Effective Detection Method Research Articles

Improved Detection Method for Micro-Targets in Remote Sensing Images

With the exponential growth of remote sensing images in recent years, there has been a significant increase in demand for micro-target detection. Recently, effective detection methods for small targets have emerged; however, for micro-targets (even fewer pixels than small targets), most existing methods are not fully competent in feature extraction, target positioning, and rapid classification. This study proposes an enhanced detection method, especially for micro-targets, in which a combined loss function (consisting of NWD and CIOU) is used instead of a singular CIOU loss function. In addition, the lightweight Content-Aware Reassembly of Features (CARAFE) replaces the original bilinear interpolation upsampling algorithm, and a spatial pyramid structure is added into the network model’s small target layer. The proposed algorithm undergoes training and validation utilizing the benchmark dataset known as AI-TOD. Compared to speed-oriented YOLOv7-tiny, the mAP0.5 and mAP0.5:0.95 of our improved algorithm increased from 42.0% and 16.8% to 48.7% and 18.9%, representing improvements of 6.7% and 2.1%, respectively, while the detection speed was almost equal to that of YOLOv7-tiny. Furthermore, our method was also tested on a dataset of multi-scale targets, which contains small targets, medium targets, and large targets. The results demonstrated that mAP0.5:0.95 increased from “9.8%, 54.8%, and 68.2%” to “12.6%, 55.6%, and 70.1%” for detection across different scales, indicating improvements of 2.8%, 0.8%, and 1.9%, respectively. In summary, the presented method improves detection metrics for micro-targets in various scenarios while satisfying the requirements of detection speed in a real-time system.

Research Progress in Tumor Diagnosis Based on Raman Spectroscopy

Background: Cancer is a major disease that threatens human life and health. Raman spectroscopy can provide an effective detection method. Objective: The study aimed to introduce the application of Raman spectroscopy to tumor detection. We have introduced the current mainstream Raman spectroscopy technology and related application research. Methods: This article has first introduced the grim situation of malignant tumors in the world. The advantages of tumor diagnosis based on Raman spectroscopy have also been analyzed. Secondly, various Raman spectroscopy techniques applied in the medical field are introduced. Several studies on the application of Raman spectroscopy to tumors in different parts of the human body are discussed. Then the advantages of combining deep learning with Raman spectroscopy in the diagnosis of tumors are discussed. Finally, the related problems of tumor diagnosis methods based on Raman spectroscopy are pointed out. This may provide useful clues for future work. Conclusion: Raman spectroscopy can be an effective method for diagnosing tumors. Moreover, Raman spectroscopy diagnosis combined with deep learning can provide more convenient and accurate detection results.

The pyrolysis mechanism analysis of polystyrene and phenol–formaldehyde resin microplastics using mass spectrometry

As a new pollutant, microplastics (MPs) have characteristics such as environmental persistence, biological toxicity, bioaccumulation and so on, even at low concentrations in the nature. At present, the detection methods for MPs are still in the development stage, and mass spectrometry (MS) is one of the most effective methods for microplastic detection and analysis. This paper utilizes a self-developed temperature programmed system pyrolysis (Py) device and a time of flight mass spectrometer (TOFMS) to establish a Py-PI-TOFMS using a single photon ionization (PI) source, and to construct an analysis technology for microplastics. The pyrolysis of thermoplastic MPs mainly follows the free radical pyrolysis mechanism, where the energy obtained through heating reaches the bond energy connected between atoms, and the molecular chains undergo fracture to generate various small molecule compounds. We used Py-PI-TOFMS technology to analyze the pyrolysis products of microplastics of polystyrene (PS) and phenol–formaldehyde resin (PF), explore the rules of free radical reactions, composition of pyrolysis products, bond dissociation characteristics, and combine theoretical calculations to study the thermodynamic and kinetic mechanisms in the pyrolysis process.

A novel method for effective intrusion detection based on convolutional speaking neural networks

These days, most people can't imagine their life without their networks. People rely more and more on networks as new technologies like 5G and the Internet of Things proliferate, and networks are becoming larger and more complicated by the day. Consequently, the enormous networked society is under increasing danger from cyber attacks, which are also growing in number, sophistication, and variety. Users' private data can end up in the wrong hands. Data communicated via a network might be compromised in some way. It is also possible to target the computer infrastructure that is linked to the network. Thus, NIDS is crucial in ensuring that contemporary society's network communication environment is secure and dependable. This work proposes a detection model that is both lightweight and effective. This model is able to keep its high detection accuracy by combining the benefits of SNN and CNN with logical algorithm design. On top of that, it can bring attention to clear strengths in relation to computing efficiency and power usage. The suggested model was tested against a number of state-of-the-art models using an extensive and integrated set of benchmarks. With a 23 % improvement in detection accuracy (AD), a 19 % improvement in delay, and a 28 % improvement in consumption energy, the suggested technique outperformed the Spiking DFR + MLP and Spike-DHS methods in the simulations.

A Novel Palm Sugar-Mediated Hydrothermal Synthesis of Spherical Tin Oxide for Enhanced SO2 Gas Detection

Sulfur dioxide (SO2), a hazardous gas resulting from combustion and natural reactions, poses environmental and health risks. This study presents a novel approach to synthesize tin oxide (SnO2) using palm sugar-mediated hydrothermal methods for enhanced SO2 gas sensing. The simplicity and cost-effectiveness of the method are highlighted, addressing challenges posed by complex and resource-intensive conventional methods. Spherical SnO2 particles were successfully synthesized and characterized using SEM and XRD techniques. From the SEM image, it was known that SnO2 has spherical morphologies and is expected to agglomerate after being calcined. XRD analysis shows that SnO2 has a rutile tetragonal phase structure. The synthesized SnO2 demonstrated favorable gas sensing properties when exposed to SO2, exhibiting elevated response at increased temperatures and a linear relationship between response and gas concentration. The results indicate the potential of this method for effective and efficient SO2 detection.

The Matrix Profile in Seismology: Template Matching of Everything With Everything

AbstractTemplate matching has proven to be an effective method for seismic event detection, but is biased toward identifying events similar to previously known events, and thus is ineffective at discovering events with non‐matching waveforms (e.g., those dissimilar to existing catalog events). In principle, this limitation can be overcome by cross‐correlating every segment (possible template) of a seismogram with every other segment to identify all similar event pairs, but doing so has been previously considered computationally infeasible for long time series. Here we describe a method, called the ‘Matrix Profile’ (MP), a “correlate everything with everything” calculation that can be efficiently and scalably computed. The MP returns the maximum value of the correlation coefficient of every sub‐window of continuous data with every other sub‐window, as well as the best‐correlated sub‐window location. Here we show how MP methods can obtain valuable results when applied to months and years of continuous seismic data in both local and global case studies. We find that the MP can identify many new events in Parkfield, California seismicity that are not contained in existing event catalogs and that it can efficiently find clusters of similar earthquakes in global seismic data. Either used by itself, or as a starting point for subsequent template matching calculations, the MP is likely to provide a useful new tool for seismology research.

Investigating FGFR2 gene as a blood-based epigenetic biomarker in gastric cancer.

Gastric adenocarcinoma is a prevalent form of cancer that often remains undetected in its early stages due to the lack of specific symptoms. This delayed diagnosis leads to poor clinical outcomes, underscoring the need for an effective and non-invasive method for early detection. Recent advances in cancer epigenetics have led to the identification of biomarkers that have the potential to revolutionize the early detection and monitoring of this disease. One such promising biomarker is the methylation of the FGFR2 promoter. This study aims to measure the methylation levels of a specific CpG site in the FGFR2 promoter gene in DNA extracted from blood leukocytes from patients with intestinal metaplasia, gastric cancer, and healthy control. The CpG site of the FGFR2 gene promoter was identified in its control region. Methylation alteration of the selected FGFR2 CpG site was determined through the (methylation-sensitive restriction enzyme) MSRE-qPCR. Genomic DNA was extracted from one hundred twenty-five participants. The normal group had mean methylation levels of 93.23 ± 4.929%, while the IM group had a level of 69.85 ± 27.15%. In GC patients, the levels varied, with 25.96 ± 18.98% in the intestinal type and 28.30 ± 16.07% in the diffuse type. The methylation levels in the IM and GC patients were significantly lower than those in the normal control group. However, no significant difference was observed between the methylation status of the intestinal type of GC and the diffuse type. The Receiver operating characteristic (ROC) curve analysis showed that FGFR2 CpG methylation levels in GC patients compared to normal controls had a high sensitivity of 100% and specificity of 100%, with a cut-off of < 74.25%; when GC patients were compared to IM patients, the sensitivity was 85%, and the specificity was 80%, with a cut-off < 44.45%. The potential of the FGFR2 methylation status as a non-invasive biomarker lies in its ability to be detected in blood leukocytes, which makes it a promising tool for the early detection of intestinal metaplasia and gastric cancer. This could significantly improve the detection and management of these gastric conditions.

Rapid identification of MRSA directly from sterile body fluids by co-magnetic bead enrichment and MALDI-TOF mass spectrometry

As a result of the extensive use of antibiotics, methicillin-resistant Staphylococcus aureus (MRSA) has emerged, leading to serious clinical and epidemiological problems. This study established a rapid and accurate method for detection of MRSA in clinical samples. The MRSA detection procedure involved a magnetic separation technique and Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS), and the characteristic peaks of proteins were compared to achieve the detection of MRSA. We treated PBS, blood and cerebrospinal fluid with MRSA, and identified it after short-term culture. The time and detection limit for identifying MRSA were determined. At the same time, we used the method to detect different proportions of mixed bacteria. The results revealed that the enrichment efficiency of Amp-MBs for MRSA in PBS, blood and cerebrospinal fluid was greater than 95 %, 80.95 % and 83.6 %, respectively. The whole detection process could be completed within 1.5 h. The negative and positive percent agreement of MRSA detection were 83 % and 95 %, respectively. In mixed bacterial samples, bacteria can be detected when the proportion of MRSA is greater than 50 %. Overall, this study provides an effective method for bacterial preconcentration and detection, which has high potential for rapid and sensitive diagnosis of MRSA infection

AIE-YOLO: Effective object detection method in extreme driving scenarios via adaptive image enhancement.

The widespread research and implementation of visual object detection technology have significantly transformed the autonomous driving industry. Autonomous driving relies heavily on visual sensors to perceive and analyze the environment. However, under extreme weather conditions, such as heavy rain, fog, or low light, these sensors may encounter disruptions, resulting in decreased image quality and reduced detection accuracy, thereby increasing the risk for autonomous driving. To address these challenges, we propose adaptive image enhancement (AIE)-YOLO, a novel object detection method to enhance road object detection accuracy under extreme weather conditions. To tackle the issue of image quality degradation in extreme weather, we designed an improved adaptive image enhancement module. This module dynamically adjusts the pixel features of road images based on different scene conditions, thereby enhancing object visibility and suppressing irrelevant background interference. Additionally, we introduce a spatial feature extraction module to adaptively enhance the model's spatial modeling capability under complex backgrounds. Furthermore, a channel feature extraction module is designed to adaptively enhance the model's representation and generalization abilities. Due to the difficulty in acquiring real-world data for various extreme weather conditions, we constructed a novel benchmark dataset named extreme weather simulation-rare object dataset. This dataset comprises ten types of simulated extreme weather scenarios and is built upon a publicly available rare object detection dataset. Extensive experiments conducted on the extreme weather simulation-rare object dataset demonstrate that AIE-YOLO outperforms existing state-of-the-art methods, achieving excellent detection performance under extreme weather conditions.

Applicability of non-invasive, digital palpation device to detection of woody breast conditions in chicken breast muscle.

Woody breast (WB) is one of muscle myopathy found in chicken breast, characterized with enlarged size and extremely stiff texture. The WB condition is one of the most prevalent quality issues in the modern poultry industry. WB has been shown to be heritable, but no effective detection method of WB severity in live birds exists for the selection purpose. The objective of this study was to determine potential of a non-invasive, portable digital palpation device as WB detection method that can be used for the selection to estimate the heritability of WB. The physical and functional properties of WB was also investigated in comparison with normal breast (NB). Two hundred ten breast muscles were obtained from a local processing plant one day after harvest and sorted based on WB scoring (1 for NB and 2 and 3 for WB). The samples were subjected to physical and physicochemical analyses, determining biomechanical properties (muscle tone, stiffness, elasticity, relaxation, and creep), pH, color, cooking yield, and texture (firmness and compression energy were used for raw meat and shear force and energy for cooked meat). The least squares means of the following variables were significantly different between WB and NB (p < 0.01): stiffness (603.4 vs 565.8; N/m), and elasticity (1.40 vs 1.55). However, relaxation and creep were not significantly different (p > 0.05). These results collectively showed that biomechanical properties of WB differ from NB. The degree of muscle stiffness in WB can be considered as a trait to be selected. The WB score showed strong negative correlations with cooking yield (-0.77) and cooked L*(-0.74), which means that as the breast becomes harder, the cooking yield decreases, and the color becomes darker after cooking. The WB score showed high correlations with physical and functional characteristics and exhibited strong correlations with the biomechanical properties measured by the device. Therefore, the results indicated that the digital palpation device has potential to detect the WB severity (degree of stiffness) of breast muscle.

Research on a Partial Discharge Expert System for the Diagnosis of Damaged Transformation Equipment

Partial discharge (PD) characteristics are very important for the diagnosis of damaged transformation equipment. If the power transmission and transformation equipment fails, it will cause large economic losses, and thus prevention is better than treatment. One of the effective methods for high-voltage insulation degradation detection is to observe the phenomenon of partial discharge of equipment, which is the earliest characteristic. Prevention can be carried out in advance, and trend observation is better than periodic inspection. Long-term observation can effectively reduce the probability of misjudgment. This project intends to develop a high-speed data-acquisition device to acquire the original discharge waveform data of partial discharge for noise suppression. In order to improve the diagnostic efficiency and accuracy of the diagnostic system, it is necessary to suppress the noise of the measurement data, so as to carry out the fault identification of the discharge type. Through this method, the actual operating data of the system can be recorded as the original discharge waveform, in order to understand the original discharge waveform conditions of the power transmission and transformation equipment in multiple partial discharge measurements, and then after noise suppression, it can be regarded as a type of partial discharge identification.

Efficient pneumonia detection using Vision Transformers on chest X-rays

Pneumonia is a widespread and acute respiratory infection that impacts people of all ages. Early detection and treatment of pneumonia are essential for avoiding complications and enhancing clinical results. We can reduce mortality, improve healthcare efficiency, and contribute to the global battle against a disease that has plagued humanity for centuries by devising and deploying effective detection methods. Detecting pneumonia is not only a medical necessity but also a humanitarian imperative and a technological frontier. Chest X-rays are a frequently used imaging modality for diagnosing pneumonia. This paper examines in detail a cutting-edge method for detecting pneumonia implemented on the Vision Transformer (ViT) architecture on a public dataset of chest X-rays available on Kaggle. To acquire global context and spatial relationships from chest X-ray images, the proposed framework deploys the ViT model, which integrates self-attention mechanisms and transformer architecture. According to our experimentation with the proposed Vision Transformer-based framework, it achieves a higher accuracy of 97.61%, sensitivity of 95%, and specificity of 98% in detecting pneumonia from chest X-rays. The ViT model is preferable for capturing global context, comprehending spatial relationships, and processing images that have different resolutions. The framework establishes its efficacy as a robust pneumonia detection solution by surpassing convolutional neural network (CNN) based architectures.

A novel AllGlo probe-quantitative PCR method for detecting single nucleotide polymorphism in CYP2C19 to evaluate the antiplatelet activity of clopidogrel

CYP2C19 gene has multiple single nucleotide polymorphism (SNP), which is the major determinant for clopidogrel treatment responses. Therefore, CYP2C19 SNP detection is essential for predicting clopidogrel efficacy. Currently, there is still no quick and effective method for routine detection of common CYP2C19 SNPs in clinical laboratories, which is critically needed prior to clopidogrel treatment. AllGlo™ based quantitative PCR was used to develop a novel genotyping method for CYP2C19 SNP detection, termed CyPAllGlo. The performance of CyPAllGlo was compared with that of the commonly used fluorescence in situ hybridization (FISH) method, and the data was verified by DNA sequencing. CyPallGlo was used to identify CYP2C19 polymorphisms in 363 patients with coronary heart disease. The univariate analysis was used to access the antiplatelet efficacy of clopidogrel in patients. The associations between CYP2C19 polymorphisms and clopidogrel efficacy were analyzed. Using CyPAllGlo to detect CYP2C19*2 and CYP2C19*3 alleles was highly specific and fast. The detection limit was approximately 0.07 µg/µl and 0.7 µg/µl for CYP2C19*2 and CYP2C19*3, respectively. The consistency between FISH and CyPAllGlo were 98.07% for CYP2C19*2 and 99.17% for CYP2C19*3. DNA sequencing showed that the accuracy of CyPAllGlo was 100%. The analysis time for the whole CyPAllGlo procedure was approximately 60 min. Univariate analysis showed that the anticoagulation efficacy of clopidogrel was related to patient age, CYP2C19 genotype, metabolic phenotype, and LDL level. The logistic regression analysis showed that the genotype of CYP2C19 and metabolic phenotype was the two risk factors for clopidogrel antiplatelet ineffectiveness. This novel CyPAllGlo is a rapid and accurate method for detection of CYP2C19 SNP. The specificity and consistency of CyPAllGlo are comparable with that of widely used DNA sequencing. These findings provide valuable rapid method for predicting clopidogrel efficacy, which can be quickly translated to improve personalized precision medicine for coronary heart disease treatment.

Mild Cognitive Impairment detection based on EEG and HRV data

Brain volume decrease is usually connected to neurodegeneration and aging. In this environment, an important percentage of elderly persons suffer from mild cognitive impairment (MCI), a kind of dementia that can lead to Alzheimer's disease (AD). Since the symptoms of cognitive impairment are scarcely discernible, developing a safe and effective method for early MCI detection has emerged as an important challenge. According to this regard, numerous cognitive training tests can be targeted to help aging people retain a good quality of life, especially in the case of fragility disorders. A Consortium to Establish a Registry for Alzheimer's Disease (CERAD) task was initially created to detect the early stages of AD. This task specifically targets various tests related to specific cognitive domains. However, it has since developed into a popular diagnostic tool for many kinds of dementia, such as MCI. Several low-cost equipment, such as electroencephalography (EEG) and heart rate variability (HRV), may be useful for predicting MCI. On the other side, various machine learning (ML) models can be employed to extract/analyze relevant features from biomedical and physiological signals, especially in the context of anomaly detection and classification. To this regard, we developed a new method based on ML models to categorize MCI and healthy control (HC) patients during the CERAD task using EEG and HRV multimodal data. Our dataset includes 15 subjects who were randomly assigned to training and testing groups of 7 HC and 8 MCI, respectively. Our raw EEG and HRV data are analyzed to extract time, frequency, and non-linear features. A scaling step is employed to reduce the significant disparity between features. For the classification task, five ML models are evaluated, including support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), and gradient boosting (GB). To enhance accuracy, a hybrid ML model with a voting system is developed, combining the top ML models with the highest accuracy rates. A comparison step is performed between the use of ML and hybrid ML models. The experimental findings demonstrated the efficacy of our proposed technique, which included a hybrid ML model. An average accuracy of 93.86%, a sensitivity of 93.87%, and a specificity of 93.53% are achieved. The obtained results allow one to conclude that the first CERAD test plays a prominent role as a novel biomarker with an ultra-short duration for early MCI identification through the combination of EEG and HRV signals.

Edukasi Kesehatan dengan Triple Breast Self-Examination (BSE) Method pada Kader Kesehatan dalam Melakukan Skrining Kanker Payudara di Puskesmas Tamalanrea Jaya Kota Makassar

Breast cancer is the most common cancer found in women. Cancer is a non-communicable disease in which there is very rapid, uncontrolled growth and development of cells and tissues. Breast self-examination (BSE) is an effective method for screening and early detection of breast cancer. BSE can be done by everyone, making it easier to detect cancer. The purpose of this community service activity is to increase knowledge and skills to health cadres related to early detection of breast cancer using the Triple Breast Self-Examination (BSE) method. The educational methods used were counseling, demonstration and discussion. A total of 40 health cadres at Tamalanrea Jaya Health Center participated voluntarily. It is hoped that from this activity health cadres will be able to better recognize and understand the breast cancer early detection method, namely Triple Breast Self-Examination (BSE).

Alterations in Rumination, Eating, Drinking and Locomotion Behavior in Dairy Cows Affected by Subclinical Ketosis and Subclinical Acidosis

This study delves into the effects of subclinical ketosis (SCK) and subclinical acidosis (SCA) on various parameters related to dairy cow rumination, eating, drinking and locomotion behavior. The research hypothesized that these subclinical metabolic disorders could affect behaviors such as rumination, feeding, and locomotion. A total of 320 dairy cows, with a focus on those in their second or subsequent lactation, producing an average of 12,000 kg/year milk in their previous lactation, were examined. These cows were classified into three groups: those with SCK, those with SCA, and healthy cows. The health status of the cows was determined based on the milk fat–protein ratio, blood beta-hydroxybutyrate, and the results of clinical examinations performed by a veterinarian. The data collected during the study included parameters from the RumiWatch sensors. The results revealed significant differences between the cows affected by SCK and the healthy cows, with reductions observed in the rumination time (17.47%) and various eating and chewing behaviors. These changes indicated that SCK had a substantial impact on the cows’ behavior. In the context of SCA, the study found significant reductions in Eating Time 2 (ET2) of 36.84% when compared to the healthy cows. Additionally, Eating Chews 2 (EC2) exhibited a significant reduction in the SCA group, with an average of 312.06 units (±17.93), compared to the healthy group’s average of 504.20 units (±18.87). These findings emphasize that SCA influences feeding behaviors and chewing activity, which can have implications for nutrient intake and overall cow health. The study also highlights the considerable impact of SCK on locomotion parameters, as the cows with SCK exhibited a 27.36% reduction in the walking time levels. These cows also displayed reductions in the Walking Time (WT), Other Activity Time (OAT), and Activity Change (AC). In conclusion, this research underscores the critical need for advanced strategies to prevent and manage subclinical metabolic disorders within the dairy farming industry. The study findings have far-reaching implications for enhancing the well-being and performance of dairy cattle. Effective management practices and detection methods are essential to mitigate the impact of SCK and SCA on dairy cow health and productivity, ultimately benefiting the dairy farming sector.

Terahertz Spectroscopic Identification of Roast Degree and Variety of Coffee Beans

In this study, terahertz time-domain spectroscopy (THz-TDS) was proposed to identify coffee of three different varieties and three different roasting degrees of one variety. Principal component analysis (PCA) was applied to extract features from frequency-domain spectral data, and the extracted features were used for classification prediction through linear discrimination (LD), support vector machine (SVM), naive Bayes (NB), and k-nearest neighbors (KNN). The classification effect and misclassification of the model were analyzed via confusion matrix. The coffee varieties, namely Catimor, Typica 1, and Typica 2, under the condition of shallow drying were used for comparative tests. The LD classification model combined with PCA had the best effect of dimension reduction classification, while the speed and accuracy reached 20 ms and 100%, respectively. The LD model was found with the highest speed (25 ms) and accuracy (100%) by comparing the classification results of Typica 1 for three different roasting degrees. The coffee bean quality detection method based on THz-TDS combined with a modeling analysis method had a higher accuracy, faster speed, and simpler operation, and it is expected to become an effective detection method in coffee identification.

Advances in Non-Enzymatic electrochemical materials for H2O2 sensing

With the progression of society, an increasing emphasis on physical well-being and environmental safety has driven extensive research into technologies closely aligned with human health and environmental protection. Hydrogen peroxide (H2O2), a representative reactive oxygen species (ROS), serves as one of the products resulting from the single electron reduction of oxygen in organisms, playing a crucial role in cell signaling and maintaining internal stability. Relevant alterations of H2O2 concentrations have been implicated in the pathogenesis of various diseases, including diabetes and cancer. Moreover, the utilization of H2O2 is widely prevalent in various industries including chemical, pharmaceutical, and food sectors. However, the repetitive or uncontrolled utilization of H2O2 engenders deleterious implications for human well-being and environmental integrity. Therefore, there is immense significance in exploring a simple yet effective method for real-time and quantitative detection of H2O2 in living cells and actual samples. The use of nanocatalysts as sensing materials in the electrochemical detection method has garnered significant attention from researchers due to their operational simplicity, high sensitivity, cost-effectiveness, and facile miniaturization. However, there remains a dearth of comprehensive reviews on the electrochemical detection of H2O2 utilizing nanomaterials. In this paper, we focus on summarizing contemporary techniques for H2O2 detection with a specific emphasis on electrochemical detection methods, and highlighting the significant advancements in the field of electrochemical detection of H2O2 nanomaterials while also providing insights into future developments.

Deepfake Detection Using Spatiotemporal Transformer

Recent advances in generative models and the availability of large-scale benchmarks have made deepfake video generation and manipulation easier. Nowadays, the number of new hyper-realistic deepfake videos used for negative purposes is dramatically increasing, thus creating the need for effective deepfake detection methods. Although many existing deepfake detection approaches, particularly CNN-based methods, show promising results, they suffer from several drawbacks. In general, poor generalization results have been obtained under unseen/new deepfake generation methods. The crucial reason for the above defect is that CNN-based methods focus on the local spatial artifacts, which are unique for every manipulation method. Therefore, it is hard to learn the general forgery traces of different manipulation methods without considering the dependencies that extend beyond the local receptive field. To address this problem, this paper proposes a framework that combines aper proposes a framework that combines with Vision Transformer (ViT) to improve detection accuracy and enhance generalizability. Our method, named HCiT , exploits the advantages of CNNs to extract meaningful local features, as well as the VIT’s self-attention mechanism to learn discriminative global contextual dependencies in a frame-level image explicitly. In this hybrid architecture, the high-level feature maps extracted from the CNN are fed into the ViT model that determines whether a specific video is fake or real. Experiments were performed on Faceforensics++, DeepFake Detection Challenge preview, Celeb datasets, and the results show that the proposed method significantly outperforms the state-of-the-art methods. In addition, the HCiT method shows a great capacity for generalization on datasets covering various techniques of deepfake generation. The source code is available at: https://github.com/KADDAR-Bachir/HCiT

Data Validity Analysis Based on Reinforcement Learning for Mixed Types of Anomalies Coexistence in Intelligent Connected Vehicle (ICV)

Compared with traditional anomaly analysis, intelligent connected vehicle (ICV) data validity analysis is faced with a variety of data anomalies, including sensor anomalies, driving behavior anomalies, malicious tampering, and so on, which eventually leads to anomalies in the data. How to integrate the vehicle moving characteristics, driving style, and traffic flow conditions to provide an effective data detection method has become a new problem in the field of intelligent networked vehicles. Based on ICV data, a particle swarm optimization data validity detection algorithm (TE-PSO-SVM) was proposed by combining driving style and traffic flow theory to realize the effective detection of driving data. In addition, aiming at the problem of mixed types of anomalies in complex scenes, a model pool is constructed, and a model selection algorithm based on reinforcement learning (RLBMS) is proposed. Experiments on the real data set HighD show that RLBMS has a better detection effect in complex scenes of mixed types of anomalies.