Improve Detection Performance Research Articles

YOLOv8's advancements in tuberculosis identification from chest images.

Tuberculosis (TB) is a chronic and pathogenic disease that leads to life-threatening situations like death. Many people have been affected by TB owing to inaccuracy, late diagnosis, and deficiency of treatment. The early detection of TB is important to protect people from the severity of the disease and its threatening consequences. Traditionally, different manual methods have been used for TB prediction, such as chest X-rays and CT scans. Nevertheless, these approaches are identified as time-consuming and ineffective for achieving optimal results. To resolve this problem, several researchers have focused on TB prediction. Conversely, it results in a lack of accuracy, overfitting of data, and speed. For improving TB prediction, the proposed research employs the Selection Focal Fusion (SFF) block in the You Look Only Once v8 (YOLOv8, Ultralytics software company, Los Angeles, United States) object detection model with attention mechanism through the Kaggle TBX-11k dataset. The YOLOv8 is used for its ability to detect multiple objects in a single pass. However, it struggles with small objects and finds it impossible to perform fine-grained classifications. To evade this problem, the proposed research incorporates the SFF technique to improve detection performance and decrease small object missed detection rates. Correspondingly, the efficacy of the projected mechanism is calculated utilizing various performance metrics such as recall, precision, F1Score, and mean Average Precision (mAP) to estimate the performance of the proposed framework. Furthermore, the comparison of existing models reveals the efficiency of the proposed research. The present research is envisioned to contribute to the medical world and assist radiologists in identifying tuberculosis using the YOLOv8 model to obtain an optimal outcome.

Determining the best feature combination through text and probabilistic feature analysis for GPT-2-based mobile app review detection

Mobile apps, used by many people worldwide, have become an essential part of life. Before using a mobile app, users judge the reliability of apps according to their reviews. Therefore, app reviews are essential components of management for companies. Unfortunately, some fake reviewers write negative reviews for competing apps. Moreover, artificial intelligence (AI)-based macro bot programs that generate app reviews have emerged and can create large numbers of reviews with malicious purposes in a short time. One notable AI technology that can generate such reviews is Generative Pre-trained Transformer-2 (GPT-2). The reviews generated by GPT-2 use human-like grammar; therefore, it is difficult to detect them with only text mining techniques, which use tools like part-of-speech (POS) tagging and sentiment scores. Thus, probability-based sampling techniques in GPT-2 must be used. In this study, we identified features to detect reviews generated by GPT-2 and determined the optimal feature combination for improving detection performance. To achieve this, based on the analysis results, we built a training dataset to find the best feature combination for detecting the generated reviews. Various machine learning models were then trained and evaluated using this dataset. As a result, the model that used both text mining and probability-based sampling techniques detected generated reviews more effectively than the model that used only text mining techniques. This model achieved a top classification accuracy of 90% and a macro F1 of 0.90. We expect the results of this study to help app developers maintain a more stable mobile app ecosystem.Graphical abstract

Electrochemical Biosensors Composed of Polyethylenimine (PEI) and Graphene Derivatives for Rapid Detection of Alzheimer’s Disease

Graphene materials can be used as nanofillers in polymers to take advantage of graphene's suitable properties. Graphene-PEI composites are materials that combine the unique properties of graphene and PEI. These composites are made by combining graphene with PEI through physical or chemical method. The resulting material is characterized by enhanced mechanical properties, improved electrical conductivity and increased stability. By incorporating graphene into biosensor platforms, it can improve overall’s performance, including sensitivity, specificity and response. In this work, the detection of anti-βA40 biomarker was tested. Beta-amyloid peptide (βA1-40) was incorporated into liposomes of Dipalmitoyl Phosphatidylglycerol (DPPG) and was immobilized using Layer-by-Layer self-assembling technique on PEI_graphene derivatives composites. To obtain these composites it was utilized graphene oxide (GO) from Hummers’ method, it’s thermally reduced form (rGO) and also electrochemically exfoliated graphene (EEG). MilliQ water was added to the PEI to obtain a concentration of 2mg/mL and stirred for 30 min at room temperature for complete dissolution of the PEI. Then EEG, GO and rGO were added to PEI solution and subjected to ultrasonication in a Hielscher UP400ST sonicator, using the H14 probe for 30 minutes to obtain a homogeneous and stable dispersion.Carbon microelectrodes modified with PEI-graphene derivatives composites and (DPPG +Aβ-40) were used as biosensors platforms to detect anti-Aβ40 related to Alzheimer's disease. The assembly of the biosensors is shown in Figure 1. Cyclic Voltammetry was performed in three cycles, at a potential of -0.6 V to 0.6 V and scan rate of 0.05 V s-1 to evaluate the sensory response. The electrochemical measurements were performed with saline phosphate buffer solution (PBS). The morphology of PEI and PEI_graphene oxide can be seen in Figure 2. The large surface area of graphene allows for increased binding sites for bioreceptors, which may favor the adsorption of Aβ-40 peptide, improving signal transduction and sensitivity of the biosensor. The wettability of the composites was obtained through measurements of the static contact angle using the Ramé-Hart Contact Angle Goniometer (model 100-25-A). Figure 3 presents the contact angle for a film layer of the composites. All PEI_graphene derivative composites showed hydrophilic behavior (θ < 90°), PEI_EEG composite stood out as less hydrophilic (θ = 68°) among all composites. Hydrophilicity is essential in biosensors because it affects the interactions between the biological recognition elements and the target analytes, as well as the overall stability and reliability of the biosensor. Figure 4 presents the calibration curves using the normalized area obtained from cyclic voltammetry of the biosensors manufactured with 2 bilayers (PEI_graphene derivative/DPPG+Aβ40) and anti-Aβ40 biomarker in concentrations ranging from 1 ng mL to 5 µg mL-1. Among the tested PEI_graphene derivatives composites, the ones with EEG and RGO stood out in relation to pure PEI. Table 1 presents the adjusted R-square of the data in Figure 4. All composites, except PEI_GO, showed excellent linear fit, adjusted R-square close to 1 in the detection range between 1 and 1000 ng mL-1. The PEI_graphene oxide composite can be used in biosensors to enable rapid and efficient transduction of the binding signal, leading to improved detection performance. Figure 1

PhishHunter: Detecting camouflaged IDN-based phishing attacks via Siamese neural network

Phishing is one of the significant threats to cybersecurity today, especially when attackers create Internationalized Domain Names (IDN) homographs to engage in phishing activities. IDN homograph takes advantage of some characters in different native languages in internationalized domain names that look similar to legitimate ones. Although researchers have proposed several enlightening detection methods, most of them focused on detecting typosquatting domain names. The ones focused on IDN homograph attack detection either need to enhance the generalization ability or improve detection performance caused by data imbalance. In this paper, we devised a Generative Adversarial Network with a Gradient Penalty (WGAN-GP) algorithm to solve the data imbalance problem. We transform domain names into images and calculate their similarity by Siamese neural networks. Our work can identify whether a domain name is IDN homograph or not effectively. We use the dataset generated based on Unicode tables, publicly available homograph tools, and the Internet traffic captured from the China Education Research Network backbone (CERNET) to evaluate the performance. Experimental results show that the proposed method improves the accuracy and reduces the false positive rate in detecting homograph domain names. In addition, it can also accurately identify typosquatting in phishing pages.

Fuzzy logic-based fault detection in containerized data centers using digital twins

Containerized data centers (CDCs) have experienced rapid growth in recent years, owing to their modular and scalable nature. However, ensuring reliability and early fault detection in these complex systems is critical. This paper presents a novel Fuzzy Logic-based Fault Detection (FLFD) framework for CDCs using Digital Twins (DTs). The proposed approach employs DTs to create accurate virtual representations of the CDCs, which enable real-time monitoring and analysis of the physical systems. This paper focuses on three main aspects: (1) the development of a comprehensive DT model for CDCs, (2) the design and implementation of a FLFD algorithm, and (3) the validation of the proposed approach through extensive simulations and real-world case studies. The FLFD algorithm leverages fuzzy logic principles to identify and localize faults in the system, thereby enhancing the overall fault detection accuracy and reducing false alarms. Results demonstrate the effectiveness of the proposed framework, with significant improvements in fault detection performance and system reliability. The FLFD approach offers a promising solution for proactive maintenance and management in containerized data centers, paving the way for more efficient and resilient operations.

YOLOv8-CGRNet: A Lightweight Object Detection Network Leveraging Context Guidance and Deep Residual Learning

The growing need for effective object detection models on mobile devices makes it essential to design models that are both accurate and have fewer parameters. In this paper, we introduce a YOLOv8 Res2Net Extended Network (YOLOv8-CGRNet) approach that achieves enhanced precision under standards suitable for lightweight mobile devices. Firstly, we merge YOLOv8 with the Context GuidedNet (CGNet) and Residual Network with multiple branches (Res2Net) structures, augmenting the model’s ability to learn deep Res2Net features without adding to its complexity or computational demands. CGNet effectively captures local features and contextual surroundings, utilizing spatial dependencies and context information to improve accuracy. By reducing the number of parameters and saving on memory usage, it adheres to a ‘deep yet slim’ principle, lessening channel numbers between stages. Secondly, we explore an improved pyramid network (FPN) combination and employ the Stage Partial Spatial Pyramid Pooling Fast (SimPPFCSPC) structure to further strengthen the network’s capability in processing the FPN. Using a dynamic non-monotonic focusing mechanism (FM) gradient gain distribution strategy based on Wise-IoU (WIoU) in an anchor-free context, this method effectively manages low-quality examples. It enhances the overall performance of the detector. Thirdly, we introduce Unifying Object Detection Heads with Attention, adapting to various input scenarios and increasing the model’s flexibility. Experimental datasets include the commonly used detection datasets: VOC2007, VOC2012, and VisDrone. The experimental results demonstrate a 4.3% improvement in detection performance by the proposed framework, affirming superior performance over the original YOLOv8 model in terms of accuracy and robustness and providing insights for future practical applications.

Deep-Orga: An improved deep learning-based lightweight model for intestinal organoid detection

ProblemOrganoids are 3D cultures that are commonly used for biological and medical research in vitro due to their functional and structural similarity to source organs. The development of organoids can be assessed by morphological tests. However, manual analysis of organoid morphology requires intensive labor from professionals and is prone to observer discrepancies. AimComputer-assisted methods alleviate the pressure of manual labor, especially with the development of deep learning, the performance of morphological detection has been further improved. The aim of this paper is to automate the assessment of organoid morphology using deep learning techniques to reduce the labor pressure of professionals. MethodsBased on the lightweight model YOLOX, a lightweight intestinal organoid detection model named Deep-Orga is proposed. First, the performance of the Deep-Orga model is compared with other classical models on the intestinal organoids dataset. Then, ablation experiments are used to validate the improvement of the model detection performance by the improved module. Finally, Deep-Orga is compared with other methods. ResultsDeep-Orga achieves optimal organoid detection with a partial increase in computational effort. Using Deep-Orga to replace the manual analysis process provides a new automated method for organoid morphology evaluation. ConclusionDeep-Orga proposed in this paper is able to accurately assess organoid development, effectively relieving the labor pressure of professionals and avoiding the subjectivity of assessment. This paper demonstrates the potential application of deep learning in the field of organoid morphology analysis.

Defect detection in the presence of geometrical artefacts

Defect detection in non-destructive testing relies on the ability to detect signals arising from defects and distinguish them from those arising from noise. Ultrasonic arrays are capable of producing multiple views of a component through combinations of mode conversions and reflections of waves from boundaries, which potentially contain additional information about defects. Noise in these views can come from random sources such as electrical noise and thermal fluctuation, and coherent sources including stochastic microstructural noise, and boundary reflections giving rise to geometrical artefacts. The number of geometrical artefacts increases with both the geometrical complexity of the part under test, and with the number of reflections used to construct a view. This paper tackles the challenge of defect detection in the presence of geometrical artefacts by characterising the intensity distribution at each point in every view produced within a pristine sample, and using a hypothesis test as a means of defect detection. This information is combined with state-of-the-art data fusion routines, improving detection performance. Experimental work shows that this method improves performance compared to other methods of defect detection over the same scan area, which assume that the defect does not occupy the same location as an artefact in at least one view. Provided the pristine sample is characterised sufficiently well, hypothesis testing provides a robust method of defect detection in the presence of geometrical artefacts.

Weed Detection Method Based on Lightweight and Contextual Information Fusion

Weed detection technology is of paramount significance in achieving automation and intelligence in weed control. Nevertheless, it grapples with several formidable challenges, including imprecise small target detection, high computational demands, inadequate real-time performance, and susceptibility to environmental background interference. In response to these practical issues, we introduce CCCS-YOLO, a lightweight weed detection algorithm, built upon enhancements to the Yolov5s framework. In this study, the Faster_Block is integrated into the C3 module of the YOLOv5s neck network, creating the C3_Faster module. This modification not only streamlines the network but also significantly amplifies its detection capabilities. Subsequently, the context aggregation module is enhanced in the head by improving the convolution blocks, strengthening the network’s ability to distinguish between background and targets. Furthermore, the lightweight Content-Aware ReAssembly of Feature (CARAFE) module is employed to replace the upsampling module in the neck network, enhancing the performance of small target detection and promoting the fusion of contextual information. Finally, Soft-NMS-EIoU is utilized to replace the NMS and CIoU modules in YOLOv5s, enhancing the accuracy of target detection under dense conditions. Through detection on a publicly available sugar beet weed dataset and sesame weed datasets, the improved algorithm exhibits significant improvement in detection performance compared to YOLOv5s and demonstrates certain advancements over classical networks such as YOLOv7 and YOLOv8.

Target detection based on generalized Bures–Wasserstein distance

Radar target detection with fewer echo pulses in non-Gaussian clutter background is a challenging problem. In this instance, the conventional detectors using coherent accumulation are not very satisfactory. In contrast, the matrix detector based on Riemannian manifolds has shown potential on this issue since the covariance matrix of radar echo data during one coherent processing interval(CPI) has a smooth manifold structure. The Affine Invariant (AI) Riemannian distance between the cell under test (CUT) and the reference cells has been used as a statistic to achieve improved detection performance. This paper uses the Bures–Wasserstein (BW) distance and Generalized Bures–Wasserstein (GBW) distance on Riemannian manifolds as test statistics of matrix detectors, and propose relevant target detection method. Maximizing the GBW distance is formulated as an optimization problem and is solved by the Riemannian trust-region (RTR) method to achieve enhanced discrimination for target detection. Our evaluation of simulated data and measured data show that the matrix detector based on GBW distance leads to a significant performance gain over existing methods.

Molecular Self‐Assembly of Au Nanoparticles on a Porous Cellulose Membrane Template for Highly Sensitive Colorimetric Detection of Glucose

AbstractA sensitive glucose colorimetric detection system (Au@QCM) based on quaternized cellulose membrane (QCM) and enzyme‐mimicking Au nanoparticles (AuNPs) was successfully developed via a simple molecular self‐assembly strategy. The positively charged QCM functioned as a template to guide the negatively charged citric acid‐reduced AuNPs uniformly distributed on the surfaces along the pore walls of its porous structure, and thus gave AuNPs an ordered interconnected three‐dimensional network. This electrostatic interaction could effectively prevent the aggregation of the AuNPs and greatly increase their exposed surface area, thus endow the Au@QCM with highly active catalytic properties towards glucose oxidation with glucose oxidase (GOx). The experimental studies showed that the color change of redox indicator 3,3′,5,5′‐tetramethylbenzidine (TMB) can be clearly observed with naked eyes and the absorbance of the oxidized TMB (at 653 nm) was directly proportional to the concentration of glucose. Au@QCM could achieve a sensitive glucose detection with a limit of detection of 0.55 μM and linear response in the range of 0.01–0.5 mM. Besides, it also had good applicability in real samples detection and excellent selectivity to glucose. This novel detection system significantly improved detection performance compared to previous work based on cellulose‐based strips loaded with GOx and horseradish peroxides.

Exploiting stance similarity and graph neural networks for fake news detection

The widespread dissemination of fake news on social media has substantial economic and social implications. Although traditional propagation-based methods employing graph neural networks show promise for fake news detection, they disregard the influence of confirmation bias in the spread of fake news between users with similar viewpoints. This paper presents a detection approach that accounts for opinion similarity between users by scrutinizing their stances toward news articles and users’ post interactions. Using a graph transformer network, our method simultaneously extracts global structural information and interactions of similar stances. Furthermore, it addresses the challenges of stance analysis targeting microblogs while minimizing the effect of poorly represented stance features. We evaluated our approach using custom-crawled Twitter data and the benchmark FibVID dataset. It demonstrated a marked improvement in detection performance compared with conventional methods, including state-of-the-art methods.

One-bit spectrum sensing using Gustafson–Kessel fuzzy clustering for cognitive radio network

In this article, we address the problem of one-bit spectrum sensing using a multi-antenna array for cognitive radio networks. Spectrum sensing with one-bit analog-to-digital converter (ADC) is a far-reaching technology as it requires only a simple comparator for conversion and has the advantage of low-cost and high-speed. However, spectrum sensing with one-bit quantized data is challenging because of severe loss of information due to nonlinearities involved. Existing one-bit spectrum sensing techniques utilize arcsine law to approximate original signal covariance matrix from their quantized version and then analytically model the signal features for deriving the spectrum classification rule. However, these techniques are vulnerable to the inaccuracies of the model and it may not be at all possible to derive a classification rule for analytically intractable noise scenarios. In this article, we follow a data-driven approach for one-bit spectrum sensing and propose Gustafson–Kessel fuzzy c-means (GKFCM) clustering based algorithms for detection of primary user signals in white and correlated noise. We identify the features which are conserved in the process of quantization and form decision vectors using them. Decision vectors obtained from the received one-bit quantized data are then clustered using GKFCM to obtain the model parameters and classification is performed to identify the occupancy of the channel. Simulation results reveal that proposed algorithms provide significant improvement in detection performance compared to existing one-bit spectrum sensing techniques in literature.

Novel methodology for detecting and localizing cancer area in histopathological images based on overlapping patches

Cancer disease is one of the most important pathologies in the world, as it causes the death of millions of people, and the cure of this disease is limited in most cases. Rapid spread is one of the most important features of this disease, so many efforts are focused on its early-stage detection and localization. Medicine has made numerous advances in the recent decades with the help of artificial intelligence (AI), reducing costs and saving time. In this paper, deep learning models (DL) are used to present a novel method for detecting and localizing cancerous zones in WSI images, using tissue patch overlay to improve performance results. A novel overlapping methodology is proposed and discussed, together with different alternatives to evaluate the labels of the patches overlapping in the same zone to improve detection performance. The goal is to strengthen the labeling of different areas of an image with multiple overlapping patch testing. The results show that the proposed method improves the traditional framework and provides a different approach to cancer detection. The proposed method, based on applying 3x3 step 2 average pooling filters on overlapping patch labels, provides a better result with a 12.9% correction percentage for misclassified patches on the HUP dataset and 15.8% on the CINIJ dataset. In addition, a filter is implemented to correct isolated patches that were also misclassified. Finally, a CNN decision threshold study is performed to analyze the impact of the threshold value on the accuracy of the model. The alteration of the threshold decision along with the filter for isolated patches and the proposed method for overlapping patches, corrects about 20% of the patches that are mislabeled in the traditional method. As a whole, the proposed method achieves an accuracy rate of 94.6%. The code is available at https://github.com/sergioortiz26/Cancer_overlapping_filter_WSI_images.

Detecting Wilson's disease from unstructured connected speech: An embedding-based approach augmented by attention and bi-directional dependency

Wilson's disease (WD) is a neurodegenerative genetic disorder in which dysarthria is the initial neurological symptom. Automated WD diagnosis from speech is thus a promising and clinically valuable approach. The present study investigates the feasibility of WD detection from unstructured connected speech (UCS) using the embedding-based approach augmented by the attention mechanism and bi-directional dependency. The classification experiment was conducted with a sample of 55 WD patients and 55 matched healthy individuals. We compare the proposed embedding approach with two models: the baseline method using the structured task and the model using conventional acoustic features. Results show that the embedding-based model achieves the best accuracy of 90.3 %, which is 4.2 % and 7 % better than the baseline and acoustic approaches, respectively. The bi-directional semantic dependency and attention mechanism can significantly improve detection performance. Moreover, we reveal that the duration of the UCS task affects the model performance, with favorable results achieved using approximately 30 s epochs. Our method provides new insights into the detection of dysarthria-related disorders.

Improved Ship Detection with YOLOv8 Enhanced with MobileViT and GSConv

In tasks that require ship detection and recognition, the irregular shapes of ships and complex backgrounds pose significant challenges. This paper presents an advanced extension of the YOLOv8 model to address these challenges. A lightweight visual transformer, MobileViTSF, is proposed and combined with the YOLOv8 model. To address the loss of semantic information that arises from inconsistent scales in the detection of small ships, a layer intended for the detection of small targets is introduced to lead to improved fusion of deep and shallow features. Furthermore, the traditional convolution (Conv) blocks are replaced with GSConv blocks, and a novel GSC2f block is designed for fewer model parameters and improved detection performance. Experiments on a benchmark dataset suggest that this new model can achieve significantly improved accuracy for ship detection with fewer model parameters and a reduced model size. A comparison with several other state-of-the-art methods shows that higher accuracy can be obtained for ship detection with this model. Moreover, this new model is suitable for edge computing devices, demonstrating practical application value.

Research on parking space detection algorithm in complex environments based on improved YOLOv7

In the context of complex parking environments, vehicle parking space detection faces challenges such as multi-scale, multi-angle, and occlusion issues, leading to low detection efficiency and problems with false positives and false negatives. In this study, we propose an improved vehicle parking space detection algorithm based on YOLOv7. Firstly, we enhance the convolutional layers by introducing the Mish activation function, thereby improving the model’s feature extraction capabilities and its ability to represent objects effectively. Secondly, we combine the parameter-free attention mechanism SimAM with feature pyramid modules and feature extraction modules to replace certain convolutional layers, thereby enhancing the reinforcement of critical parking space information and adaptability to variations in target scale. Finally, we replace the nearest-neighbor interpolation in the upsampling section with the lightweight operator CARAFE, effectively extracting parking space feature information and enhancing the algorithm’s feature fusion capabilities. Through ablation experiments and comparative trials on publicly available parking space datasets, our improved YOLOv7 algorithm achieves an mAP of 78.7%. Compared to the original algorithm, it demonstrates a 1.5% improvement in detection performance and a 5.6% increase in recall rate. These enhancements significantly improve parking space detection in complex environments, addressing issues such as false positives and false negatives, thereby meeting the performance requirements of parking space detection in parking lots.

Ship detection and identification in SDGSAT-1 glimmer images based on the glimmer YOLO model

ABSTRACTRemote sensing technology has been widely used for marine monitoring. However, due to the limitations of sensor technologies and data sources, effective monitoring of marine ships at night remains challenging. To address these challenges, our study developed SDGST, a high-resolution glimmer marine ship dataset from SDGSAT-1 satellite and proposed a ship detection and identification method based on the YOLOv5s model, the Glimmer YOLO model. Considering the characteristics of glimmer images, our model has made several effective improvements to the original YOLOv5s model. In particular, the improved model incorporates a new layer for detecting small targets and integrates the CA (Coordinate Attention) mechanism. To enhance the original feature fusion strategy, we introduced BiFPN (Bi-directional Feature Pyramid Network). We also adopted the EIOU Loss function and replaced the initially defined anchors with clustering results, thus improving detection performance. The mean Average Precision (mAP%) reaches 96.7%, which is a 5.1% improvement over the YOLOv5s model. Notably, it significantly improves the detection of small ships. This model demonstrates superior performance in ship detection under glimmer conditions compared to the original YOLOv5s model and other popular target detection models, and may serve as a valuable reference for achieving high-precision nighttime marine monitoring.

Enhanced Pelican Optimization Algorithm with Deep Learning-Driven Mitotic Nuclei Classification on Breast Histopathology Images.

Breast cancer (BC) is a prevalent disease worldwide, and accurate diagnoses are vital for successful treatment. Histopathological (HI) inspection, particularly the detection of mitotic nuclei, has played a pivotal function in the prognosis and diagnosis of BC. It includes the detection and classification of mitotic nuclei within breast tissue samples. Conventionally, the detection of mitotic nuclei has been a subjective task and is time-consuming for pathologists to perform manually. Automatic classification using computer algorithms, especially deep learning (DL) algorithms, has been developed as a beneficial alternative. DL and CNNs particularly have shown outstanding performance in different image classification tasks, including mitotic nuclei classification. CNNs can learn intricate hierarchical features from HI images, making them suitable for detecting subtle patterns related to the mitotic nuclei. In this article, we present an Enhanced Pelican Optimization Algorithm with a Deep Learning-Driven Mitotic Nuclei Classification (EPOADL-MNC) technique on Breast HI. This developed EPOADL-MNC system examines the histopathology images for the classification of mitotic and non-mitotic cells. In this presented EPOADL-MNC technique, the ShuffleNet model can be employed for the feature extraction method. In the hyperparameter tuning procedure, the EPOADL-MNC algorithm makes use of the EPOA system to alter the hyperparameters of the ShuffleNet model. Finally, we used an adaptive neuro-fuzzy inference system (ANFIS) for the classification and detection of mitotic cell nuclei on histopathology images. A series of simulations took place to validate the improved detection performance of the EPOADL-MNC technique. The comprehensive outcomes highlighted the better outcomes of the EPOADL-MNC algorithm compared to existing DL techniques with a maximum accuracy of 97.83%.

Modified Threshold-based Spectrum Sensing Approach for VANETs

The Primary User (PU) signal detection in Cognitive Radio (CR) is crucial and is achieved through spectrum sensing techniques. The Energy Detection method is a commonly used technique, and selecting a proper threshold is essential to enhance the efficiency of the CR system. This research paper demonstrates the maximum achievable throughput and validates a Modified Threshold (MT) approach. The authors consider a scenario with multiple antennas at the receiver, where these antennas are correlated and subjected to mobility effects, and they employ the Energy Detection (ED) for spectrum sensing. The study analyzes the system's performance over a Nakagami-m fading channel, considering available correlations among the antenna elements. To compute important statistical values, the Moment Generating Function (MGF) method is employed. The research employs specialized mathematical functions, such as the Lauricella and confluent hypergeometric functions, to derive closed-form expressions for the Probability of Detection when employing the diversity technique. The results indicate a significant enhancement in the performance of the proposed algorithm when utilizing the modified threshold parameter across a wide range of Signal to Noise Ratio (SNR) values. Additionally, increasing the number of branches in the antenna system further improves detection performance. Interestingly, under high fading parameter conditions (m=4), the detection probability is found to be superior with exponential correlation among the L antenna elements compared to other available correlated branches.