Effective Detection Model Research Articles

Dementia Classification Approach Based on Non-Singleton General Type-2 Fuzzy Reasoning

Dementia is the most critical neurodegenerative disease that gradually destroys memory and other cognitive functions. Therefore, early detection is essential, and to build an effective detection model, it is required to understand its type, symptoms, stages and causes, and diagnosis methodologies. This paper presents a novel approach to classify dementia based on a data set with some relevant patient features. The classification methodology employs non-singleton general type-2 fuzzy sets, non-singleton interval type-2 fuzzy sets, and non-singleton type 1 fuzzy sets. These advanced fuzzy sets are compared with traditional singleton fuzzy sets to evaluate their performance. The Takagi–Sugeno–Kang TSK inference method is used to handle fuzzy reasoning. In the process, the parameters of the membership functions (MFs) and rules are obtained using ANFIS, and non-singleton MFs are optimized with PSO. The results demonstrate that non-singleton general type-2 fuzzy sets improve classification accuracy compared to singleton fuzzy sets, demonstrating their ability to model the uncertainties inherent in the diagnosis of dementia. This improvement suggests that non-singleton fuzzy systems offer a more robust framework for developing effective diagnostic tools in the medical domain. Accurate classification of dementia is of utmost importance to improve patient care and advance medical research.

K‐Fuse: Credit card fraud detection based on a classification method with a priori class partitioning and a novel feature selection strategy

AbstractOnline transactions have become the dominant and most popular form of online payment in today's digital economy. Due to the growing popularity of e‐commerce and the convenience it offers, both consumers and businesses are rapidly adopting online transactions. Notably, credit cards have become one of the most popular and standard online payment methods. However, it should be noted that credit card transactions are not without challenges. In particular, detecting and preventing fraudulent transactions is a major concern of the online payment system. It is difficult to find an effective detection model that can detect the new patterns created by fraudsters, due to the constant evolution of their methods to exploit the vulnerability of current security protocols. These fraud patterns are evolving and may not correspond to existing documented models, leading to a reduction in their identification. In addition, the customer's behavior can affect the model detection as it is susceptible to change based on factors such as economic conditions, trends, and individual circumstances. When consumers deviate from their typical behavior, the model may generate false alerts, thereby reducing its ability to differentiate between legitimate and fraudulent transactions. This article presents a new supervised detection model, called K‐Fuse, which introduces an unsupervised phase in order to detect fraud patterns that may correspond to innovative models introduced by fraudsters. K‐Fuse is a supervised classification method that fuses three steps consisting of (i) unsupervised clustering to identify hidden patterns of transactions in a dataset, (ii) a novel feature selection criterion based on the unsupervised results, and (iii) supervised classification to exploit the results of clustering and feature selection to predict new transactions as fraudulent or legitimate.

Survey on graph neural network-based community detection and its applications

Detecting communities within a network is a critical component of network analysis. The process involves identifying clusters of nodes that exhibit greater similarity to each other compared to other nodes in the network. In the context of Complex networks (CN), community detection becomes even more important as these clusters provide relevant information of interest. Traditional mathematical and clustering methods have limitations in terms of data visualization and high-dimensional information extraction. To address these challenges, graph neural network learning methods have gained popularity in community detection, as they are capable of handling complex structures and multi-dimensional data. Developing a framework for community detection in complex networks using graph neural network learning is a challenging and ongoing research objective. Therefore, it is essential for researchers to conduct a thorough review of community detection techniques that utilize cutting-edge graph neural network learning methods [102], in order to analyze and construct effective detection models. This paper provides a brief overview of graph neural network learning methods based on community detection methods and summarizes datasets, evaluation metrics, applications, and challenges of community detection in complex networks.

AN AUTOMATED BREAST CANCER DETECTION BY HEURISTIC-BASED ENSEMBLE DEEP CLASSIFIER USING MAMMOGRAM AND TOMOSYNTHESIS IMAGES

Nowadays, breast cancer is the most founded cancer among women. Moreover, 2.3 million new cases of breast cancer have been detected among women since 2020 as reported by World Health Organization (WHO). Many research studies based on breast cancer mainly aim at ultrasound, mammography, and Magnetic Resonance Images (MRI). However, there exist certain limitations such as lack of access for the detection of disease in rural and remote cities and insufficient knowledge about the availability of computer-aided systems. In order to overcome the problem, it is necessary to develop an effective detection model for breast cancer. For the experimentation, the input images like mammogram images and tomosynthesis images are garnered from the benchmark online resources. The input image further undergoes the pre-processing stage, which is made by using “Contrast Limited Adaptive Histogram Equalization (CLAHE) and histogram equalization (HE)”. Then, the pre-processed images are further given as input to the segmentation, in which deeplabv3 is deployed. Consequently, with the assistance of segmented images, the relevant features like “texture features, color features, shape features, deep features, statistical features and morphological features” are extracted. Then, these obtained features are used in weighted feature selection, where the optimal feature selection is performed, and weights are optimized by the Enhanced Adaptive Prey Location-Based Pelican Optimization Algorithm (APL-POA). Finally, the weighted accurate features are given as input to the Ensemble Deep Learning (EDC) model. The ensemble model is structured by “Deep Neural Network (DNN), Recurrent Neural Network (RNN), Bidirectional Long Short-Term Memory (Bi-LSTM), Deep Temporal Convolution Networks (DTCN) and Gated Recurrent Unit (GRU)”, in which the hyperparameters of every classifier are smoothened optimally by the enhanced APL-POA algorithm. Through the experimental analysis, the proposed work tends to provide an improved classification rate and rapid detection of disease that aids in better diagnosis of the patients.

An efficient intrusion detection model based on convolutional spiking neural network

Many intrusion detection techniques have been developed to ensure that the target system can function properly under the established rules. With the booming Internet of Things (IoT) applications, the resource-constrained nature of its devices makes it urgent to explore lightweight and high-performance intrusion detection models. Recent years have seen a particularly active application of deep learning (DL) techniques. The spiking neural network (SNN), a type of artificial intelligence that is associated with sparse computations and inherent temporal dynamics, has been viewed as a potential candidate for the next generation of DL. It should be noted, however, that current research into SNNs has largely focused on scenarios where limited computational resources and insufficient power sources are not considered. Consequently, even state-of-the-art SNN solutions tend to be inefficient. In this paper, a lightweight and effective detection model is proposed. With the help of rational algorithm design, the model integrates the advantages of SNNs as well as convolutional neural networks (CNNs). In addition to reducing resource usage, it maintains a high level of classification accuracy. The proposed model was evaluated against some current state-of-the-art models using a comprehensive set of metrics. Based on the experimental results, the model demonstrated improved adaptability to environments with limited computational resources and energy sources.

LA-YOLO: an effective detection model for multi-UAV under low altitude background

Detecting unmanned aerial vehicles (UAVs) in various environments and conditions is highly demanded in applications, and for solving the problem of detecting UAVs under low altitude background, we propose a high performance and effective LA-YOLO network by integrating the SimAM attention mechanism and introducing a fusion block with the normalized Wasserstein distance. By recording images of multi-UAV under low altitude background and annotating them, we construct a dataset called GUET-UAV-LA to evaluate the performance of the proposed network. Using the GUET-UAV-LA dataset and public datasets, the experiments validate the effectiveness of the proposed network and show that LA-YOLO can improve mAP by up to 5.9% compared to the existing networks.

Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation

Graph anomaly detection (GAD) has wide applications in real-world networked systems. In many scenarios, people need to identify anomalies on new (sub)graphs, but they may lack labels to train an effective detection model. Since recent semi-supervised GAD methods, which can leverage the available labels as prior knowledge, have achieved superior performance than unsupervised methods, one natural idea is to directly adopt a trained semi-supervised GAD model to the new (sub)graphs for testing. However, we find that existing semi-supervised GAD methods suffer from poor generalization issues, i.e., well-trained models could not perform well on an unseen area (i.e., not accessible in training) of the graph. Motivated by this, we formally define the problem of generalized graph anomaly detection that aims to effectively identify anomalies on both the training-domain graph(s) and the unseen test graph(s). Nevertheless, it is a challenging task since only limited labels are available, and the normal data distribution may differ between training and testing data. Accordingly, we propose a data augmentation method named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AugAN</i> ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aug</u> mentation for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> nomaly and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> ormal distributions) to enrich training data and adopt a customized episodic training strategy for learning with the augmented data. Extensive experiments verify the effectiveness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AugAN</i> in improving model generalizability.

The Detection of Fake News in Arabic Tweets Using Deep Learning

Fake news has been around for a long time, but the rise of social networking applications over recent years has rapidly increased the growth of fake news among individuals. The absence of adequate procedures to combat fake news has aggravated the problem. Consequently, fake news negatively impacts various aspects of life (economical, social, and political). Many individuals rely on Twitter as a news source, especially in the Arab region. Mostly, individuals are reading and sharing regardless of the truth behind the news. Identifying fake news manually on these open platforms would be challenging as they allow anyone to build networks and publish the news in real time. Therefore, creating an automatic system for recognizing news credibility on social networks relying on artificial intelligence techniques, including machine learning and deep learning, has attracted the attention of researchers. Using deep learning methods has shown promising results in recognizing fake news written in English. Limited work has been conducted in the area of news credibility recognition for the Arabic language. This work proposes a deep learning-based model to detect fake news on Twitter. The proposed model utilizes the news content and social context of the user who participated in the news dissemination. In seeking an effective detection model for fake news, we performed extensive experiments using two deep learning algorithms with varying word embedding models. The experiments were evaluated using a self-created dataset. The experimental results revealed that the MARBERT with the convolutional neural network (CNN) model scores a superior performance in terms of accuracy and an F1-score of 0.956. This finding proves that the proposed model accurately detects fake news in Arabic Tweets relating to various topics.

EW-YOLOv7: A Lightweight and Effective Detection Model for Small Defects in Electrowetting Display

In order to overcome the shortcomings of existing electrowetting display defect detection models in terms of computational complexity, structural complexity, detection speed, and detection accuracy, this article proposes an improved YOLOv7-based electrowetting display defect detection model. The model effectively optimizes the detection performance of display defects, especially small target defects, by integrating GhostNetV2 modules, Acmix attention mechanisms, and NGWD (Normalized Gaussian Wasserstein Distance) Loss. At the same time, it reduces the parameter size of the network model and improves the inference efficiency of the network. This article evaluates the performance of an improved model using a self-constructed electrowetting display defect dataset. The experimental results show that the proposed improved model achieves an average detection rate (mAP) of 89.5% and an average inference time of 35.9 ms. Compared to the original network, the number of parameters and computational costs are reduced by 19.2% and 64.3%, respectively. Compared with current state-of-the-art detection network models, the proposed EW-YOLOv7 exhibits superior performance in detecting electrowetting display defects. This model helps to solve the problem of defect detection in industrial production of electrowetting display and assists the research team in quickly identifying the causes and locations of defects.

Privacy-preserving malware detection in Android-based IoT devices through federated Markov chains

The continuous emergence of new and sophisticated malware specifically targeting Android-based Internet of Things devices is causing significant security hazards and is consequently fostering the need for effective detection models and strategies able to work with these hardware-constrained devices. In addition, since such models are often trained on confidential application data, many involved subjects are reluctant to share their data for this purpose. Accordingly, several Federated Learning-based solutions are emerging, which rely on the capabilities of Machine Learning models in malware detection/classification without sharing user data. However, Federated Learning methods are often adversely affected by non-independent and identically distributed data in terms of both the required training time and classification results. Therefore, a promising solution could be to overcome the Federated Learning-related issues by preserving the privacy of end-user data. In this direction, the capabilities of Markov chains and associative rules are extended within a federated environment to face malware classification tasks in the IoT scenario. The presented approach, evaluated on several malware families, has achieved an average accuracy of 99% in the presence of centralized and decentralized unbalanced training/testing data by overcoming the most common state-of-the-art approaches. Also, its runtime performance is comparable with centralized ones by considering several non-independent and identically distributed dataset partitions, splitting criteria, and clients, respectively.

Recalibrating Features and Regression for Oriented Object Detection

The objects in remote sensing images are normally densely packed, arbitrarily oriented, and surrounded by complex backgrounds. Great efforts have been devoted to developing oriented object detection models to accommodate such data characteristics. We argue that an effective detection model hinges on three aspects: feature enhancement, feature decoupling for classification and localization, and an appropriate bounding box regression scheme. In this article, we instantiate the three aspects on top of the classical Faster R-CNN, with three novel components proposed. First, we propose a weighted fusion and refinement (WFR) module, which adaptively weighs multi-level features and leverages the attention mechanism to refine the fused features. Second, we decouple the RoI (region of interest) features for the subsequent classification and localization via a lightweight affine transformation-based feature decoupling (ATFD) module. Third, we propose a post-classification regression (PCR) module for generating the desired quadrilateral bounding boxes. Specifically, PCR predicts the precise vertex location on each side of a predicted horizontal box, by simply learning the following: (i) classify the discretized regression range of the vertex, and (ii) revise the vertex location with an offset. We conduct extensive experiments on the DOTA, DIOR-R, and HRSC2016 datasets to evaluate our method.

Leveraging the first line of defense: a study on the evolution and usage of android security permissions for enhanced android malware detection

Android security permissions are built-in security features that constrain what an app can do and access on the system, that is, its privileges. Permissions have been widely used for Android malware detection, mostly in combination with other relevant app attributes. The available set of permissions is dynamic, refined in every new Android OS version release. The refinement process adds new permissions and deprecates others. These changes directly impact the type and prevalence of permissions requested by malware and legitimate applications over time. Furthermore, malware trends and benign apps’ inherent evolution influence their requested permissions. Therefore, the usage of these features in machine learning-based malware detection systems is prone to concept drift issues. Despite that, no previous study related to permissions has taken into account concept drift. In this study, we demonstrate that when concept drift is addressed, permissions can generate long-lasting and effective malware detection systems. Furthermore, the discriminatory capabilities of distinct set of features are tested. We found that the initial set of permissions, defined in Android 1.0 (API level 1), are sufficient to build an effective detection model, providing an average 0.93 F1 score in data that spans seven years. In addition, we explored and characterized permissions evolution using local and global interpretation methods. In this regard, the varying importance of individual permissions for malware and benign software recognition tasks over time are analyzed.

A Lightweight Framework for Obstacle Detection in the Railway Image Based on Fast Region Proposal and Improved YOLO-Tiny Network

The operation of trains is substantially jeopardized by the presence of railway obstacles. Recent research on vision-based railway obstacle detectors has emphasized using sizeable neural network models to pursue a high accuracy while neglecting power consumption and training costs. To accelerate the model updating, training, and deployment cycle applied in the railway safety guarantee system, this article proposes a lightweight three-stage detection framework to identify obstacles in the single railway image. It is mainly comprised of the coarse region proposal (CRP) module, the lightweight railway obstacle detection network (RODNet), and postprocessing stage. In the CRP module, we set the binarized normed gradient (BING) detector to work on two different scale images for a more effective generation of proposal regions. Then, the intersection-over-minimum (IoM) metric is presented to filter and merge generated subregions. In the RODNet, a more lightweight and effective detection model is designed based on substantial improvements of YOLOv4-tiny, to simultaneously predict bounding boxes on the whole image and in each subregion. The RODNet’s backbone is rebuilt with one-shot aggregation and attention mechanism to reduce parameters and to maintain feature extraction abilities. Moreover, some residual blocks are also integrated into RODNet’s neck and head to improve the detection performance. Finally, a series of operations is devised to fuse, remove, and suppress predicted boxes on the boundary of each candidate subregion to reduce false-positive results at the postprocessing stage. Experimental results in the railway obstacle dataset show that RODNet reduces 48% parameters and 29% floating-point operations per second (FLOPs) with at least a 68.2% improvement of inference speed in the CPU compared with YOLOv4-tiny. Meanwhile, the proposed framework provides 80.3 mean average precision (mAP) at a speed of 65.9 ms/pic in the low-power graphics processing unit (GPU) and achieves a competitive or even better performance compared with the state-of-the-art large detection models.

Unsupervised Deep Multitask Anomaly Detection With Robust Alarm Strategy for Online Evaluation of Bearing Early Fault Occurrence

Early fault detection of rolling bearings under online mode focuses on the evaluation of fault occurrence without system halt and is becoming a new research hotpot. In this problem, however, the monitoring data is generally collected in streaming form, lacking prior information about bearing&#x2019;s running status to build an effective detection model. Irregular noise interference in normal state may further make the detection model biased. Besides, the model is expected to work with less human involvement. Therefore, it is challenging to improve the detection robustness with streaming monitoring data and to avoid false alarms as well. To address this concern, this paper proposes a new unsupervised robust anomaly detection scheme. First, by introducing the idea of rule adaptation into convolutional neural network architecture, this paper builds a deep multi-task one-class anomaly detection model. This model can learn the task-shared feature representation and then transfer the anomaly detection rule from offline normal data to online unlabeled data. An alternative optimization algorithm is then provided to train this model. Furthermore, considering simultaneously the characteristics of bearing fault degradation and faulty data distribution, this paper proposes a new robust alarm strategy to determine the location of early fault occurrence via integrating permutation entropy and hypersphere-formed detection rule. Experimental results on the IEEE PHM Challenge 2012 bearing dataset and the XJTU-SY bearing dataset demonstrate that the proposed scheme can adaptively and accurately evaluate the occurrence of early fault with much lower false alarm rate. More importantly, the proposed scheme does not need to assume the starting part of online streaming data being in normal state, which is of better deployment and practical significance.

Selection of optimal wavelet features for epileptic EEG signal classification with LSTM

Epilepsy remains one of the most common chronic neurological disorders; hence, there is a need to further investigate various models for automatic detection of seizure activity. An effective detection model can be achieved by minimizing the complexity of the model in terms of trainable parameters while still maintaining high accuracy. One way to achieve this is to select the minimum possible number of features. In this paper, we propose a long short-term memory (LSTM) network for the classification of epileptic EEG signals. Discrete wavelet transform (DWT) is employed to remove noise and extract 20 eigenvalue features. The optimal features were then identified using correlation and P value analysis. The proposed method significantly reduces the number of trainable LSTM parameters required to attain high accuracy. Finally, our model outperforms other proposed frameworks, including popular classifiers such as logistic regression (LR), support vector machine (SVM), K-nearest neighbor (K-NN) and decision tree (DT).

Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection

Infrared dim small target detection is one of the important contents in the research of military applications such as remote sensing intelligence reconnaissance, long-range precision strike, aerospace offense–defense confrontation, etc. In this article, we focus on the detection of low-flying fixed-wing unmanned aerial vehicle target based on infrared imaging. To this end, we propose a simple and effective detection model, which can be viewed as a combination of low-rank approximation and multiple sparse constraints. We first model the infrared image that to be detected as a sum of three patch matrices called background, target, and noise. Then, we put a nonconvex low-rank approximation on the background patch matrix to suppress the background edges and put a reweighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1,1}$</tex-math></inline-formula> -norm constraint on the target matrix to better preserve the dim small target. Moreover, in order to eliminate the strong residual edges left in the target image under complex background, both the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1,1}$</tex-math></inline-formula> matrix norm and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{2,1}$</tex-math></inline-formula> matrix norm are used to constrain the noise patch. Finally, we develop an alternating optimization algorithm to solve the associated minimization problem. Extensive experiments carried out on a recently released real low-flying UAV database show that the proposed approach works well in detecting infrared dim small target measured by qualitative analysis and quantitative analysis.

Underwater Image Fish Recognition Technology Based on Transfer Learning and Image Enhancement

Yuan, H.; Zhang, S.; Chen, G., and Yang, Y., 2020. Underwater image fish recognition technology based on transfer learning and image enhancement. In: Hu, C. and Cai, M. (eds.), Geo-informatics and Oceanography. Journal of Coastal Research, Special Issue No. 105, pp. 124–128. Coconut Creek (Florida), ISSN 0749-0208.To effectively identify fish targets in underwater images, an image processing technology based on secondary migration learning and the Retinex algorithm is proposed to solve the problem of few underwater data sets and unclear underwater images. This method only uses a small-scale underwater image data set to train the network, and the Faster region-based convolutional neural networks (R-CNN) model can quickly detect underwater fish targets. The first transfer learning is applied between the ultra–large-scale ImageNet open-source dataset and the medium-scale Open Images high-definition fish dataset. Then, the Retinex iterative algorithm is used to enhance the underwater image to apply the second transfer learning between the high-resolution medium-scale fish data set and the small-scale underwater data set. Experiments show that this method can train a very effective detection model using small underwater image data sets at a low cost. The effect and performance of the detection model are far superior to traditional machine learning methods. This research can provide a certain reference value for deep-sea exploration, resource protection, and other engineering applications.

Deep Active Learning for Surface Defect Detection.

Most of the current object detection approaches deliver competitive results with an assumption that a large number of labeled data are generally available and can be fed into a deep network at once. However, due to expensive labeling efforts, it is difficult to deploy the object detection systems into more complex and challenging real-world environments, especially for defect detection in real industries. In order to reduce the labeling efforts, this study proposes an active learning framework for defect detection. First, an Uncertainty Sampling is proposed to produce the candidate list for annotation. Uncertain images can provide more informative knowledge for the learning process. Then, an Average Margin method is designed to set the sampling scale for each defect category. In addition, an iterative pattern of training and selection is adopted to train an effective detection model. Extensive experiments demonstrate that the proposed method can render the required performance with fewer labeled data.

A Method for Weak Target Detection Based on Human Visual Contrast Mechanism

In order to detect space weak targets in low signal-to-clutter ratio (SCR) environment, this letter presents an effective detection model. At the first stage, two-dimensional least-mean-square preprocessing part is applied to the original image, after which, the suspicious area can be obtained. At the second stage, a novel method based on the contrast mechanism of human visual system called neighborhood saliency map is applied, which improves the local contrast map algorithm and greatly increases the accuracy of neighborhood saliency estimation, meanwhile, effectively enhances targets and suppresses background. Then, a simple threshold segmentation is used to get real targets. Compared with other state-of-the-art algorithms, the proposed algorithm obtains the superior performance in terms of SCR gain, background suppression factor, and detection results (detection rate and false alarm rate). The model proposed in this letter can effectively detect spatial weak point targets in the case of average SCR ≈ 1 or even SCR <; 1 (with minimum SCR is about 0.55).

Inhibition of Voltage-Gated Calcium Channels After Subchronic and Repeated Exposure of PC12 Cells to Different Classes of Insecticides.

We previously demonstrated that acute inhibition of voltage-gated calcium channels (VGCCs) is a common mode of action for (sub)micromolar concentrations of chemicals, including insecticides. However, because human exposure to chemicals is usually chronic and repeated, we investigated if selected insecticides from different chemical classes (organochlorines, organophosphates, pyrethroids, carbamates, and neonicotinoids) also disturb calcium homeostasis after subchronic (24 h) exposure and after a subsequent (repeated) acute exposure. Effects on calcium homeostasis were investigated with single-cell fluorescence (Fura-2) imaging of PC12 cells. Cells were depolarized with high-K(+) saline to study effects of subchronic or repeated exposure on VGCC-mediated Ca(2+) influx. The results demonstrate that except for carbaryl and imidacloprid, all selected insecticides inhibited depolarization (K(+))-evoked Ca(2+) influx after subchronic exposure (IC50's: approximately 1-10 µM) in PC12 cells. These inhibitory effects were not or only slowly reversible. Moreover, repeated exposure augmented the inhibition of the K(+)-evoked increase in intracellular calcium concentration induced by subchronic exposure to cypermethrin, chlorpyrifos, chlorpyrifos-oxon, and endosulfan (IC50's: approximately 0.1-4 µM). In rat primary cortical cultures, acute and repeated chlorpyrifos exposure also augmented inhibition of VGCCs compared with subchronic exposure. In conclusion, compared with subchronic exposure, repeated exposure increases the potency of insecticides to inhibit VGCCs. However, the potency of insecticides to inhibit VGCCs upon repeated exposure was comparable with the inhibition previously observed following acute exposure, with the exception of chlorpyrifos. The data suggest that an acute exposure paradigm is sufficient for screening chemicals for effects on VGCCs and that PC12 cells are a sensitive model for detection of effects on VGCCs.