer vision and deep transfer learning for automatic gauge reading detection

This manuscript proposes an automatic reading detection system for an analogue gauge using a combination of deep learning, machine learning, and image processing. The study suggests image-processing techniques in manual analogue gauge reading that include generating readings for the image to provide supervised data to address difficulties in unsupervised data in gauges and to achieve better accuracy using DenseNet 169 compared to other approaches. The model uses artificial intelligence to automate reading detection using deep transfer learning models like DenseNet 169, InceptionNet V3, and VGG19. The models were trained using 1011 labeled pictures, 9 classes, and readings from 0 to 8. The VGG19 model exhibits a high training precision of 97.00% but a comparatively lower testing precision of 75.00%, indicating the possibility of overfitting. On the other hand, InceptionNet V3 demonstrates consistent precision across both datasets, but DenseNet 169 surpasses other models in terms of precision and generalization capabilities.

Generative AI with WGAN-GP for boosting seizure detection accuracy.

Imbalanced datasets pose challenges for developing accurate seizure detection systems based on electroencephalogram (EEG) data. Generative AI techniques may help augment minority class data to facilitate automatic epileptic seizure detection. This study investigates the impact of various data augmentation (DA) approaches, including Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), Vanilla GAN, Conditional GAN (CGAN), and Cramer GAN, on classification performance with Random Forest models. The best-performing GAN variant, WGAN-GP, was then integrated with a bidirectional Long Short-Term Memory (LSTM) architecture and compared against traditional and synthetic oversampling methods. The evaluation of different GAN variants for data augmentation with Random Forest classifiers identified WGAN-GP as the most effective approach. The integration of WGAN-GP with bidirectional LSTM yielded substantial performance improvements, outperforming traditional oversampling methods and achieving an accuracy of 91.73% on the augmented data, compared to 86% accuracy on real data without augmentation. The proposed generative AI approach combining WGAN-GP and recurrent neural network models outperforms comparative synthetic oversampling methods on metrics relevant for reliable seizure detection from imbalanced EEG datasets. Incorporating the WGAN-GP generative AI technique for data augmentation and integrating it with bidirectional LSTM elevates seizure detection accuracy for imbalanced EEG datasets, surpassing the performance of traditional oversampling and class weight adjustment methods. This approach shows promise for improving epilepsy monitoring and management through enhanced automated detection system effectiveness.

A novel deep learning model for cabbage leaf disease detection and classification

Manual observation bias in identifying cabbage leaf diseases necessitates efficient automated detection systems in agriculture. In this study, a deep learning-based approach was developed for the automatic recognition and categorization of cabbage diseases, focusing on aphids, worm cuts, and black rot. Using 1400 images, including healthy and diseased samples, our method employs novel deep learning models. EfficientNetB7, MobileNetV2, and DenseNet201 were utilized to successfully identify three types of cabbage leaf diseases with accuracies of 98.56%, 98.26%, and 97.64%, respectively. EfficientNetB7 achieved the highest accuracy of 98.56% for disease classification. The system's ability to automatically extract features from images enhances disease detection, crucial for mitigating yield losses. Through visualization techniques, we analyzed the location of unhealthy regions in the leaves, the filter, and the intermediate layer.