High Complexity Research Articles

DualTrans: A Novel Glioma Segmentation Framework Based on a Dual-Path Encoder Network and Multi-View Dynamic Fusion Model

Segmentation methods based on convolutional neural networks (CNN) have achieved remarkable results in the field of medical image segmentation due to their powerful representation capabilities. However, for brain-tumor segmentation, owing to the significant variations in shape, texture, and location, traditional convolutional neural networks (CNNs) with limited convolutional kernel-receptive fields struggle to model explicit long-range (global) dependencies, thereby restricting segmentation accuracy and making it difficult to accurately identify tumor boundaries in medical imaging. As a result, researchers have introduced the Swin Transformer, which has the capability to model long-distance dependencies, into the field of brain-tumor segmentation, offering unique advantages in the global modeling and semantic interaction of remote information. However, due to the high computational complexity of the Swin Transformer and its reliance on large-scale pretraining, it faces constraints when processing large-scale medical images. Therefore, this study addresses this issue by proposing a smaller network, consisting of a dual-encoder network, which also resolves the instability issue that arises in the training process of large-scale visual models with the Swin Transformer, where activation values of residual units accumulate layer by layer, leading to a significant increase in differences in activation amplitudes across layers and causing model instability. The results of the experimental validation using real data show that our dual-encoder network has achieved significant performance improvements, and it also demonstrates a strong appeal in reducing computational complexity.

CTITF: A tensor factorization model with constrained bidirectional user trust and implicit feedback for context-aware recommender systems

Recommender systems offer an efficient solution to the problem of information overload, which is exacerbated by the rapid expansion of data. Context-aware recommender systems (CARS) have become prominent, incorporating contextual information to provide more desirable recommendations. While the dimension of contextual information rises, the complexity of algorithms increases as well, highlighting the data sparsity problem. In this paper, we propose CTITF, a novel tensor factorization model that incorporates constrained user bidirectional trust and implicit feedback. First, CTITF distinguishes the roles of trust networks in terms of the directionality of trust relationships: the trustor and the trustee. CTITF then constructs trust influences and biases for both roles to linearly represent user preferences. Furthermore, CTITF improves the rating prediction function and recommendation objective function based on CP factorization. Finally, CTITF filters out trust relationships where users do not have the same history items. It also uses contextual information to limit the amount of implicit feedback, addressing the issue of high time complexity. Experiments on two real-world datasets illustrate that the proposed model outperforms previous TF-based models and social recommender systems in terms of rating prediction accuracy. In addition, the CTITF model reduces the training time by 50.28% compared to the trustTF model, which also uses trust.

PHSI-RTDETR: A Lightweight Infrared Small Target Detection Algorithm Based on UAV Aerial Photography

To address the issues of low model accuracy caused by complex ground environments and uneven target scales and high computational complexity in unmanned aerial vehicle (UAV) aerial infrared image target detection, this study proposes a lightweight UAV aerial infrared small target detection algorithm called PHSI-RTDETR. Initially, an improved backbone feature extraction network is designed using the lightweight RPConv-Block module proposed in this paper, which effectively captures small target features, significantly reducing the model complexity and computational burden while improving accuracy. Subsequently, the HiLo attention mechanism is combined with an intra-scale feature interaction module to form an AIFI-HiLo module, which is integrated into a hybrid encoder to enhance the focus of the model on dense targets, reducing the rates of missed and false detections. Moreover, the slimneck-SSFF architecture is introduced as the cross-scale feature fusion architecture of the model, utilizing GSConv and VoVGSCSP modules to enhance adaptability to infrared targets of various scales, producing more semantic information while reducing network computations. Finally, the original GIoU loss is replaced with the Inner-GIoU loss, which uses a scaling factor to control auxiliary bounding boxes to speed up convergence and improve detection accuracy for small targets. The experimental results show that, compared to RT-DETR, PHSI-RTDETR reduces model parameters by 30.55% and floating-point operations by 17.10%. Moreover, detection precision and speed are increased by 3.81% and 13.39%, respectively, and mAP50, impressively, reaches 82.58%, demonstrating the great potential of this model for drone infrared small target detection.

Micron-sized fiber diamond probe for quantum precision measurement of microwave magnetic field

Abstract We present a quantitative measurement of the horizontal component of the microwave magnetic field of a coplanar waveguide using a quantum diamond probe in fiber format. The measurement results are compared in detail with simulation, showing a good consistence. Further simulation shows fiber diamond probe brings negligible disturbance to the field under measurement compared to bulk diamond. This method will find important applications ranging from electromagnetic compatibility test and failure analysis of high frequency and high complexity integrated circuits.

Classification of Coffee Fruit Maturity Level based on Multispectral Image Using Naïve Bayes Method

The current research about the classification of coffee fruit ripeness based on multispectral images has been developed using the Convolutional Neural Network (CNN) method to extract patterns from highdimensional multispectral images. The high complexity of CNN allows the model to capture complex features but requires more time and computational resources for model training and testing. Therefore, in this study, classification is performed using a more straightforward method such as Naïve Bayes because its complexity only depends on the number of features and samples. The method only considers each feature independently, so it has high speed and does not require a lot of computational resources. Naïve Bayes is applied to color and texture features extracted from multispectral images of coffee fruit. There are 300 features consisting of 60 color features and 240 texture features. Experiments were conducted based on the comparison of training and testing data and the use of each feature. The combination of color and texture features showed better performance than color or texture features alone, with the highest accuracy reaching 91.01%. In conclusion, using Naïve Bayes is still reasonably good in classifying the ripeness of coffee fruit based on multispectral images.

Quantifying the features of earthquake clusters in north-Central Iran, based on nearest-neighbor distances and network analysis

The statistical features of earthquake clusters in North-Central Iran (Tehran Region) are investigated, with the aim of quantitatively characterizing the properties of earthquake triggering and allow exploring their possible relations with the tectonic setting of the study area.The nearest-neighbor approach is used for the identification of the earthquake clusters in the space-time-energy domain. This approach permits for a data-driven identification of clusters so that, within multi-event clusters, the features of secondary and higher orders dependent events can be explored. The study is based on a revised dataset that is extracted from the catalog compiled by the Iranian Seismological Center (IRSC) for the period of 1996–2022. In order to exclude the effect of non-tectonic events, which turn out quite numerous within the study region, explosions within quarry-rich areas are removed; the identification of non-tectonic events is performed by considering the normalized ratios of daytime to nighttime events in an iterative removal procedure. According to preliminary analysis of the resulting catalog, an area is selected, within which a satisfactory completeness level is assessed for events with magnitude >2.0. Robust values of the scaling parameters, namely the b-value and the fractal dimension of epicenters, are also computed and are used to calculate the nearest-neighbor distances and to identify the earthquake clusters.The nearest-neighbor method also permits to investigate the internal structure of earthquake sequences, and to differentiate the spatial properties of seismicity according to the different topological features of the clusters structure. The obtained results allow us identifying two macro-areas, approximately separated by the 52°E meridian, which are characterized by different clustering features, namely: high complexity indexes, indicating simple (burst-like) structure of clusters, to the East; low complexity index, corresponding to complex multi-level (swarm-like) structure of clusters, to the West. The complexity measures, borrowed from network theory (i.e. the Closeness and Outdegree Centralization indexes), consistently capture the complexity of the identified clusters, and confirm that the cluster structures have distinct preferred geographic locations. The territorial heterogeneity of the examined clustering properties can be related with the spatial variability of tectonic, structural and geophysical features of the Alborz region, in good agreement with findings from the Alps-Dinarides junction (Northeastern Italy), a region also characterized by a contractional structural setting, mainly including reverse and strike-slip faulting systems, and by moderate to high seismic activity.

Handheld echocardiography for the screening and diagnosis of rheumatic heart disease: a systematic review to inform WHO guidelines

Early detection and diagnosis of acute rheumatic fever and rheumatic heart disease are key to preventing progression, and echocardiography has an important diagnostic role. Standard echocardiography might not be feasible in high-prevalence regions due to its high cost, complexity, and time requirement. Handheld echocardiography might be an easy-to-use, low-cost alternative, but its performance in screening for and diagnosing acute rheumatic fever and rheumatic heart disease needs further investigation. In this systematic review and meta-analysis, we searched Embase, MEDLINE, LILACS, and Conference Proceedings Citation Index-Science up to Feb 9, 2024, for studies on the screening and diagnosis of acute rheumatic fever and rheumatic heart disease using handheld echocardiography (index test) or standard echocardiography or auscultation (reference tests) in high-prevalence areas. We included all studies with useable data in which the diagnostic performance of the index test was assessed against a reference test. Data on test accuracy in diagnosing rheumatic heart disease, acute rheumatic fever, or carditis with acute rheumatic fever (primary outcomes) were extracted from published articles or calculated, with authors contacted as necessary. Quality of evidence was appraised using GRADE and QUADAS-2 criteria. We summarised diagnostic accuracy statistics (including sensitivity and specificity) and estimated 95% CIs using a bivariate random-effects model (or univariate random-effects models for analyses including three or fewer studies). Area under the curve (AUC) was calculated from summary receiver operating characteristic curves. Heterogeneity was assessed by visual inspection of plots. This study was registered with PROSPERO (CRD42022344081). Out of 4868 records we identified 11 studies, and two additional reports, comprising 15 578 unique participants. Pooled data showed that handheld echocardiography had high sensitivity (0·87 [95% CI 0·76-0·93]), specificity (0·98 [0·71-1·00]), and overall high accuracy (AUC 0·94 [0·84-1·00]) for diagnosing rheumatic heart disease when compared with standard echocardiography (two studies; moderate certainty of evidence), with better performance for diagnosing definite compared with borderline rheumatic heart disease. High sensitivity (0·79 [0·73-0·84]), specificity (0·85 [0·80-0·89]), and overall accuracy (AUC 0·90 [0·85-0·94]) for screening rheumatic heart disease was observed when pooling data of handheld echocardiography versus standard echocardiography (seven studies; high certainty of evidence). Most studies had a low risk of bias overall. Some heterogeneity was observed for sensitivity and specificity across studies, possibly driven by differences in the prevalence and severity of rheumatic heart disease, and level of training or expertise of non-expert operators. Handheld echocardiography has a high accuracy and diagnostic performance when compared with standard echocardiography for diagnosing and screening of rheumatic heart disease in high-prevalence areas. World Health Organization. For the Chinese, French, Italian, Persian, Portuguese, Spanish and Urdu translations of the abstract see Supplementary Materials section.

Identifying imaging challenges for 1D and 3D geometric complexity (LG space)

In some situations, it is difficult to image seismic data even when factors such as illumination, anisotropy, and attenuation are resolvable. An often-overlooked but dominant factor affecting imaging is the geometric complexity of subsurface reflectors. In some of these settings, we propose that geometric complexity plays a dominant role in the quality of seismic imaging. This has been documented in subsalt regions with poor image quality where 3D vertical seismic profiles (VSPs) are available. VSPs often demonstrate that there is good illumination below salt, but the complexity of the observed downgoing wave and diffractions cannot be simulated with smooth earth models. We use synthetic examples with high geometric complexity and no other complications to analyze the imaging process: a 2D sediment-salt model with rough salt-top topography, and a 1D model with complex reflectivity. In these models, it is difficult to estimate velocity, and it is challenging to image the data. These synthetic examples are useful to understand the limitations of imaging with smooth models, to create guidelines to identify geometric complexity in field data, and to develop possible mitigations to improve imaging. Further real data examples will be needed to test geometric complexity.

Lesion-symptom Mapping of Acceptability Judgments in Chronic Poststroke Aphasia Reveals the Neurobiological Underpinnings of Receptive Syntax.

Disagreements persist regarding the neural basis of syntactic processing, which has been linked both to inferior frontal and posterior temporal regions of the brain. One focal point of the debate concerns the role of inferior frontal areas in receptive syntactic ability, which is mostly assessed using sentence comprehension involving complex syntactic structures, a task that is potentially confounded with working memory. Syntactic acceptability judgments may provide a better measure of receptive syntax by reducing the need to use high working memory load and complex sentences and by enabling assessment of various types of syntactic violations. We therefore tested the perception of grammatical violations by people with poststroke aphasia (n = 25), along with matched controls (n = 16), using English sentences involving errors in word order, agreement, or subcategorization. Lesion data were also collected. Control participants performed near ceiling in accuracy with higher discriminability of agreement and subcategorization violations than word order; aphasia participants were less able to discriminate violations, but, on average, paralleled control participants discriminability of types of violations. Lesion-symptom mapping showed a correlation between discriminability and posterior temporal regions, but not inferior frontal regions. We argue that these results diverge from models holding that frontal areas are amodal core regions in syntactic structure building and favor models that posit a core hierarchical system in posterior temporal regions.

Iterative hybrid compressive sensing-based channel estimation method for intelligent reflecting surface-supported millimeter wave systems

Iterative hybrid compressive sensing-based channel estimation method for intelligent reflecting surface-supported millimeter wave systems

FTPE-BC: Fast thumbnail-preserving image encryption using block-churning

With the boom in personal images, cloud services are beginning to take on a significant role in image storage. However, images uploaded to the cloud will face new privacy threats. Image privacy can be effectively protected using traditional encryption methods, but the encrypted images are snowflake-like noisy images, which makes the images unusable. Lately, Thumbnail Preserving Encryption (TPE) is proposed, which relieves this conflict between image availability and privacy in cloud platforms by retaining thumbnails unchanged after encryption. Existing TPE schemes for intra-block grouping usually utilize a lot of pseudo-random functions with high computational complexity, which makes encryption and decryption time too long. We propose a new model, block-churning, and propose a new TPE scheme based on this model. In this scheme, all pixels in the block are churned as a whole. This fundamentally solves the problem of low encryption efficiency caused by intra-block grouping, and greatly improves the encryption speed, while being able to change pixel values more significantly in blocks. The experiment shows that the encrypted image achieves a good balance between availability and privacy.

Advanced CKD detection through optimized metaheuristic modeling in healthcare informatics

Data categorization is a top concern in medical data to predict and detect illnesses; thus, it is applied in modern healthcare informatics. In modern informatics, machine learning and deep learning models have enjoyed great attention for categorizing medical data and improving illness detection. However, the existing techniques, such as features with high dimensionality, computational complexity, and long-term execution duration, raise fundamental problems. This study presents a novel classification model employing metaheuristic methods to maximize efficient positives on Chronic Kidney Disease diagnosis. The medical data is initially massively pre-processed, where the data is purified with various mechanisms, including missing values resolution, data transformation, and the employment of normalization procedures. The focus of such processes is to leverage the handling of the missing values and prepare the data for deep analysis. We adopt the Binary Grey Wolf Optimization method, a reliable subset selection feature using metaheuristics. This operation is aimed at improving illness prediction accuracy. In the classification step, the model adopts the Extreme Learning Machine with hidden nodes through data optimization to predict the presence of CKD. The complete classifier evaluation employs established measures, including recall, specificity, kappa, F-score, and accuracy, in addition to the feature selection. Data related to the study show that the proposed approach records high levels of accuracy, which is better than the existing models.

MOX-NET: Multi-stage deep hybrid feature fusion and selection framework for monkeypox classification

Background:Monkeypox virus has quickly expanded throughout several nations, raising serious public health concerns. Lack of precautionary measures raises concerns about the possibility of global pandemic. As a result, early detection of monkeypox is essential for efficient diagnosis and treatment before broad community transmission. Artificial intelligence (AI) and machine learning (ML) have recently shown tremendous promise in early monkeypox diagnosis using image-based diagnoses methods. But, challenges such as low contrast lesions, overloaded datasets, high memory complexity, and redundant feature extraction still need to be addressed. Methods:This work proposes a coalesced CAD system for monkeypox classification using a deep learning framework. The proposed approach includes multiple stages: First, the given dataset images are pre-processed by a proposed fusion-based contrast enhancement method. Second, six pre-trained deep learning models i.e., Vision Transformers (ViT), Shifted Windows (Swin) Transformers, ResNet-50, ResNet-101, EfficientNetV2, and ConvNeXt-V2 are modified and transfer learning is used for training. Third, from all the deep learning networks, the deep feature vectors are acquired and integrated through the convolutional sparse image decomposition fusion strategy. Subsequently, an entropy-controlled firefly feature selection approach is applied to identify the most suitable features for classification. Lastly, the multi-class support vector machine is utilized to classify the input data based on the selected features. Results:The proposed algorithm utilized the MSLID dataset and obtained an accuracy of 98.64% which is better than other state-of-the-art works. Conclusions:Experimental outcomes reveals that the proposed monkeypox classification algorithm outperforms well-established existing algorithms in both visual and quantitative evaluations, resulting in improved accuracy.

Adaptive three-way KNN classifier using density-based granular balls

The granular ball k-nearest neighbors (GBKNN) algorithm improves the efficiency and robustness of traditional k-nearest neighbors (KNN) by replacing point input with granular ball. However, in the generation process of granular balls by GBKNN, there may be unbalanced distribution of data points existed in some granular balls, which will cause more classification errors. In addition, the fixed value of k in GBKNN may also reduce the accuracy of classification. In order to address these issues, an adaptive three-way KNN classifier using density-based granular balls is proposed. Firstly, an improved density-based granular ball computing using density peak clustering is presented. This method introduces a refined threshold to subdivide the granular balls. Secondly, a data-driven neighborhood is defined to search the optimal k value and a density-based granular ball KNN (DBGBKNN) algorithm is proposed. Thirdly, by considering the fuzziness of the testing set in the classification process, the density-based granular ball KNN with three-way decision (DBGBKNN-3WD) is constructed. Finally, experimental results verify that DBGBKNN-3WD achieves high comprehensive score and low time complexity while maintaining less fuzziness loss than other algorithms.

Patterns of vancomycin use in high-risk febrile neutropenia.

e23224 Background: Febrile neutropenia (FN) is a common complication in patients with hematologic malignancy. ASCO guidelines on the management of FN endorse empiric anti-Pseudomonas coverage while reserving the use of agents targeting gram-positive organisms and methicillin-resistant Staphylococcus aureus (MRSA) for select criteria-defined patients such as those with severe mucositis, hemodynamic instability, radiographic pneumonia, or history of MRSA colonization among others. We sought to examine our institutional management of FN, hypothesizing that local use of empiric vancomycin for high-risk FN will deviate from ASCO guidelines reflected in institutional policy. Methods: Case data from 1/1/2016 – 6/1/2023 was collected retrospectively, comprising patients admitted to the general medical floor with known leukemia, lymphoma, or multiple myeloma meeting ASCO criteria for FN. Patients admitted to intensive care at time of FN or for less than 48-hours were excluded. For each patient, use of vancomycin at time of FN diagnosis (t0) and 48-hours later (t48) was evaluated for congruence with society guidelines. Results: 91 patients were reviewed with a median age of 68 years old (IQR 59-74, 15). Median length of stay (LOS) was 20 days (IQR 7-29, 22) with FN occurring on day seven of hospitalization on average. Most patients receiving vancomycin were admitted to the hematology specialty unit at time of diagnosis (63%). Acute leukemia was the most common malignancy represented (62.8%), while high grade lymphoma comprised 23.1%. 98.9% of patients were on active chemotherapy at time of FN. 81 patients (89%) received vancomycin as empiric treatment for FN with 76 (93.8%) receiving vancomycin at t0. Of this latter cohort, 29.7% of patients received vancomycin at t0 without indication. At t48, vancomycin was discontinued in 28.4% of patients, 44.4% received vancomycin with true indication, and 27.2% were continued on vancomycin without indication. The median duration of therapy for patients receiving vancomycin incongruently at t48 was 4 days. Of those meeting criteria for vancomycin use at t48, severe mucositis and soft tissue infection were each cited in 22.2% of cases, followed by pneumonia, persistent fever, and hemodynamic instability. Notably, the presence of any grade mucositis was significantly associated with the continued use of empiric vancomycin at t48 (p = .025). Admission to hematology specialty unit did not significantly affect adherence to FN management guidelines. Conclusions: These data suggest that the use of vancomycin for FN at our institution deviates from society guidelines including on specialized hematology wards. This study was underpowered to assess adverse outcomes seen with vancomycin use such as renal injury, prolonged LOS and greater cost of care. Given the high complexity of this patient population, detailed provider education is indicated to promote safe, evidence-based care and broader antimicrobial stewardship.

Musical retail therapy: toward a conceptual framework on the impact of musical elements on consumer mood, attention, and decision-making

PurposeThis paper aims to provide a comprehensive framework on how and when musical elements such as keys, complexity, tempo and volume influence consumers’ mood, attention, information recall, product evaluation and purchase decisions in the context of retail therapy.Design/methodology/approachThis paper builds on a critical literature review to develop a conceptual framework and formulate relevant propositions, leveraging self-regulation and self-control theories.FindingsThrough our analysis of these studies, we found that in musical retail therapy, minor keys paired with high complexity can intensify negative moods and negative product evaluation. Conversely, major keys combined with low complexity can moderate negative and positive moods, leading to positive product evaluations. We argue that high tempo and high volume can invoke high arousal and low impulse control, leading to low attention and low recall. This subsequently moderates the negative mood, resulting in weak negative product evaluation. Low tempo and low volume, however, invoke low arousal and high impulse control, leading to high attention and high recall, which moderates negative mood and negative product evaluation.Originality/valueReflecting on the limitations of the existing studies, this conceptual work proposes a comprehensive theoretical framework for understanding the significance of musical elements in retail therapy that can enhance consumers moods and experiences. Further, the current paper is different from the existing literature in that it helps academic researchers and marketers understand different ways to use musical elements that can positively affect consumer behavior in a complex situation like retail therapy.

Enhancing spinnability and properties of carbon fibers through modification of isotropic coal tar pitch precursor

Producing general-purpose carbon fibers (GPCFs) with excellent properties using an economical raw material such as coal tar pitch (CTP) holds significant importance. To synthesize carbon fibers, the spinnability of precursor material plays an important role. However, the CTP impedes flowability during the spinning process due to possessing high molecular weight complex aromatic structures. To address this limitation, CTP was blended with varying proportions of petroleum pitch (5, 10 and 20 %) and spinnability and flow behavior were analyzed using Rheology measurements. The distinct constitutional properties of petroleum pitch also contributed to alterations in both the stabilization and carbonization processes. Carbon fibers produced from 10PP showed the highest tensile strength and modulus of ∼784 MPa and ∼47 GPa, respectively. The impact of oxidative and carbonization treatments on the resultant microstructural and mechanical properties of carbon fibers was thoroughly examined using various techniques such as FTIR, XPS, XRD, SEM, Raman and UTM. The results showed that the composition of petroleum pitch influenced the stabilization of fibers which eventually affected the carbonization process and resultant properties of carbon fibers. This study offers an understanding of the connection between the precursor's composition, stabilization conditions, and mechanical properties of the resultant carbon fibers.

Desiccation Tolerance of Epiphytic Macrolichens in an Evergreen Temperate Rain Forest (Alerce Costero National Park, Chile).

The Valdivian region has a temperate rainy climate with differences in rainfall throughout the year. This heterogeneity results in periods of summer drought that expose the poikilohydric epiphytes to desiccation. With this research, we aim to answer different research questions related to phorophyte preference, response to desiccation, and response to radiation. How does the diversity of macrolichens vary at a local and microclimate scale in three tree species within an evergreen forest? What is the tolerance limit of macrolichens against prolonged desiccation, according to evaluation of the maximum efficiency of PSII (Fv/Fm) and pigment concentration? What is the tolerance limit against a potential increase in radiation? We found that macrolichen communities are determined by tree species, which regulate the suitability of the substrate by modifying the temperature and humidity conditions. In addition, our results show a rapid photosynthetic alteration in temporal exposure to desiccation, measured through Fv/Fm and pigment concentration. Our results showed that the most sensitive lichens to radiation and desiccation are not coincident. We confirm the low tolerance of macrolichen species to high radiation, reflected in the saturation profile obtained for the set studied. The lichen community in the evergreen forest showed high complexity and vulnerability, pointing to the importance of more research.

Reduced protein kinase C delta in a high molecular weight complex in mitochondria and elevated creatine uptake into Barth syndrome B lymphoblasts

Reduced protein kinase C delta in a high molecular weight complex in mitochondria and elevated creatine uptake into Barth syndrome B lymphoblasts

An expert system for privacy-driven vessel detection harnessing YOLOv8 and strengthened by SHA-256

The integration of deep learning with security is undergoing rapid and substantial growth across numerous fields. This combination has led to propelled advancements in securing maritime surveillance data. Applications utilizing deep learning require large amounts of data to deliver precise results. However, Deep learning applications face significant issues like low accuracy, high computational complexity, and GPU utilization. Moreover, security features such as integrity and authenticity are notably absent. Hence, in this paper privacy preserved deep learning-based vessel monitoring system is developed which includes evaluation of integrity and authenticity-based surveillance. This model ensures accuracy without compromising on one way authenticity while also optimizing GPU utilization. Therefore, the YOLOv8 model is integrated with SHA-256 for tracking and classification, also ensuring data integrity and authentication of vessel data. The model is fed with class-balanced, unstructured dataset comprising 693 photo-realistic video sequences. It has three phases which are used for feature extraction and bounding box prediction and also includes CSPDarkNet53 as backbone, Spatial Pyramid Pooling (SPP) as neck layer and detection is done using head layer. They employ a 3 × 3 convolution for better feature extraction. The proposed model provides better performance than other state-of-the-art methods. Specifically, YOLOv8 achieves a 9.3 % increase in precision over YOLOv7.