Growth,yield,yield components,and grain qualities of groundnut(Arachis hypogaea L.)as affected by liming and phosphorus rates in southwest Ethiopia

Two field experiments were conducted in the main seasons of 2021/22 at the farmers'farm in two districts of southwest Ethiopia to investigate the effects of lime and phosphorus on groundnut yield and yield components,employing four levels of lime(0,2,4,and 6 t CaCO_(3)ha^(-1))and phosphorus(0,46,69,and 92 kg P_(2)O_(5)ha^(-1))arranged in factorial RCBD design with three replications.Data on the yield and its components were collected and subjected to an ANOVA using SAS software.The result demonstrated that plant height,number of branches,canopy spread,and shelling percentage were affected significantly by liming while effective nodule number,total peg,matured pod,and pod yield of groundnut significantly influenced by the main factors and their interactions.The combined application of 4 t lime and 46 kg P_(2)O_(5)ha^(-1)resulted in the highest number of effective nodules(147.23 plant^(-1)),total pod(72.6 plant^(-1)),mature pod(62.4 plant^(-1)),pod yield(4.49 t ha^(-1)),oil content(50.6%)and protein content(33.1%)whereas the lowest values of these parameters were seen in plots where neither lime nor phosphorus was applied.Therefore,it is advised that groundnut growers in the study areas,and similar agroecologies,apply the combination of 4 t lime and 46 kg P_(2)O_(5)ha^(-1).

Liming and Fertilizer Potentials of Some Underutilized Plant Materials in Southwestern Nigeria

Soil acidification and phosphorus deficiency are a major constraint to crop production in tropical soils. Use of conventional liming materials is associated with some limitations viz： inability to solely improve nitrogen and available phosphorus in soils, loss of soil organic carbon and soil aggregate stability. Liming and fertilizer potentials of leaves from three plant materials （Tithonia diversifolia （TL）, Imperata cylindrica （SG） and Gliricidia sepium （GL）） widely growing in Ogbomoso, southwest Nigeria, were tested under incubation condition. Each of the plant material was applied at.the rate of 10 t·hm-2 with and without 50% concentration of NPK 15 ： 15 ： 15-urea mix in 500 g acidic soiL Sole lime applied at 1 t·hm-2, sole NPK 15 ： 15 ： 15 applied at 60 kg·hm2 mixed with urea at 60 kg N· hm2 and an unamended soil were compared in completely randomized design in three replicates. The treated soils were incubated for 12 weeks. Thereafter, maize seeds were raised in each pot for a period of 3 weeks. Data coUected were subjected to analysis of variance. Regression analysis was used to predict contributions of increased soil pH in plant material treated soils to exchangeable A1, H, dry root weight of maize and available phosphorus. Results indicated that sole plant materials were the order SG〉TL〉GL significantly （P〈0.05） reduced exchangeable acidity compared to unamended and sole NPK. Sole NPK had the highest exchangeable acidity （4.7 cmol·kg-1） compared to unamended soil （3.3 cmol·kg-1） and sole lime （2.7 cmol·kg-1）. Application of sole Tithonia diversifolia increased available phosphorus by 214% and 97% compared to unamended and sole NPK treated soils respectively. Sole plant materials increased maize root weight by 33% compared to sole NPK. Increasing soil pH at harvesting in plant material treated soils significantly reduced exchangeable H and A1. Soil pH was responsible for up to 33% and 53% reductions in exchangeable Al and H, respectively. This culminated into up to 22% increases in dry root weight of maize seedling. Present results showed ability of the plant materials tested to ameliorate soil acidity and improved soil available phosphorus. The plant materials should be explored for using as green manure and composting feedstock. It will go a long way to reduce high dosage use of conventional liming and fertilizer materials on acidic nutrient degraded soils.

SOC Turnover and Lime-CO₂Evolution during Liming of an Acid Andisol and Ultisol

Agricultural liming contributes significantly to atmospheric CO2 emission from soils but data on magnitude of lime- contributed CO2 in a wide range of acid soils are still few. Data on lime-contributed CO2 and SOC turnover for global acid soils are needed to estimate the potential contribution of agricultural liming to atmospheric CO2. Using Ca13CO3 (13C 99%) as lime and tracer, here we separated lime-contributed and SOC-originated CO2 evolution in an acidic Kuroboku Andisol from Tanashi, Tokyo Prefecture (35°44′ N, 139°32′ E) and Kunigami Mahji Ultisol of Nakijin, Okinawa Prefecture, Japan (26°38′ N, 127°58′ E). On the average, lime-CO2 was 76.84% (Kuroboku Andisol) and 66.36% (Kunigami Mahji Ultisol) of overall CO2 emission after 36 days. There was increased SOC turnover in all limed soils, confirming priming effect (PE) of liming. The calculated PE of lime (Kuroboku Andisol, 51.97% - 114.95%;Kunigami Mahji Ultisol, 10.13% - 35.61%) was entirely 12C turnover of stable soil organic carbon (SOC) since SMBC, a labile SOC pool, was suppressed by liming in our experiment. Our results confirmed that mineralization of lime-carbonates is the major source of CO2 emission from acid soils during agricultural liming. Liming can influence the size of CO2 evolution from agricultural ecosystems considering global extent of acid soils and current volume of lime utilization. We propose the inclusion of liming in simulating carbon dynamics in agricultural ecosystems.

Shenyang Liming Garments Group

The Shenyang Liming Garments Group is a large consortium, specializing in garments and the development of wool-spinning maerials, and combining science and technology with industry, trade and real estate. It consists of more than 20 enterprises, with total assets of RMB 650 million, it has imported garment production equipment from Japan. the USA and Germany, and its automatic computer composition, plate making mid cutting systems have reached the world’s advanced level. The group produces, mainly the following: western-style clothes for men and women, overcoats, unlined and cotton-padded jackets, shirts, and a series of fine woolen products. Its Lifu Brand Westem-style clothes were designated One of the "

Effect of Liming on Cadmium Forms and Its Toxicity in Red Soils

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Liming Characteristics of a High-Calcium Dry Flue Gas Desulfurization By-Product and a Class-C Fly Ash

Due to coal’s availability and low cost, coal combustion continues to be the United States’ primary energy source. However, coal combustion produces large quantities of waste material. Some coal combustion by-products (CCBs) have chemical and physical characteristics that make them potentially useful as soil amendments. The objectives of this study were to characterize a relatively new, high-calcium dry flue gas desulfurization (DFGD) by-product and compare its agronomic liming potential to a Class-C fly ash (FA) and reagent-grade calcium carbonate (CaCO3). Calcium car-bonate equivalence (CCE), degree of fineness (DOF), and effective neutralizing value (ENV) for each CCB were determined using standard methods. The CCBs and CaCO3 were also incubated with an acidic (~4.5) clay sub-soil at application rates equiva-lent to 0, 0.5, 1, and 2 times the soil’s lime requirement and compared to an una-mended control. Soil pH was then measured periodically during a 40-day incubation. The ENV of 79.4% for the DFGD by-product and 57.3% for the FA were comparable to those of commercially available liming materials, but were significantly lower (P < 0.05) than that of reagent-grade CaCO3. After 40 days of incubation at the 0.5× ap-plication rate, both CCBs raised the pH of the clay soil to only 5.0, while the CaCO3 raised the pH to 6.5. After 40 days at the 1× rate, all three materials had raised the soil pH to between 6.5 and 7.0, although the FA increased the soil pH more slowly than did the other two materials. At the 2× rate, both CCBs increased the soil pH to between 7.5 and 8.0, while the CaCO3 increased the soil pH to only 7.0. Both CCBs appear to be useful as soil liming materials, although care should be taken to avoid over-application, as this may make the soil too alkaline for optimum plant growth.

基于前背景特性的逆光交通场景图像增强

由于逆光交通场景下采集到的图像存在前景亮度过低、背景亮度失真等特点,导致采集图像清晰度低、信息丢失严重和可识别性差,针对逆光图像前背景的不同特性,研究提出了一种分区域增强方法。对逆光图像使用最大类间方差法(OTSU法)分割图像的前景和背景;对逆光图像全局使用LIME方法,提升前景亮度的同时保持色彩的失真度;单独将背景部分RGB三个通道上的全局直方图均衡化结果一一映射到对应的限定区间内,提高了背景的对比度;使用Canny算子检测出前背景拼接处的黑边,根据黑边生成3个自适应的滤波模板对黑边进行分步均值滤波处理,消除了黑色边缘,提升了图像的视觉质量。在实验室自建的CHD_B数据集上,所提方法在4种常用客观评价指标上综合占优。实验结果表明,所提出的图像增强算法能有效地消除图像中的逆光现象。

Optimization of the main liming process for inulin crude extract from Jerusalem artichoke tubers

A three-stage homogenate extraction was used as a new method for inulin extraction from Jerusalem artichoke tubers. Compared with the results from conventional hot water extraction, the three-stage homogenate extraction gave higher yields and caused less degradation of the inulin. The inulin crude extract was then clarified by a carbonate-precipitation method, during which three variables -- the quicklime mass, the reaction temperature and the reaction time were optimized for the main liming process to give the best clarification effect. A Plackett- Burman design, the path of steepest ascent method, a Box- Behnken design and response surface methodology （RSM） were employed in the experimental design. The optimal conditions for the main liming process were determined to be 12.0g/L, 71.4℃ and 8min. The confirmatory tests proved that the best clarification efficiency （92.74%） was achieved at these conditions and this was approximately equal to the value predicted by the model.

Multi-scale analysis of carbon mineralization in lime-treated soils considering soil mineralogy

Mineral carbonation is emerging as a reliable CO_(2) capture technology that can mitigate climate change.In lime-treated clayey soils,mineral carbonation occurs through the carbonation of free lime and cementitious products derived from pozzolanic reactions.The kinetics of the reactions in lime-treated clayey soils are variable and depend primarily on soil mineralogy.The present study demonstrates the role of soil mineralogy in CO_(2) capture and the subsequent changes caused by carbon mineralization in terms of the unconfined compressive strength(UCS)of lime-treated soils during their service life.Three clayey soils(kaolin,bentonite,and silty clay)with different mineralogical characteristics were treated with 4%lime content,and the samples were cured in a controlled environment for 7 d,90 d,180 d,and 365 d.After the specified curing periods,the samples were exposed to CO_(2) in a carbonation cell for 7 d.The non-carbonated samples purged with N2 gas were used as a benchmark to compare the mechanical,chemical-mineralogical,and microstructure changes caused by carbonation reactions.Experimental investigations indicated that exposure to CO_(2) resulted in an average increase of 10%in the UCS of limetreated bentonite,whereas the strength of lime-treated kaolin and silty clay was reduced by an average of 35%.The chemical and microstructural analyses revealed that the precipitated carbonates effectively filled the macropores of the treated bentonite,compared to the inadequate cementation caused by pozzolanic reactions,resulting in strength enhancement.In contrast,strength loss in lime-treated kaolin and silty clay was attributed to the carbonation of cementitious phases and partly to the tensile stress induced by carbonate precipitation.In terms of carbon mineralization prospects,lime-treated kaolin exhibited maximum carbonation due to the higher availability of unreacted lime.The results suggest that,in addition to the increase in compressive strength,adequate calcium-bearing phases and macropores determine the efficiency of carbon mineralization in lime-treated clayey soils.

Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.

Cement and Lime Stabilization Effect on the Evolutivity of an Expansive Overconsolidated Clay

This paper presents and analyzes the results of a series of compaction,fragmentability and damage tests performed on an expansive overconsolidated clay treated with cement and lime.This clay was obtained from the urban site of Sidi-Hadjrès city(wilaya of M'sila,Algeria),where significant damages frequently appears in the road infrastructures,roadway systems and light structures.Tests results obtained show that the geotechnical parameters values deduced from these tests are concordant and confirm the evolutivity of this natural clay treated with composed Portland cement or extinct lime and compacted under optimum Proctor conditions.

Explainable Deep Fake Framework for Images Creation and Classification

Deep learning is a practical and efficient technique that has been used extensively in many domains. Using deep learning technology, deepfakes create fake images of a person that people cannot distinguish from the real one. Recently, many researchers have focused on understanding how deepkakes work and detecting using deep learning approaches. This paper introduces an explainable deepfake framework for images creation and classification. The framework consists of three main parts: the first approach is called Instant ID which is used to create deepfacke images from the original one;the second approach called Xception classifies the real and deepfake images;the third approach called Local Interpretable Model (LIME) provides a method for interpreting the predictions of any machine learning model in a local and interpretable manner. Our study proposes deepfake approach that achieves 100% precision and 100% accuracy for deepfake creation and classification. Furthermore, the results highlight the superior performance of the proposed model in deep fake creation and classification.

基于可解释关系模型的SCR入口气温预测

新能源在全社会发电中占比不断增高,为了能够辅助电网进行削峰填谷,火电机组需要增加在低负荷范围内的运行时间,这会对选择性催化还原(selective catalytic reduction,SCR)的脱硝效果产生负面影响。因此,对SCR入口烟气温度(SCR gas temperature,SCRIGT)进行准确预测非常重要。首先,采用极限梯度提升(extreme gradient boosting,XGBOOST)模型,以电厂的运行参数作为输入,功率与SCRIGT的比值作为输出进行预测,然后计算得到SCRIGT。结果显示,对于两个不同锅炉类型的火电机组,平均绝对百分比误差(mean absolute percentage error,MAPE)分别为3.07%和2.49%。其次,采用不可知模型的局部可解释(local interpretable model-agnostic explanations,LIME)算法分析XGBOOST模型的预测结果,显示功率和功率与SCRIGT的比值之间存在线性关系,且R2(R-squared)为0.994,基于此构建了一种可解释关系模型进行SCRIGT预测。最后,比较分析显示,对于试验中的两个机组,可解释关系模型预测结果的MAPE分别改进至0.68%和0.97%。

Effect of liming on sulfate transformation and sulfur gas emissions in degraded vegetable soil treated by reductive soil disinfestation

Reductive soil disinfestation（RSD）, namely amending organic materials and mulching or flooding to create strong reductive status, has been widely applied to improve degraded soils.However, there is little information available about sulfate（SO4^2-） transformation and sulfur（S）gas emissions during RSD treatment to degraded vegetable soils, in which S is generally accumulated. To investigate the effects of liming on SO4^2-transformation and S gas emissions,two SO4^2--accumulated vegetable soils（denoted as S1 and S2） were treated by RSD, and RSD plus lime, denoted as RSD0 and RSD1, respectively. The results showed that RSD0 treatment reduced soil SO4^2-by 51% and 61% in S1 and S2, respectively. The disappeared SO4^2-was mainly transformed into the undissolved form. During RSD treatment, hydrogen sulfide（H2S）,carbonyl sulfide（COS）, and dimethyl sulfide（DMS） were detected, but the total S gas emission accounted for 〈 0.006% of total S in both soils. Compared to RSD0, lime addition stimulated the conversion of SO42-into undissolved form, reduced soil SO4^2-by 81% in S1 and 84% in S2 and reduced total S gas emissions by 32% in S1 and 57% in S2, respectively. In addition to H2S, COS and DMS, the emissions of carbon disulfide, methyl mercaptan, and dimethyl disulfide were also detected in RSD1 treatment. The results indicated that RSD was an effective method to remove SO4^2-, liming stimulates the conversion of dissolved SO4^2-into undissolved form,probably due to the precipitation with calcium.

样本级实时空中格斗决策可解释模型研究

空中格斗决策作为空战领域重要组成部分,格斗决策的实时性、准确性直接关系到空战的成败,构建一个有效的、高速的、可解释的空中格斗模型显得十分重要.实际场景中,基于深度强化学习的空中格斗决策模型为了实现强大的学习能力,往往得到的模型参数过多,这导致了决策时间的延迟,不能较好的满足实时性强的空中格斗场景.同时深度强化学习模型的黑盒特性,使得模型在智能决策过程中的决策原因不可知,可是每一次空战决策过程中的不可解释性在空战场景中存在极大的未知风险.为了解决这些问题,本文提出一种基于高效的轻量级决策树的模型用于空战中战斗决策,该轻量级模型不仅满足可解释性同时满足空战场景中的高速决策.为了解决智能决策模型在每一次决策过程中的黑箱特性,本文采用一种局部代理技术完成空战格斗中每一次决策过程中的样本级解释.实践表明本文所构建的空中格斗模型具有较高的决策精度、速度与实战性能.

Quality Improvement of Recycled Concrete Aggregate by Accelerated Carbonation under Different Pressure

Due to the presence of old mortar (OM) and interfacial transition zone (ITZ),recycled concrete aggregate (RCA) is inferior to natural aggregate (NA).The purpose of this paper was to study the effect of accelerated carbonation on the macro-properties and micro-properties of RCA under different pressure(0.05,0.15,0.30 MPa).The macro-property tests included colour change,apparent density,water absorption,and crushing value of RCA.The micro-property tests included scanning electron microscopy (SEM),X-ray diffraction (XRD),thermogravimetry-differential scanning calorimetry (TG-DSC),and Vickers micro-hardness(VMH).The results showed that the change trends of apparent density,water absorption,and crushing value of RCA displayed exponential relationships as pressure increasing,with the optimum pressure of 0.30 MPa.SEM images indicated that the calcite caused by the hydration products in RCA and the Ca(OH)_(2) derived from saturated lime water improved the properties of RCA;as the apparent density increased,the water absorption and crushing value decreased.The results of XRD and TG-DSC indicated that,as the pressure increased,the masses of Ca(OH)_(2) in carbonated RCA gradually decreased,while those of CaCO_(3) gradually increased,which demonstrated that the carbonation degree gradually increased.Besides,ITZ-2 was the weakest phase in RCA,but its improvement degree of VMH by accelerated carbonation was higher than that of OM.However,RCA was not completely carbonated,but only carbonated in a certain depth after 24 h accelerated carbonation.

Sustainability of lime stabilized road subgrade in mountainous regions of Türkiye

In this paper the use of lime stabilized subgrade for low volume roads in two regions with high mountains and different frost penetration conditions in Türkiye was investigated in terms of design,performance,and cost.Pavements on unstabilized and stabilized subgrade were designed for two regions(Izmir and Van),covering all climate variations.The resilient modulus of the lime stabilized subgrade with different soil pulverization levels for non-freezing and freezing conditions were taken from a previous laboratory study.Frost effects were considered in pavement design using two different approaches,including limited subgrade frost penetration method and reduced subgrade strength method.Detailed application and evaluation were performed for all steps.Lime stabilized subgrades significantly reduced the thickness of base courses,and the benefit of lime stabilization was highly dependent on soil pulverization level.A detailed cost analysis on the unstabilized and stabilized cases found that the use of lime stabilization in the subgrade provided significant initial cost savings.Comparative analysis by using the AASHTO(1993)method and KENPAVE software,and quantity effect of soil pulverization level on the performance of low volume roads from a service life perspective,show that subgrade resilient modulus can be estimated.It is also possible to make correct performance estimation in the field.The results of the study show that lime stabilization is a good solution for low volume roads in the mountainous regions of Türkiye.

Implementation of Rapid Code Transformation Process Using Deep Learning Approaches

Our previous work has introduced the newly generated program using the code transformation model GPT-2,verifying the generated programming codes through simhash(SH)and longest common subsequence(LCS)algo-rithms.However,the entire code transformation process has encountered a time-consuming problem.Therefore,the objective of this study is to speed up the code transformation process signi􀀀cantly.This paper has proposed deep learning approaches for modifying SH using a variational simhash(VSH)algorithm and replacing LCS with a piecewise longest common subsequence(PLCS)algorithm to faster the veri􀀀cation process in the test phase.Besides the code transformation model GPT-2,this study has also introduced MicrosoMASS and Facebook BART for a comparative analysis of their performance.Meanwhile,the explainable AI technique using local interpretable model-agnostic explanations(LIME)can also interpret the decision-making ofAImodels.The experimental results show that VSH can reduce the number of quali􀀀ed programs by 22.11%,and PLCS can reduce the execution time of selected pocket programs by 32.39%.As a result,the proposed approaches can signi􀀀cantly speed up the entire code transformation process by 1.38 times on average compared with our previous work.

Preliminary Study on the Treatment Efficiency of Pasteurized Lime Thermal Alkaline Hydrolysis for Excess Activated Sludge and Reduction of Tetracycline Resistance Genes

Thermal alkaline hydrolysis is a common pretreatment method for the utilization of excess activated sludge(EAS).Owing to strict environment laws and need for better energy utilization,new methods were developed in this study to improve the efficiency of pretreatment method.Direct thermal hydrolysis(TH),pasteurized thermal hydrolysis(PTH),and alkaline pasteurized thermal hydrolysis(PTH+CaO and PTH+NaOH)methods were used to treat EAS.Each method was compared and analyzed in terms of dissolution in ammonium nitrogen(NH_(4)^(+)-N)and soluble COD(SCOD)in EAS.Furthermore,the removal of tetracycline resistance genes(TRGs)and class 1 transposon gene intI1 from EAS was investigated.The NH_(4)^(+)-N and SCOD concentrations in EAS treated by PTH were 1.24 and 2.58 times higher than those of TH.However,the removal efficiency of total TRGs and intI1 between the groups was comparable.The SCOD concentration of the PTH+NaOH group was 4.37 times higher than that of the PTH group,and the removal efficiency of total TRGs was increased by 9.52%compared with that by PTH.The NH_(4)^(+)-N and SCOD concentrations of the PTH+CaO group could reach 85.04%and 92.14%of the PTH+NaOH group,but the removal efficiency of total TRGs by PTH+CaO was 19.78%lower than that by PTH+NaOH.Thus,to reduce the financial cost in actual operation,lime(CaO)can be used instead of a strong alkali(NaOH),and pasteurized steam at 70℃ instead of conventional high-temperature heating to treat EAS.This study provides a reference for the development of alkaline hydrolysis under moderate temperatures along with the removal of TRGs in EAS.

Detecting Deepfake Images Using Deep Learning Techniques and Explainable AI Methods

Nowadays,deepfake is wreaking havoc on society.Deepfake content is created with the help of artificial intelligence and machine learning to replace one person’s likeness with another person in pictures or recorded videos.Although visual media manipulations are not new,the introduction of deepfakes has marked a breakthrough in creating fake media and information.These manipulated pic-tures and videos will undoubtedly have an enormous societal impact.Deepfake uses the latest technology like Artificial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL)to construct automated methods for creating fake content that is becoming increasingly difficult to detect with the human eye.Therefore,automated solutions employed by DL can be an efficient approach for detecting deepfake.Though the“black-box”nature of the DL system allows for robust predictions,they cannot be completely trustworthy.Explainability is thefirst step toward achieving transparency,but the existing incapacity of DL to explain its own decisions to human users limits the efficacy of these systems.Though Explainable Artificial Intelligence(XAI)can solve this problem by inter-preting the predictions of these systems.This work proposes to provide a compre-hensive study of deepfake detection using the DL method and analyze the result of the most effective algorithm with Local Interpretable Model-Agnostic Explana-tions(LIME)to assure its validity and reliability.This study identifies real and deepfake images using different Convolutional Neural Network(CNN)models to get the best accuracy.It also explains which part of the image caused the model to make a specific classification using the LIME algorithm.To apply the CNN model,the dataset is taken from Kaggle,which includes 70 k real images from the Flickr dataset collected by Nvidia and 70 k fake faces generated by StyleGAN of 256 px in size.For experimental results,Jupyter notebook,TensorFlow,Num-Py,and Pandas were used as software,InceptionResnetV2,DenseNet201,Incep-tionV3,and ResNet152V2 were used as CNN models.All these models’performances were good enough,such as InceptionV3 gained 99.68%accuracy,ResNet152V2 got an accuracy of 99.19%,and DenseNet201 performed with 99.81%accuracy.However,InceptionResNetV2 achieved the highest accuracy of 99.87%,which was verified later with the LIME algorithm for XAI,where the proposed method performed the best.The obtained results and dependability demonstrate its preference for detecting deepfake images effectively.