Traditional Techniques Research Articles

Multiscale and multidisciplinary data-driven reservoir characterization of a fractured carbonate field in Kurdistan

Abstract The combination of traditional subsurface interpretation techniques with advanced data analytics is a key stepping stone for better predicting reservoir quality, especially in heterogeneous and complex geological systems. The Peshkabir oil and gas field, located in the north of the Kurdistan Region of Iraq and within the Tawke Production Sharing Contract, is one such heterogeneous system. Well oil rates vary significantly across the field and cannot be simply correlated to fracture densities measured at the wells. Understanding which fractures matter and what influences reservoir deliverability is a question of major importance for maximizing oil production. The carbonate reservoirs include karstified vuggy zones and hydrothermal dolostones, in addition to an extensively developed fractured network. This paper presents a geological conceptual model for the Peshkabir field, and an application of Python based data science techniques to identify key predictors of reservoir deliverability from drilling, logging and production data. We demonstrate that the major advantage of the application of advanced data analytics is that it can enable the recognition of patterns and associations in a complex, high-dimensional parameter environment whereas traditional interpretation methods typically only allow for the comparison of two or three parameters at a time. This method allows the integration of dynamic and static data effectively and empowers the interpreter to incorporate all the available insights which, coupled with domain knowledge, allows for data-driven decision-making.

Effectiveness of NOTES versus traditional techniques on surgical outcomes in adults with diverticulitis: a systematic review protocol.

This review will evaluate the effectiveness of natural orifice transluminal endoscopic surgery (NOTES)/hybrid NOTES versus traditional surgical techniques in the management of diverticular disease. Diverticular disease can have a significant impact on a patient's quality of life, especially the changes affecting bowel function. Recurrent/severe symptoms may require surgery. Resections are invasive and can have significant operative complications. There has been a shift from an open resection to a more minimally invasive technique such as laparoscopy. Furthermore, the use of natural orifice transluminal endoscopic resection, using a transanal/transvaginal approach, has begun to be used in colorectal resections. Eligible studies will include patients who underwent surgical resection for diverticular disease or diverticulitis. The review will consider studies that evaluate NOTES in the management of diverticular disease or diverticulitis. This will include hybrid procedures involving both NOTES and natural orifice specimen extraction (NOSE), with the aid of laparoscopy/endoscopy/robotic equipment. Studies on participants under 18 years of age and presenting with non-diverticular pathologies will be excluded. This review will follow the JBI methodology for systematic reviews of effectiveness and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Databases to be searched will include PubMed, MEDLINE (Ovid), CINAHL (EBSCOhost), Cochrane Library, Web of Science, Scopus, ProQuest, ClinicalTrials.gov, and ANZCTR, with no language limitations. The reference lists of included studies will be screened for additional studies. This review will preferentially consider experimental and quasi-experimental study designs. Two reviewers will conduct critical appraisal and data extraction. Studies will, where possible, be pooled in statistical meta-analyses. PROSPERO CRD42023420771.

Generative Adversarial Network for Wireless Communication: Principle, Application, and Trends

Generative adversarial network (GAN) has attracted wide attention because of its remarkable ability to learn highdimensional and complex data distributions based on game theory and machine learning. In this paper, we focus on the role that GANs play in wireless communications, which has been well researched and developed in recent years. With the gradual increase in communication rates and the complicacy of communication scenarios, some urgent issues have emerged, such as complex channel generation, high-dimensional channel estimation, and insufficient real-world signal acquisition. The powerful nonlinear fitting property of GAN can break through the bottleneck of traditional communication techniques, whose applications can be categorized into data generation, performance optimization, and data classification according to different purposes and network structures. For each category, a universal network structure summarizing the characteristics and targets is investigated, and the performance of GANs in communication is also analyzed with simulation examples. Moreover, the open research issues are discussed at the end to provide some directions for future study.

Distributed Estimation of Support Vector Machines for Matrix Data.

Discrimination problems are of significant interest in the machine learning literature. There has been growing interest in extending traditional vector-based machine learning techniques to their matrix forms. In this article, we investigate the statistical properties of the nuclear-norm-based regularized linear support vector machines (SVMs), in particular establishing the convergence rate of the estimator in the high-dimensional setting. Furthermore, within the distributed estimation paradigm, we propose a communication-efficient estimator that can achieve the same convergence rate. We illustrate the performances of the estimators via some simulation examples and an empirical data analysis.

Identification Of Traditional Foods In West Pasaman District

This study aims to learn more about traditional food in West Pasaman District. The research was conducted August-December 2023 in West Pasaman District.The purpose of this study was to identify the types of food, ingredients used and traditional food processing techniques in West Pasaman District. The type of research used is a mix method (qualitative and quantitative). This research data was obtained from two ways, namely primary data and secondary data. Primary data was conducted to identify the types of traditional food, ingredients used and traditional food processing techniques in West Pasaman District. While secondary data is in the form of demographics of West Pasaman District. Data collection techniques are carried out by observation, interviews, and documentation. Data analysis is done by reducing data, presenting data, and concluding. The conclusion of the research results is that traditional food in West Pasaman Regency is a typical food that has existed for a long time. The ingredients used are food ingredients developed by yourself. As well as traditional food processing techniques that are typical in West Pasaman District.

Comparative study of surface roughness between electropolishing process and traditional sanding for low carbon steel

Steel is a versatile material utilized across various sectors, encompassing both industrial and decorative applications. AISI (American Iron and Steel Institute) 1005 steel, recognized for its low carbon content (approximately 0.05% carbon), is commonly referred to as mild steel and finds extensive use in society. This study aims to investigate the influence of electropolishing parameters, specifically current density, and time, on the surface roughness of steel. The methodology involved the preparation of samples for electropolishing tests, systematic variation of the parameters, and subsequent analysis of the resulting surface finish through roughness measurements. Samples subjected to higher current densities for shorter durations exhibited substantial reductions in roughness, while longer durations resulted in minimal or negligible changes in roughness regardless of the current density employed. This comparative analysis between electropolishing and tradi-tional sanding techniques provides valuable insights into optimizing surface finishing processes for low carbon steel applications.

StegEraser: Defending cybersecurity against malicious covert communications

Traditionally, the mission of intercepting malicious traffic between the Internet and the internal network of entities like organizations and corporations, is largely fulfilled by techniques such as deep packet inspection (DPI). However, steganography, the methodology of hiding secret data in seemingly benign public mediums (e.g., images), has been leveraged by advanced persistent threat (APT) groups in recent years, and is almost impossible to be detected and intercepted by traditional techniques, posing a pervasive and realistic threat to cybersecurity. Additionally, internal networks’ vulnerability to steganography is further exacerbated by the connectivity and large attack surface of the Internet of Things (IoT), whose adoption and deployment are quickly expanding. To protect computer systems against malicious communications that apply steganographic methods potentially unknown to cybersecurity stakeholders, we propose StegEraser, an approach to removing the secret information embedded in public mediums by adversaries, that is fundamentally distinct from existing research which is primarily designed for known steganographic methods. Implemented for images, StegEraser injects an excessively huge amount of random binary data with a novel steganographic method into the images, by utilizing the information-merging capabilities of invertible neural networks (INNs), in order to “overload” adversaries’ steganographic hiding capacity of images transmitted through the firewall performing DPI. In the meantime, StegEraser preserves the perceptual quality of the images. In other words, StegEraser “defeats unknown steganography with steganography”. Extensive evaluation verifies that StegEraser significantly outperforms state-of-the-art (SOTA) methods in terms of removing secret information embedded with both traditional and neural network-based steganographic methods, while visually maintaining the image quality.

GeoNLPlify: A spatial data augmentation enhancing text classification for crisis monitoring

Crises such as natural disasters and public health emergencies generate vast amounts of text data, making it challenging to classify the information into relevant categories. Acquiring expert-labeled data for such scenarios can be difficult, leading to limited training datasets for text classification by fine-tuning BERT-like models. Unfortunately, traditional data augmentation techniques only slightly improve F1-scores. How can data augmentation be used to obtain better results in this applied domain? In this paper, using neural network explicability methods, we aim to highlight that fine-tuned BERT-like models on crisis corpora give too much importance to spatial information to make their predictions. This overfitting of spatial information limits their ability to generalize especially when the event which occurs in a place has evolved and changed since the training dataset has been built. To reduce this bias, we propose GeoNLPlify,1 a novel data augmentation technique that leverages spatial information to generate new labeled data for text classification related to crises. Our approach aims to address overfitting without necessitating modifications to the underlying model architecture, distinguishing it from other prevalent methods employed to combat overfitting. Our results show that GeoNLPlify significantly improves F1-scores, demonstrating the potential of the spatial information for data augmentation for crisis-related text classification tasks. In order to evaluate the contribution of our method, GeoNLPlify is applied to three public datasets (PADI-web, CrisisNLP and SST2) and compared with classical natural language processing data augmentations.

Downlink Scheduler for Delay Guaranteed Services Using Deep Reinforcement Learning

In this paper, we propose a novel scheduling scheme to guarantee per-packet delay in single-hop wireless networks for delay-critical applications. We consider several classes of packets with different delay requirements, where high-class packets yield high utility after successful transmission. Considering the correla-tionship of delays among competing packets, we apply a delay-laxity concept and introduce a new output gain function for scheduling decisions. Particularly, the selection of a packet takes into account not only its output gain but also the delay-laxity of other packets. In this context, we formulate a multi-objective optimization problem aiming to minimize the average queue length while maximizing the average output gain under the constraint of guaranteeing per-packet delay. However, due to the uncertainty in the environment (e.g., time-varying channel conditions and random packet arrivals), it is difficult and often impractical to solve this problem using traditional optimization techniques. We develop a deep reinforcement learning (DRL)-based framework to solve it. Specifically, we decompose the original optimization problem into a set of scalar optimization subproblems and model each of them as a partially observable Markov Decision Process (POMDP). We then resort to a Double Deep Q Network (DDQN)-based algorithm to learn an optimal scheduling policy for each subproblem, which can overcome the large-scale state space and reduce Q-value overestimation. Simulation results show that our proposed DDQN-based algorithm outperforms the conventional Q-learning algorithm in terms of reward and learning speed. In addition, our proposed scheduling scheme can achieve significant reductions in average delay and delay outage drop rate compared to other benchmark schemes.

Deep Learning Approaches and Biomarkers in Medical Diagnosis

Abstract: The size of the medical information system is growing gradually. Due to this, traditional data analysis for extracting helpful information for any disease has become inefficient in providing accurate real-time valid information. Traditional data mining and statistical learning techniques, which lack sufficient domain knowledge on a complicatedly colossal amount of data, need to stop adopting new advances in deep learning technologies. Thus, the use of sophisticated machines with in-depth understanding and analysis capability is vital to provide real-time information in detecting and diagnosing diseases in the healthcare system. In this paper, we study recent deep learning approaches which are capable of working on high-dimensional and multi-dimensional data. These approaches have been deployed to identify the root cause of various diseases like Cancer, Lung Diseases, Heart Diseases, Diabetes, Hepatitis, Alzheimer’s, Dengue, Parkinson’s, etc. : In this study, our key contributions are: 1: A decent overview of the deep learning techniques. 2: Several modalities in the diagnosis of various diseases. 3: Set out recent trends in various applications of deep learning in healthcare, some of which are analysis and diagnosis of medical images, precision medicine, drug discovery, predictive analysis to support clinical decisions, and sustainable public health. 4: Several deep learning approaches with their performance are described in detail. 5: Although deep learning has achieved notable performance in detecting AD, there are several limitations, especially the availability of the patient’s data for training deep learning models on a particular disease is comparatively much less than required.

An advanced ensemble load balancing approach for fog computing applications

Fog computing has emerged as a viable concept for expanding the capabilities of cloud computing to the periphery of the network allowing for efficient data processing and analysis from internet of things (IoT) devices. Load balancing is essential in fog computing because it ensures optimal resource utilization and performance among distributed fog nodes. This paper proposed an ensemble-based load-balancing approach for fog computing environments. An advanced ensemble load balancing approach (AELBA) uses real-time monitoring and analysis of fog node metrics, such as resource utilization, network congestion, and service response times, to facilitate effective load distribution. Based on the ensemble's collective decision-making, these metrics are fed into a centralized load-balancing controller, which dynamically adjusts the load distribution across fog nodes. Performance of the proposed ensemble load-balancing approach is evaluated and compared it to traditional load-balancing techniques in fog using extensive simulation experiments. The results demonstrate that our ensemble-based approach outperforms individual load-balancing algorithms regarding response time, resource utilization, and scalability. It adapts to dynamic fog environments, providing efficient load balancing even under varying workload conditions.

Robust Policy Iteration of Uncertain Interconnected Systems With Imperfect Data

This paper investigates the robust optimal control problem of a class of continuous-time, partially linear, interconnected systems. In addition to the dynamic uncertainties resulted from the interconnected dynamic system, unknown bounded disturbances are taken into account throughout the learning process, wherein the system’s dynamics and the disturbances are assumed unknown. These challenges lead the collected online data to be imperfect. In this scenario, traditional data-driven control techniques, such as adaptive dynamic programming (ADP) and robust ADP, encounter a challenge in approximating the optimal control policy precisely due to imperfect data and computational errors. In this paper, a novel data-driven robust policy iteration method is proposed to simultaneously solve the robust optimal control problems. Without relying on the knowledge of the system’s dynamics, the external disturbances or the complete state, the implementation of the proposed method only needs to access the input and partial state information. Based on the small-gain theorem, the notions of strong unboundedness observability and input-to-output stability, it is guaranteed that the learned robust optimal control gain is stabilizing and that the solution of the closed-loop system is uniformly ultimately bounded despite the existence of dynamic uncertainties and unknown external disturbances. The simulation results reveal the efficiency and practicality of the proposed data-driven control method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by the use of reinforcement learning to improve the quality of designing adaptive optimal controllers for engineering applications. Adaptive dynamic programming methods, in particular policy iteration (PI), are widely used in solving optimal control problems. However, due to the iterative nature of PI, the approximated optimal control policy may be inaccurate and imprecise. Especially, when using imperfect system’s measurements instead of the modelling information. This can result in causing the learned control policy to deviate from the actual optimal policy. This becomes more challenging in the existence of dynamic uncertainties and unknown external disturbances which corrupt the measurements and result in imperfect data. This work investigates the conditions on the uncertainties such that the proposed novel data-driven PI algorithm is robust to system’s uncertainties, unknown external disturbances and imperfect measurements. The approximated robust optimal control policy performs robustly in the existences of imperfect data and uncertainties, and at the same time is close enough to the optimal control policy.

MoReVis: A Visual Summary for Spatiotemporal Moving Regions.

Spatial and temporal interactions are central and fundamental in many activities in our world. A common problem faced when visualizing this type of data is how to provide an overview that helps users navigate efficiently. Traditional approaches use coordinated views or 3D metaphors like the Space-time cube to tackle this problem. However, they suffer from overplotting and often lack spatial context, hindering data exploration. More recent techniques, such as MotionRugs, propose compact temporal summaries based on 1D projection. While powerful, these techniques do not support the situation for which the spatial extent of the objects and their intersections is relevant, such as the analysis of surveillance videos or tracking weather storms. In this article, we propose MoReVis, a visual overview of spatiotemporal data that considers the objects' spatial extent and strives to show spatial interactions among these objects by displaying spatial intersections. Like previous techniques, our method involves projecting the spatial coordinates to 1D to produce compact summaries. However, our solution's core consists of performing a layout optimization step that sets the size and positions of the visual marks on the summary to resemble the actual values on the original space. We also provide multiple interactive mechanisms to make interpreting the results more straightforward for the user. We perform an extensive experimental evaluation and usage scenarios. Moreover, we evaluated the usefulness of MoReVis in a study with 9 participants. The results point out the effectiveness and suitability of our method in representing different datasets compared to traditional techniques.

A survey on brain MRI segmentation

Neurological disorders pose a significant challenge in medical diagnostics, requiring accurate and efficient analysis of brain Magnetic Resonance Imaging (MRI) scans. This study introduces a novel approach for enhanced neurological diagnosis through the application of deep learning techniques to automate the segmentation of brain structures in MRI images. The proposed method leverages the power of convolutional neural networks (CNNs) to extract intricate patterns and features from complex neuro imaging data. The research involves the development and training of a deep learning model capable of accurately delineating key anatomical regions, such as the cortex, hippocampus, and ventricles. The model is trained on a large data set of annotated MRI scans, optimizing its performance through rigorous validation processes. The utilization of deep learning enables the algorithm to learn and generalize from diverse imaging data, improving its adaptability to variations in patient demographics and scanner characteristics. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a diverse set of MRI datasets, encompassing various neurological conditions. Quantitative metrics, including Dice coefficient and Hausdorff distance, are employed to assess the segmentation accuracy compared to ground truth annotations. Additionally, the clinical relevance of the automated segmentation is validated through collaboration with neurologists and radiologists. The results demonstrate that the deep learning- enabled segmentation method consistently outperforms traditional image processing techniques, providing more accurate and reliable segmentation results. The proposed approach not only streamlines the diagnostic process but also has the potential to uncover subtle abnormalities that may be overlooked by manual inspection. In conclusion, the integration of deep learning into the segmentation of brain MRI scans presents a promising avenue for enhancing neurological diagnosis. The automated and precise delineation of brain structures contributes to the efficiency and accuracy of diagnostic workflows, ultimately improving patient outcomes and facilitating timely interventions in the realm of neurological disorders.

Inversion of Boreal Forest Height Using the CRITIC Weighted Least Squares Three-Stage Temporal Decorrelation Iterative Algorithm

The inversion of forest height using the RVoG (Random Volume over Ground) model is susceptible to overestimation or underestimation due to three error sources, propagating inaccuracies to the estimated forest height. Furthermore, variations in the height and density of the scenario could impact how well the RVoG three-stage inversion performs. This work utilizes the L-band single-baseline full polarization interferometric dataset as its basis. It optimally applies the CRITIC (Criteria Importance Through Intercriteria Correlation) method to the first stage of a three-stage process. This approach aims to overcome the issues mentioned above and enhance the accuracy of forest parameter estimation. A CRITIC weighted least squares temporal decoherence iterative algorithm is also proposed for the characteristics of the spaceborne data, in combination with the temporal decoherence algorithm of previous research. The proposed approach is tested and applied to both simulated and actual data. The optimization approach is first assessed using four simulated datasets that simulate coniferous forests with different densities and heights. The preliminary findings suggest that optimizing the complex coherence fitting process through the weighted least squares method enhances the accuracy of ground phase estimation and, consequently, improves the accuracy of the three-stage approach for inverting forest height. The ground phase estimation results for low forest height consistently remained within 0.02 rad, with a root mean square error (RMSE) below 0.05 rad, and no saturation occurred with increasing forest density. The enhanced algorithm outperforms the traditional technique in terms of accuracy in ground phase estimation. Subsequently, the optimized approach is applied to ALOS-2 spaceborne data, proving more successful than the conventional algorithm in reducing the RMSE of forest height. The findings illustrate the method’s superior inversion performance, obtaining an accuracy exceeding 80% in both the test and validation sets. The validation set’s RMSE is approximately 2.5 m, and the mean absolute error (MAE) is within 2 m. Moreover, it is observed that to counteract the uncertainty in temporal decoherence induced by climate change, a larger temporal baseline necessitates a larger random motion compensation term and phase offset term.

Impact of Using Authentic Videos on Foreign Language Vocabulary Learning

This study investigated the impact of authentic video use on the vocabulary reservoir of English language learners and aimed to compare the effectiveness of this technique to traditional techniques. The study included 45 students in two language classes with B1 English proficiency levels. The language context took place in a preparatory school at a foundation university in Istanbul. Half of the 45 students were chosen as the control group, and the other half became the experimental group. In the control group, the students were taught target vocabulary merely by the definitions and translations provided by the teacher. The experimental group was taught vocabulary using short excerpts from authentic videos such as different series, movies, and songs. The study lasted seven weeks. A pretest and post-test were given to both participant groups to assess their vocabulary discernment. The results of the quantitative data demonstrated that the experimental group was much more successful in their vocabulary learning process than the control group as the test fort he vocabulary recognition indicated, and each student had significant improvement in their target vocabulary knowledge, proven by the number of correct answers marked in the vocabulary test. In addition to the quantitative data, interviews with 6 open-ended questions, provided a deeper understanding of students' views about authentic video excerpts. Interviews were conducted with 10 students from the experimental group. The results indicated that students had positive attitudes toward the use of authentic video excerpts in the language classroom and expressed support for being more effective in vocabulary building.

Enhanced complete ensemble EMD with superior noise handling capabilities: A robust signal decomposition method for power systems analysis

AbstractSignal decomposition is crucial in several domains, particularly in the dissection of complex signals present in electrical power systems. Understanding the oscillations and patterns within these signals can significantly influence energy resource management, grid stability, and efficient system operation. This paper presents an advanced enhanced decomposition method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to mitigate the inherent drawbacks of the conventional CEEMDAN and its improved version. Unlike CEEMDAN's generalized noise approach, the proposed method introduces adaptive noise, enhancing target signal noise handling by incorporating a tailored filtering and updating process after each iteration. This leads to more accurate signal decomposition compared to traditional methods. Comprehensive tests were conducted using artificially generated signals characterized by mode mixing, varying frequency oscillations, complex real‐world electrical demand signals, generator axis vibrations and partial discharge signals. The results demonstrate that the proposed method outperforms traditional techniques in two significant aspects. First, it provides superior spectral separation of the intrinsic modes (IMF) of the signal, thereby enhancing decomposition accuracy. Second, it significantly reduced the number of shifting iterations, thereby alleviating the computational load. These advancements have led to a more accurate and efficient framework that is essential for analyzing nonlinear and nonstationary signals.

Intelligent Resource Orchestration for 5G Edge Infrastructures

The adoption of edge infrastructure in 5G environments stands out as a transformative technology aimed at meeting the increasing demands of latency-sensitive and data-intensive applications. This research paper presents a comprehensive study on the intelligent orchestration of 5G edge computing infrastructures. The proposed Smart 5G Edge-Cloud Management Architecture, built upon an OpenNebula foundation, incorporates a ONEedge5G experimental component, which offers intelligent workload forecasting and infrastructure orchestration and automation capabilities, for optimal allocation of virtual resources across diverse edge locations. The research evaluated different forecasting models, based both on traditional statistical techniques and machine learning techniques, comparing their accuracy in CPU usage prediction for a dataset of virtual machines (VMs). Additionally, an integer linear programming formulation was proposed to solve the optimization problem of mapping VMs to physical servers in distributed edge infrastructure. Different optimization criteria such as minimizing server usage, load balancing, and reducing latency violations were considered, along with mapping constraints. Comprehensive tests and experiments were conducted to evaluate the efficacy of the proposed architecture.

LinChemIn: Route Arithmetic─Operations on Digital Synthetic Routes.

Computational tools are revolutionizing our understanding and prediction of chemical reactivity by combining traditional data analysis techniques with new predictive models. These tools extract additional value from the reaction data corpus, but to effectively convert this value into actionable knowledge, domain specialists need to interact easily with the computer-generated output. In this application note, we demonstrate the capabilities of the open-source Python toolkit LinChemIn, which simplifies the manipulation of reaction networks and provides advanced functionality for working with synthetic routes. LinChemIn ensures chemical consistency when merging, editing, mining, and analyzing reaction networks. Its flexible input interface can process routes from various sources, including predictive models and expert input. The toolkit also efficiently extracts individual routes from the combined synthetic tree, identifying alternative paths and reaction combinations. By reducing the operational barrier to accessing and analyzing synthetic routes from multiple sources, LinChemIn facilitates a constructive interplay between artificial intelligence and human expertise.

Unlocking the potential of oncology biomarkers: advancements in clinical theranostics.

Cancer biomarkers have revolutionized the field of oncology by providing valuable insights into tumor changes and aiding in screening, diagnosis, prognosis, treatment prediction, and risk assessment. The emergence of "omic" technologies has enabled biomarkers to become reliable and accurate predictors of outcomes during cancer treatment. In this review, we highlight the clinical utility of biomarkers in cancer identification and motivate researchers to establish a personalized/precision approach in oncology. By extending a multidisciplinary technology-based approach, biomarkers offer an alternative to traditional techniques, fulfilling the goal of cancer therapeutics to find a needle in a haystack. We target different forms of cancer to establish a dynamic role of biomarkers in understanding the spectrum of malignancies and their biochemical and molecular characterization, emphasizing their prospective contribution to cancer screening. Biomarkers offer a promising avenue for the early detection of human cancers and the exploration of novel technologies to predict disease severity, facilitating maximum survival and minimum mortality rates. This review provides a comprehensive overview of the potential of biomarkers in oncology and highlights their prospects in advancing cancer diagnosis and treatment.