Rock physics modeling of heterogeneous carbonatereservoirs： porosity estimation and hydrocarbon detection

In heterogeneous natural gas reservoirs, gas is generally present as small patchlike pockets embedded in the water-saturated host matrix. This type of heterogeneity, also called "patchy saturation", causes significant seismic velocity dispersion and attenuation. To establish the relation between seismic response and type of fluids, we designed a rock physics model for carbonates. First, we performed CT scanning and analysis of the fluid distribution in the partially saturated rocks. Then, we predicted the quantitative relation between the wave response at different frequency ranges and the basic lithological properties and pore fluids. A rock physics template was constructed based on thin section analysis of pore structures and seismic inversion. This approach was applied to the limestone gas reservoirs of the right bank block of the Amu Darya River. Based on poststack wave impedance and prestack elastic parameter inversions, the seismic data were used to estimate rock porosity and gas saturation. The model results were in good agreement with the production regime of the wells.

基于改进Detection Transformer的棉花幼苗与杂草检测模型研究

基于深度学习的目标检测技术在棉花幼苗与杂草检测领域已取得一定进展。论文提出了基于改进Detection Transformer的棉花幼苗与杂草检测模型,以提高杂草目标检测的准确率和效率。首先,引入了可变形注意力模块替代原始模型中的Transformer注意力模块,提高模型对特征图目标形变的处理能力。提出新的降噪训练机制,解决了二分图匹配不稳定问题。提出混合查询选择策略,提高解码器对目标类别和位置信息的利用效率。使用Swin Transformer作为网络主干,提高模型特征提取能力。通过对比原网络,论文提出的模型方法在训练过程中表现出更快的收敛速度,并且在准确率方面提高了6.7%。

A New Method for Hydrocarbon Detection Based on Multi-phase Theory

The hydrocarbon detection techniques used currently are generally based on the theory of single-phase medium, but hydrocarbon reservoir mostly is multi-phase medium, therefore, multisolutions and uncertainties are existed in the result of hydrocarbon detection. This paper presents a fast way to detect hydrocarbon in accordance with BOIT theory and laboratory data. The technique called DHAF technique has been applied to several survey area and obtained good result where the coincidence rate for hydrocarbon detection is higher than other similar techniques. The method shows a good prospect of the application in hydrocarbon detecting at exploration stage and in reservoir monitoring at production stage.

Hydrocarbon Detection Based on Phase Decomposition in Chaoshan Depression, Northern South China Sea

Located in the northern South China Sea,Chaoshan Depression is mainly a residual Mesozoic depression,with a construction of Meso-Cenozoic strata over 7000m thick and good hydrocarbon accumulation conditions.Amplitude attribute of-90°phase component derived by phase decomposition is employed to detect Hydrocarbon in the zone of interest(ZOI)in Chaoshan Depression.And it is found that there are evident amplitude anomalies occurring around ZOI.Phase decomposition is applied to forward modeling results of the ZOI,and high amplitudes occur on the-90°phase component more or less when ZOI is charged with hydrocarbon,which shows that the amplitude abnormality in ZOI is probably caused by oil and gas accumulation.

Optimization of Ultrasonic Extraction and Clean-up Protocol for the Determination of Polycyclic Aromatic Hydrocarbons in Marine Sediments by High-performance Liquid Chroma-tography Coupled with Fluorescence Detection

The procedures of ultrasonic extraction and clean-up were optimized for the determination of polycyclic aromatic hydrocarbons (PAHs) in marine sediments. Samples were ultrasonically extracted, and the extracts were purified with a miniaturized silica gel chromatographic column and analyzed with high performance liquid chromatography (HPLC) with a fluorescence detector. Ultrasonication with methanol-dichloromethane (2:1, v/v) mixture gave higher extraction efficiency than that with dichloromethane. Among the three elution solvents used in clean-up step, dichloromethane-hexane (2:3, v/v) mixture was the most satisfactory. Under the optimized conditions, the recoveries in the range of 54.82% to 94.70% with RSDs of 3.02% to 23.22% for a spiked blank, and in the range of 61.20% to 127.08% with RSDs of 7.61% to 26.93% for a spiked matrix, were obtained for the 15 PAHs studied, while the recoveries for a NIST standard reference SRM 1941b were in the range of 50.79% to 83.78% with RSDs of 5.24% to 21.38%. The detection limits were between 0.75 ng L-1 and 10.99 ng L-1for different PAHs. A sample from the Jiaozhou Bay area was examined to test the established methods.

Hydrocarbon accumulation and orderly distribution of whole petroleum system in marine carbonate rocks of Sichuan Basin,SW China

Based on the situation and progress of marine oil/gas exploration in the Sichuan Basin,SW China,the whole petroleum system is divided for marine carbonate rocks of the basin according to the combinations of hydrocarbon accumulation elements,especially the source rock.The hydrocarbon accumulation characteristics of each whole petroleum system are analyzed,the patterns of integrated conventional and unconventional hydrocarbon accumulation are summarized,and the favorable exploration targets are proposed.Under the control of multiple extensional-convergent tectonic cycles,the marine carbonate rocks of the Sichuan Basin contain three sets of regional source rocks and three sets of regional cap rocks,and can be divided into the Cambrian,Silurian and Permian whole petroleum systems.These whole petroleum systems present mainly independent hydrocarbon accumulation,containing natural gas of affinity individually.Locally,large fault zones run through multiple whole petroleum systems,forming a fault-controlled complex whole petroleum system.The hydrocarbon accumulation sequence of continental shelf facies shale gas accumulation,marginal platform facies-controlled gas reservoirs,and intra-platform fault-and facies-controlled gas reservoirs is common in the whole petroleum system,with a stereoscopic accumulation and orderly distribution pattern.High-quality source rock is fundamental to the formation of large gas fields,and natural gas in a whole petroleum system is generally enriched near and within the source rocks.The development and maintenance of large-scale reservoirs are essential for natural gas enrichment,multiple sources,oil and gas transformation,and dynamic adjustment are the characteristics of marine petroleum accumulation,and good preservation conditions are critical to natural gas accumulation.Large-scale marginal-platform reef-bank facies zones,deep shale gas,and large-scale lithological complexes related to source-connected faults are future marine hydrocarbon exploration targets in the Sichuan Basin.

Ultrasensitive Detection of Polycyclic Aromatic Hydrocarbons(PAHs) in Water Using Three-Dimensional SERS Substrate Based on Porous Material and pH 13 Gold Nanoparticles

Sensitivity is crucially important for surface-enhanced Raman spectroscopy(SERS)application to detect trace-level polycyclic aromatic hydrocarbons(PAHs)in the seawater.In this study,a high sensitivity three-dimensional(3-D)SERS substrate composed with syringe filter,glycidyl methacrylate-ethylene dimethacrylate(GMA-EDMA)porous material and optimal parameters(57 nm,pH 13)gold nanoparticles(Au NPs)was developed for the detection of PAHs in water.The enhancement effect and repeatability of this 3-D substrate were also explored.The Raman intensity of pyrene using 3-D SERS substrate is about 8 times higher than that of substrate only using p H 13 gold colloid solution and about 12 times higher than that of substrate using natural Au NPs and GMA-EDMA porous material,which means both the pH 13 AuN Ps and the GMA-EDMA porous material are important factors for the sensitivity of this 3-D SERS substrate.Good repeatability of this optimal 3-D substrate was obtained.The relative standard deviation(RSD)is less than 8.66% on the same substrate and less than 3.69% on other different substrates.Four kinds of PAHs,i.e.,phenanthrene,pyrene,benzo(a)pyrene,benzo(k)fluoranthene and their mixture,were detected at the different concentrations.Their limits of detection(LODs)are 8.3×10^-10(phenanthrene),2.1×10^-10(pyrene),3.8×10^-10(benzo(a)pyrene)and 1.7×10^-10 mol L^-1(benzo(k)fluoranthene),respectively.In addition,these four PAHs were also detected by fluorescence spectroscopy to evaluate the sensitivity of SERS technology using this optimal 3-D SERS substrate.The results showed that the sensitivity of SERS based on the 3-D SERS substrate even using the portable Raman system was closed to that of fluorescence spectroscopy.Therefore,the SERS technology using this optimal 3-D substrate is expected to be an in-situ method for the detection of environmental PAHs.

Detection of Interaction of Binding Affinity of Aromatic Hydrocarbon Receptor to the Specific DNA by Exonuclease Protection Mediated PCR Assay

A novel exonuclease protection mediated PCR assay (EPM-PCR) to detect the interaction of protein and DNA at a dioxin-responsive enhancer (DRE) upstream of the CYP1A1 gene in rat hepatic cytosol was established. A double-stranded DNA fragment containing two binding sites was designed and incubated with the aryl hydrocarbon receptor (AhR) transformed by 2,3,7,8-tetrachlorodibenzo-p dioxin (TCDD) to generate TCDD:AhR:DNA complex which could protect receptor-binding DNA against exonuclease Ⅲ (Exo Ⅲ) digestion. With ExoⅢ treatment, free DNAs were digested and receptor-bound DNAs remained that could be amplified by PCR. By agarose gel electrophoreses a clear band (285bp) was detected using TCDD-treated sample, while nothing with control samples. To detect transformed AhR-DRE complex, 2 fmol DNAs and 3 ug cytosol proteins were found to be sufficient in the experiment. Compared with gel retardation assay, this new method is more sensitive for monitoring the Ah receptor-enhancer interaction without radioactive pollution.

An innovative classification system for ranking the biological effects of marine aromatic hydrocarbons based on fish embryotoxicity

Petroleum hydrocarbon pollution is a global concern,particularly in coastal environments.Polycyclic aromatic hydrocarbons(PAHs) are regarded as the most toxic components of petroleum hydrocarbons.In this study,the biomonitoring and ranking effects of petroleum hydrocarbons and PAHs on the marine fish model Oryzias melastigma embryos were determined in the Jiulong River Estuary(JRE) and its adjacent waters in China.The results showed that the levels of petroleum hydrocarbons from almost all sites met the primary standard for marine seawater quality,and the concentrations of the 16 priority PAHs in the surface seawater were lower compared with those in other coastal areas worldwide.A new fish expert system based on the embryotoxicity of O.melastigma(OME-FES) was developed and applied in the field to evaluate the biological effects of petroleum hydrocarbons and PAHs.The selected physiological index and molecular indicators in OME-FES were appropriate biomarkers for indicating the harmful effects of petroleum hydrocarbons and PAHs.The outcome of OME-FES revealed that the biological effect levels of the sampling sites ranged from level Ⅰ(no stress) to level Ⅲ(medium stress),which is further corroborated by the findings of nested analysis of variance(ANOVA) models.Our results suggest that the OME-FES is an effective tool for evaluating and ranking the biological effects of marine petroleum hydrocarbons and PAHs.This method may also be applied to evaluate other marine pollutants based on its framework.

Improvement of High-Speed Detection Algorithm for Nonwoven Material Defects Based on Machine Vision

Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.

A Hybrid Intrusion Detection Method Based on Convolutional Neural Network and AdaBoost

To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection method.Hence,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and AdaBoost algorithm.This algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack data.The AdaBoost algorithm is used to classify network attack data that improved the detection effect of unbalanced data classification.We adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm environment.The results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.

Geochemical identification of a source rock affected by migrated hydrocarbons and its geological significance:Fengcheng Formation,southern Mahu Sag,Junggar Basin,NW China

The Fengcheng Formation is a crucial source rock and the primary reservoir for oil accumulation in the Mahu Sag.Crude oils are distributed throughout the Fengcheng Formation,ranging from the edge to the interior of the sag in the southern Mahu Sag.These crude oils originate from in-situ source rocks in shallowly buried areas and the inner deep sag.During migration,the crude oil from the inner deep sag affects the source rocks close to carrier beds,leading to changes in the organic geochemical characteristics of the source rocks.These changes might alter source rock evaluations and oil-source correlation.Based on data such as total organic carbon(TOC),Rock-Eval pyrolysis of source rocks,and gas chromatography-mass spectrometry(GC-MS)of the saturated fraction,and considering the geological characteristics of the study area,we define the identification characteristics of source rock affected by migrated hydrocarbons and establish the various patterns of influence that migrated hydrocarbons have on the source rock of the Fengcheng Formation in the southern Mahu Sag.The source rocks of the Fengcheng Formation are mostly fair to good,containing mainly Type II organic matter and being thermally mature enough to generate oil.Source rocks affected by migrated hydrocarbons exhibit relatively high hydrocarbon contents(S1/TOC>110 mg HC/g TOC,Extract/TOC>30%,HC:hydrocarbon),relatively low Rock-Eval Tmax values,and relatively high tricyclic terpane contents with a descending and mountain-shaped distribution.Furthermore,biomarker composition parameters indicate a higher thermal maturity than in-situ source rocks.Through a comparison of the extract biomarker fingerprints of adjacent reservoirs and mudstones in different boreholes,three types of influence patterns of migrated hydrocarbons are identified:the edge-influence of thin sandstone-thick mudstone,the mixed-influence of sandstone-mudstone interbedded,and the full-influence of thick sandstone-thin mudstone.This finding reminds us that the influence of migrated hydrocarbons must be considered when evaluating source rocks and conducting oil-source correlation.

Hydrocarbon detections using multi-attributes based quantum neural networks in a tight sandstone gas reservoir in the Sichuan Basin, China

A direct hydrocarbon detection is performed by using multi-attributes based quantum neural networks with gas fields.The proposed multi-attributes based quantum neural networks for hydrocarbon detection use data clustering and local wave decomposition based seismic attenuation characteristics,relative wave impedance features of prestack seismic data as the selected multiple attributes for one tight sandstone gas reservoir and further employ principal component analysis combined with quantum neural networks for giving the distinguishing results of the weak responses of the gas reservoir,which is hard to detect by using the conventional technologies.For the seismic data from a tight sandstone gas reservoir in the Sichuan basin,China,we found that multiattributes based quantum neural networks can effectively capture the weak seismic responses features associated with gas saturation in the gas reservoir.This study is hoped to be useful as an aid for hydrocarbon detections for the gas reservoir with the characteristics of the weak seismic responses by the complement of the multiattributes based quantum neural networks.

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

Automated Vulnerability Detection of Blockchain Smart Contacts Based on BERT Artificial Intelligent Model

The widespread adoption of blockchain technology has led to the exploration of its numerous applications in various fields.Cryptographic algorithms and smart contracts are critical components of blockchain security.Despite the benefits of virtual currency,vulnerabilities in smart contracts have resulted in substantial losses to users.While researchers have identified these vulnerabilities and developed tools for detecting them,the accuracy of these tools is still far from satisfactory,with high false positive and false negative rates.In this paper,we propose a new method for detecting vulnerabilities in smart contracts using the BERT pre-training model,which can quickly and effectively process and detect smart contracts.More specifically,we preprocess and make symbol substitution in the contract,which can make the pre-training model better obtain contract features.We evaluate our method on four datasets and compare its performance with other deep learning models and vulnerability detection tools,demonstrating its superior accuracy.

Quantitative effect of kerogen type on the hydrocarbon generation potential of Paleogene lacustrine source rocks,Liaohe Western Depression,China

Kerogen types exert a decisive effect on the onset and capacity of hydrocarbon generation of source rocks.Lacustrine source rocks in the Liaohe Western Depression are characterized by thick deposition,high total organic carbon(TOC)content,various kerogen types,and a wide range of thermal maturity.Consequently,their hydrocarbon generation potential and resource estimation can be misinterpreted.In this study,geochemical tests,numerical analysis,hydrocarbon generation kinetics,and basin modeling were integrated to investigate the differential effects of kerogen types on the hydrocarbon generation potential of lacustrine source rocks.Optimized hydrocarbon generation and expulsion(HGE)models of different kerogen types were established quantitatively upon abundant Rock-Eval/TOC/vitrinite reflectance(R_(o))datasets.Three sets of good-excellent source rocks deposited in the fourth(Es4),third(Es3),and first(Es1)members of Paleogene Shahejie Formation,are predominantly types I-II_(1),II_(1)-II_(2),and II-III,respectively.The activation energy of types I-II_(2)kerogen is concentrated(180-230 kcal/mol),whereas that of type III kerogen is widely distributed(150-280 kcal/mol).The original hydrocarbon generation potentials of types I,II_(1),II_(2),and III kerogens are 790,510,270,and 85 mg/g TOC,respectively.The Ro values of the hydrocarbon generation threshold for type I-III source rocks gradually increase from 0.42%to 0.74%,and Ro values of the hydrocarbon expulsion threshold increase from 0.49%to 0.87%.Types I and II_(1)source rocks are characterized by earlier hydrocarbon generation,more rapid hydrocarbon expulsion,and narrower hydrocarbon generation windows than types II_(2)and III source rocks.The kerogen types also affect the HGE history and resource potential.Three types(conventional,tight,and shale oil/gas)and three levels(realistic,expected,and prospective)of hydrocarbon resources of different members in the Liaohe Western Depression are evaluated.Findings suggest that the Es3 member has considerable conventional and unconventional hydrocarbon resources.This study can quantitatively characterize the hydrocarbon generation potential of source rocks with different kerogen types,and facilitate a quick and accurate assessment of hydrocarbon resources,providing strategies for future oil and gas exploration.

Development of Multiphoton Ionization Technique for Detection of Polycyclic Aromatic Hydrocarbon (PAH) in Solution

A simple low-cost system for detection of polycyclic aromatic hydrocarbon (PAH) in solution based on multiphoton ionization configuration is designed using a circulating ionization cell of 0.1 × 2 × 5 mm dimension with quartz optical window. Fourth harmonic emission of Nd:YAG laser (266 nm, 6 ns, 10 Hz, and 2 mJ) and second harmonic generation of distributed feedback dye laser (278 - 286 nm, 20 ps, 10 Hz, and 300 μJ) were used as the ionization source. A high voltage of 800 V was applied to separate the ions after ionization. The photocurrent includes a sharp peak and a broad tail indexed to electron and ion currents, respectively. The lowest concentration of anthraxcene (C14H10) in order of few nano-grams per milliliter was detected by this multiphoton ionization configuration.

Single-atom catalysts for the electrochemical reduction of carbon dioxide into hydrocarbons and oxygenates

The electrochemical reduction of carbon dioxide offers a sound and economically viable technology for the electrification and decarbonization of the chemical and fuel industries.In this technology,an electrocatalytic material and renewable energy-generated electricity drive the conversion of carbon dioxide into high-value chemicals and carbon-neutral fuels.Over the past few years,single-atom catalysts have been intensively studied as they could provide near-unity atom utilization and unique catalytic performance.Single-atom catalysts have become one of the state-of-the-art catalyst materials for the electrochemical reduction of carbon dioxide into carbon monoxide.However,it remains a challenge for single-atom catalysts to facilitate the efficient conversion of carbon dioxide into products beyond carbon monoxide.In this review,we summarize and present important findings and critical insights from studies on the electrochemical carbon dioxide reduction reaction into hydrocarbons and oxygenates using single-atom catalysts.It is hoped that this review gives a thorough recapitulation and analysis of the science behind the catalysis of carbon dioxide into more reduced products through singleatom catalysts so that it can be a guide for future research and development on catalysts with industry-ready performance for the electrochemical reduction of carbon dioxide into high-value chemicals and carbon-neutral fuels.

Esophageal cancer screening,early detection and treatment:Current insights and future directions

Esophageal cancer ranks among the most prevalent malignant tumors globally,primarily due to its highly aggressive nature and poor survival rates.According to the 2020 global cancer statistics,there were approximately 604000 new cases of esophageal cancer,resulting in 544000 deaths.The 5-year survival rate hovers around a mere 15%-25%.Notably,distinct variations exist in the risk factors associated with the two primary histological types,influencing their worldwide incidence and distribution.Squamous cell carcinoma displays a high incidence in specific regions,such as certain areas in China,where it meets the cost-effect-iveness criteria for widespread endoscopy-based early diagnosis within the local population.Conversely,adenocarcinoma(EAC)represents the most common histological subtype of esophageal cancer in Europe and the United States.The role of early diagnosis in cases of EAC originating from Barrett's esophagus(BE)remains a subject of controversy.The effectiveness of early detection for EAC,particularly those arising from BE,continues to be a debated topic.The variations in how early-stage esophageal carcinoma is treated in different regions are largely due to the differing rates of early-stage cancer diagnoses.In areas with higher incidences,such as China and Japan,early diagnosis is more common,which has led to the advancement of endoscopic methods as definitive treatments.These techniques have demonstrated remarkable efficacy with minimal complications while preserving esophageal functionality.Early screening,prompt diagnosis,and timely treatment are key strategies that can significantly lower both the occurrence and death rates associated with esophageal cancer.

Feature extraction for machine learning-based intrusion detection in IoT networks

A large number of network security breaches in IoT networks have demonstrated the unreliability of current Network Intrusion Detection Systems(NIDSs).Consequently,network interruptions and loss of sensitive data have occurred,which led to an active research area for improving NIDS technologies.In an analysis of related works,it was observed that most researchers aim to obtain better classification results by using a set of untried combinations of Feature Reduction(FR)and Machine Learning(ML)techniques on NIDS datasets.However,these datasets are different in feature sets,attack types,and network design.Therefore,this paper aims to discover whether these techniques can be generalised across various datasets.Six ML models are utilised:a Deep Feed Forward(DFF),Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),Decision Tree(DT),Logistic Regression(LR),and Naive Bayes(NB).The accuracy of three Feature Extraction(FE)algorithms is detected;Principal Component Analysis(PCA),Auto-encoder(AE),and Linear Discriminant Analysis(LDA),are evaluated using three benchmark datasets:UNSW-NB15,ToN-IoT and CSE-CIC-IDS2018.Although PCA and AE algorithms have been widely used,the determination of their optimal number of extracted dimensions has been overlooked.The results indicate that no clear FE method or ML model can achieve the best scores for all datasets.The optimal number of extracted dimensions has been identified for each dataset,and LDA degrades the performance of the ML models on two datasets.The variance is used to analyse the extracted dimensions of LDA and PCA.Finally,this paper concludes that the choice of datasets significantly alters the performance of the applied techniques.We believe that a universal(benchmark)feature set is needed to facilitate further advancement and progress of research in this field.