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Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model
Tomatoes are widely cultivated and consumed worldwide and are susceptible to various leaf diseases during their growth. Therefore, early detection and prediction of leaf diseases in tomato crops are crucial. Farmers can take proactive measures to prevent the spread and minimize the impact on crop yield and quality by identifying leaf diseases in their early stages. Several Machine Learning (ML) and Deep Learning (DL) frameworks have been developed recently to identify leaf diseases. This research presents an efficient deep-learning approach based on a hybrid classifier by optimizing the CNN and LSTM models, which helps to enhance classification accuracy. Initially, Median Filtering (MF) is used for leaf image pre-processing. Then, an improved watershed approach is used for segmenting the leaf images. Subsequently, enhanced Local Gabor Pattern (LGP) and statistical and color features are extracted. An optimized CNN and LSTM are used for classification, and the weights are tuned using the SISS-OB (Self Improved Shark Smell With Opposition Behavior) algorithm. Finally, we have analyzed the performance using various measures. Since we have done segmentation, feature extraction, and optimization improvisations, our proposed methodology results are higher than other available methods and existing works. The results obtained at Learning Percentage (LP) is 90% which is far superior to those obtained at other LPs. The FNR (False Negative Rate) is much lower (0.05) at the 90th LP. The proposed model achieved better classification performance in terms of Accuracy of 97.13%, Sensitivity of 95.09%, Specificity of 95.24%, Precision of 94.31%, F measure of 96.71% and MCC 87.34%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Potato Leaf Disease Identification using Hybrid Deep Learning Model
The potato is one of the most significant crops in the world. However, it is prone to several leaf diseases that can result in significant productivity losses, leading to economic challenges. Early and precise disease identification is essential for sensitive crops like potatoes. Deep learning approaches have demonstrated excellent potential in image-based disease classification tasks in recent years. This paper presents a hybrid strategy for classifying potato leaf image diseases by integrating Optimised Convolutional Neural Network (OCNN) and Long Short-Term Memory (LSTM) networks. The Adaptive Shark Smell Optimisation (ASSO) technique is used to optimize the weights of CNN models. The CNN component is initially used to extract pertinent characteristics from Potato leaves, capturing significant visual patterns related to various diseases. These extracted features are then fed into the LSTM model, which utilizes its sequential learning capability to model the temporal dependencies among the extracted features. The model performance is analyzed based on Accuracy, Precision, Recall, and F1-score criteria. Experimental results showed that the hybrid OCNN-LSTM model outperforms the individual CNN, LSTM, and MobileNet models. The proposed model results are compared with existing state-of-the-art work, and it was found that the OCNN-LSTM model performed better and received 99.02% accuracy. 2023 IEEE. -
A Comprehensive Review onCrop Disease Prediction Based onMachine Learning andDeep Learning Techniques
Leaf diseases cause direct crop losses in agriculture, and farmers cannot detect the disease early. If the diseases are not detected early and correctly, the farmer must undergo huge losses. It may lead to the wrong pesticide or over pesticide, directly affect crop productivity and economy, and indirectly affect human health. Sensitive crops have various leaf diseases, and early prediction of these diseases remains challenging. This paper reviews several machine learning (ML) and deep learning (DL) methods used for different crop disease segmentation and classification. In the last few years, computer vision and DL techniques have made tremendous progress in object detection and image classification. The study summaries the available research on different diseases on various crops based on machine learning (ML) and deep learning (DL) techniques. It also discusses the data sets used for research and the accuracy and performance of existing methods. It does mean that the methods and available data sets presented in this paper are not projected to replace published solutions for crop disease identification, perhaps to enhance them by finding the possible gaps. Seventy-five articles are analysed and reviewed to find essential issues that involve additional study for future research in this domain to promote continuous progress for data sets, methods, and techniques. It mainly focuses on image segmentation and classification techniques used to solve agricultural problems. Finally, this paper provides future research scope and challenges, limitations, and research gaps. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Hybrid Deep Learning-Based Potato andTomato Leaf Disease Classification
Predicting potato and tomato leaf disease is vital to global food security and economic stability. Potatoes and tomatoes are among the most important staple crops, providing essential nutrition to millions worldwide. However, many tomato and potato leaf diseases can seriously reduce food productivity and yields. We are proposing a hybrid deep learning model that combines optimized CNN (OCNN) and optimized LSTM (OLSTM). The weight values of LSTM and CNN models are optimized using the modified raindrop optimization (MRDO) algorithm and the modified shark smell optimization (MSSO) algorithm, respectively. The OCNN model is used to extract potato leaf image features and then fed into the OLSTM model, which handles data sequences and captures temporal dependencies from the extracted features. Precision, recall, F1-score, and accuracy metrics are used to analyze the output of the proposed OCNN-OLSTM model. The experimental performance is compared without optimizing the CNN-LSTM model, individual CNN and LSTM models, and existing MobileNet and ResNet50 models. The presented model results are compared with existing available work. We have received an accuracy of 99.25% potato and 99.31% for tomato. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Sensitive crop leaf disease prediction based on computer vision techniques with handcrafted features
Agricultural production is considered the primary source of the economy of many countries. Tomato and Potatoes are the most sensitive and consumable vegetables worldwide. However, during the growth of these crops, they suffer from many leaf diseases, which lead to loss of productivity and economy of the farmers. Many farmers detect and find plant diseases that are more time-consuming, expensive, and require expert decisions following the naked eye method. Therefore, early and accurate diagnosis of Tomato and Potato crops leaf diseases plays a vital role in sustainable agriculture. So, this research paper proposes an efficient leaf disease classification model based on computer vision techniques. The proposed Adaptive Deep Neural Network (ADNN) leaf disease classification method is a hybrid model which combines an optimized long short-term memory (OLSTM) and convolution neural network (CNN). The weight values supplied in the LSTM classifier are optimally selected using the Adaptive Raindrop Optimization algorithm. The handcrafted features are extracted from the segmented image and fused with the hybrid deep neural network to improve the classifier performance. The ADNN method consists of five steps: preprocessing, feature extraction, segmentation, handcrafted feature extraction, and classification. At first, the images are given to the preprocessing stage to remove the noise from leaf images. Then, the image-affected portion is segmented using an enhanced radial basis function neural network. After the segmentation process, the segmented image is given as an input to the adaptive deep neural network (ADNN) that classifies various types of diseases in the Potato and Tomato leaves. The efficiency of the ADNN model based on the OLSTM-CNN approach is determined concerning multiple metrics, namely Accuracy, Precision, Recall, F-measure, Specificity, and Sensitivity. The ADNN model achieved the best Accuracy of 98.02% for Tomatoes and 98% for Potatoes. The ADNN is compared with existing state-of-the-art CNN, LSTM, ResNet50, and MobileNet techniques. The performance analysis proved that the ADNN model improved efficiency in terms of all metrics and methods. 2023, The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden. -
Smart Attendance Management System using IoT
Taking student attendance is mandatory in an educational organization, and maintaining those attendance plays a vital role. The conventional way of taking student attendance in any institution is time-consuming and challenging, because in the conventional procedure taking attendance/Roll call is performed manually by calling student names as per their roll numbers and marking 'absent(A)' or 'present(P)' on the attendance/logbook accordingly in every class per day. To improve teaching efficiency/teaching time in classrooms by reducing the time required for Roll call's, we have proposed a biometric student attendance system based on IoT. The proposed system records students' attendance using the facial-based biometric system and stores the attendance details on the server through the internet. In this system, the Raspberry pi camera captures the student face images and compares them with the stored images in the database. If the captured image is comparable with the stored image, then the student's attendance is recorded on the remote server as a present(P) in class; otherwise, attendance is recorded as absent (A). The developed system has been tested for sample classes, and the results proved that the system is simple, cost-effective, and portable for managing students' attendance. 2022 IEEE. -
A colorimetric chemosensor for distinct color change with (E)-2-(1-(3-aminophenyl)ethylideneamino)benzenethiol to detect Cu2+ in real water samples
The study reports the synthesis of chemosensor (E)-2-(1-(3-aminophenyl)ethylideneamino)benzenethiol (C1), a highly sensitive, colorimetric metal probe that shows distinct selectivity for the detection of Cu2+ ion in various real water samples. Upon complexation with Cu2+ in CH3OH/H2O (60:40 v/v) (aqueous methanol), the C1 demonstrate significant enhancement in the absorption at 250nm and 300nm with a color change from light yellow to brown which was visualized using naked-eye. Therefore, these properties make C1 as an effective candidate for on-site Cu2+ ions detection. The emission spectrum of C1 illustrated TURN-ON recognition of Cu2+ with a limit of detection (LOD) of 46nM. Furthermore, Density Functional Theory (DFT) calculations were performed to better understand the interactions between C1 and Cu2+. The obtained results suggested that the electron clouds present around the NH2 innitrogen and sulfur in SH play a pivotal role in the formation of a stable complex. The computational results were in good agreement with the experimental UVvisible spectrometry results. Graphical abstract: [Figure not available: see fulltext.] 2023, The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry. -
Liberalisation and cashew industry: evidence from India (1965 to 2018)
We examine the impact of liberalisation on production, import, export and area under cultivation of cashew industry in India during 1965 to 2018 period using regression method. We divide data into two sub-periods. The liberalisation and pre-liberalisation period is from 1965 to 1991 and the post-liberalisation period covers the period from 1992 to 2018. We find that cashew production is not influenced post trade liberalisation. This study also finds trade liberalisation has a significant and positive impact on export. Further, we reveal an insignificant impact of liberalisation on import. This study show that the area under cultivation is not changed after the trade liberalisation. 2024 Inderscience Publishers. All rights reserved. -
Dandelion Algorithm for Optimal Location and Sizing of Battery Energy Storage Systemsin Electrical Distribution Networks
This paper describes a new way to improve the performance of an EDN by integrating distributed battery energy storage systems (BESs) in the best way possible. This method is based on the Dandelion Algorithm (DA). The search space for BES locations is first predetermined using loss sensitivity factors (LSFs), and then DA is used to determine the optimal locations and sizes. The reduction of real power distribution loss is regarded as the primary objective function, and the impact of BESs is extended to examine the network voltage profile, voltage stability, and GHG emissions. IEEE 33-busEDN is used to calculate the computational efficiency of LSF-DA. Results show that DA is more efficient than Archimedes optimization (AOA), future search algorithm(FSA), pathfinder algorithm(PFA), and butterfly optimization algorithm(BOA) algorithms. Furthermore, the results show that the proposed DA enhances all technological and environmental factors and RDN performance. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Advancements in Deep Learning Techniques for Potato Leaf Disease Identification Using SAM-CNNet Classification
Potato leaf diseases like Late Blight and Early Blight significantly challenge potato cultivation, impacting crop yield and quality worldwide. Potatoes are a staple for over a billion people and crucial for food security, especially in developing countries. The economic impact is substantial, with Late Blight alone causing annual damages over $6 billion globally. Effective detection and management are essential to mitigate these effects on agricultural productivity and economic stability. This paper presents a novel approach to potato leaf disease detection using advanced deep learning and optimization techniques. Key components include data normalization to eliminate noise, feature extraction using GoogLeNet, and hyperparameter tuning through the Elk Herd Optimizer (EHO). Additionally, a Spatial Attention Mechanism and Convolutional Neural Network (SAM-CNNet) are employed for robust classification. The method is validated using the Plant Village dataset, yielding an accuracy of 98.58%, with precision of 97.68%, recall of 98.42%, and F1-Score of 98.21%, demonstrating exceptional performance and reliability. This study highlights the proposed approach's efficacy in accurately identifying and classifying potato leaf diseases, offering a promising solution for precision agriculture and crop management. Copyright: 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license. -
Text Mining-A Comparative Review of Twitter Sentiments Analysis
Background: Text mining derives information and patterns from textual data. Online social media platforms, which have recently acquired great interest, generate vast text data about human behaviors based on their interactions. This data is generally ambiguous and unstructured. The data includes typing errors and errors in grammar that cause lexical, syntactic, and semantic uncertainties. This results in incorrect pattern detection and analysis. Researchers are employing various text mining techniques that can aid in Topic Modeling, the detection of Trending Topics, the identification of Hate Speeches, and the growth of communities in online social media net-works. Objective: This review paper compares the performance of ten machine learning classification techniques on a Twitter data set for analyzing users' sentiments on posts related to airline usage. Methods: Review and comparative analysis of Gaussian Naive Bayes, Random Forest, Multinomial Naive Bayes, Multinomial Naive Bayes with Bagging, Adaptive Boosting (AdaBoost), Optimized AdaBoost, Support Vector Machine (SVM), Optimized SVM, Logistic Regression, and Long-Short Term Memory (LSTM) for sentiment analysis. Results: The results of the experimental study showed that the Optimized SVM performed better than the other classifiers, with a training accuracy of 99.73% and testing accuracy of 89.74% compared to other models. Conclusion: Optimized SVM uses the RBF kernel function and nonlinear hyperplanes to split the dataset into classes, correctly classifying the dataset into distinct polarity. This, together with Feature Engineering utilizing Forward Trigrams and Weighted TF-IDF, has improved Optimized SVM classifier performance regarding train and test accuracy. Therefore, the train and test accuracy of Optimized SVM are 99.73% and 89.74% respectively. When compared to Random Forest, a mar-ginal of 0.09% and 1.73% performance enhancement is observed in terms of train and test accuracy and 1.29% (train accuracy) and 3.63% (test accuracy) of improved performance when compared with LSTM. Likewise, Optimized SVM, gave more than 10% of enhanced performance in terms of train accuracy when compared with Gaussian Nae Bayes, Multinomial Nae Bayes, Multinomial Nae Bayes with Bagging, Logistic Regression and a similar enhancement is observed with Ada-Boost and Optimized AdaBoost which are ensemble models during the experimental process. Optimized SVM also has outperformed all the classification models in terms of AUC-ROC train and test scores.. 2024 Bentham Science Publishers. -
Lattice thermal conduction in suspended molybdenum disulfide monolayers with defects
In this study, we investigated the effect of lattice defects comprising vacancies and boundaries on the lattice thermal conductivity (LTC), ? p , of suspended molybdenum disulfide monolayers (MLs) over a wide temperature range (1 < T < 500 K). Using the phonon Boltzmann formalism, the acoustic phonons were considered to be scattered by the sample and grain boundaries, isotopic impurities, vacancies, and other phonons via Umklapp and normal (N-) processes. ? p was evaluated using a modified Callaway model by considering the in-plane longitudinal acoustic and transverse acoustic phonons, and out-of-plane flexural acoustic phonon modes. We demonstrated the need to include the often neglected non-resistive N-processes when evaluating the LTC. Numerical calculations of the temperature dependence of the LTC for crystalline and polycrystalline MoS 2 MLs showed the dominance of sample-dependent scattering mechanisms at low temperatures (T < 100 K) and of phonon-phonon scattering at higher temperatures, where the N-processes played an important role. The effects of vacancies and boundaries were to alter the behavior and suppress the magnitude of the LTC. The suppression due to vacancies was greater in crystalline MLs with specular surfaces and in polycrystalline MLs with larger grain sizes. The calculations compared well with recent thermal conductivity data obtained for polycrystalline samples. The need for further investigations is suggested. 2018 Elsevier Ltd -
Effect of vacancies on thermopower of molybdenum disulfide monolayers
A detailed theoretical investigation of the effect of scattering of electrons and phonons by lattice vacancies in molybdenum disulfide (MoS2) monolayers (MLs) on diffusion, S d, and phonon-drag, S g, components of thermoelectric power (TEP), S, is presented over a wide-temperature range (1 < T < 300 K) using the Boltzmann transport formalism. The diffusion component is assumed to be influenced, not only by vacancies via short-range and Coulomb disorder scattering, but also by charged impurities (CIs) and acoustic and optical phonons. In the case of S g, the phonons are considered to be scattered, besides the vacancies, by sample boundaries, substitutional isotopic impurities, as well as other phonons via both N- and U-processes. Numerical calculations of S d and S g, as functions of temperature and vacancy defect density are presented for MoS2 MLs with n s = 1017 m-2 supported on SiO2/Si substrates. The role of carrier scatterings by mono-sulfur and mono-molybdenum vacancies in influencing the overall electron and phonon relaxation rates and in determining S d and S g are investigated. The behavior of S d and S g is found to be noticeably influenced by vacancy scattering. The influence on S d is seen to be more for mono-sulfur vacancies for densities lesser than 1%. The influence, is to enhance S d slightly for MLs with realizable CI concentrations. On the other hand, S g is found to depend sensitively on the vacancy disorder for T < 50 K; a S-vacancy density of 0.1% is found to suppress the characteristic peak of S g by almost 60%. The extent of reduction in the characteristic peak of S g, observable in low temperature measurements of S, can provide information about defect density. The calculations demonstrate that defect engineering of MoS2 ML systems can be used to tune their thermoelectric performance. A need for detailed experimental studies is suggested. 2018 IOP Publishing Ltd. -
Role of charged impurities in thermoelectric transport in molybdenum disulfide monolayers
A theoretical study of the electronic properties, namely, electrical conductivity (EC), electronic thermal conductivity (ETC) and thermoelectric power (TEP) in 2D MoS2 monolayers (MLs), over a wide range of temperatures (10 < T < 300 K), is presented employing Boltzmann transport formalism. Considering the electrons to be scattered by screened charged impurities and the acoustic, optical and remote phonons, the transport equation is solved using Ritz iterative method. Numerical calculations of EC, ETC and TEP presented for supported and free-standing MLs with high electron concentrations, as a function of temperature, bring out the relative importance of the various scattering mechanisms operative. The role of CIs, with regard to both concentration and separation from the substrate-ML interface, in determining the properties of supported MLs is demonstrated for the first time. Validity of Wiedemann-Franz law and Mott formula are examined for supported and free standing MLs. Calculations are in consonance with recent experimental data on mobility and TEP of exfoliated SiO2-supported MoS2 ML samples. In the case of TEP it is found that though the diffusion contribution is dominant the inclusion of the drag component, incorporating contributions from all relevant phonon scattering mechanisms, is needed to obtain good agreement with the data. 2017 IOP Publishing Ltd. -
Effect of phonon-substrate scattering on lattice thermal conductivity of monolayer MoS2
The effect of phonon-substrate scattering on lattice thermal conductivity (LTC) of supported MoS2 MLs is investigated over a wide temperature range (1 -
Parkinson's Disease Progression Prediction Using Longitudinal Imaging Data and Grey Wolf Optimizer-Based Feature Selection
This work uses longitudinal imaging data and a feature selection method based on the Grey Wolf Optimizer (GWO) to create a novel method for forecasting the course of Parkinson's disease.Magnetic resonance imaging (MRI) and positron emission tomography (PET) longitudinal imaging data offer important insights into the structural and functional changes in the brain over time. However, because of its great dimensionality, analysing this complicated data might be difficult. We suggest using the GWO-based feature selection method to identify the most informative imaging features related to illness development in order to solve this problem.The Grey Wolf Optimizer is an algorithm that draws inspiration from nature and imitates the way that grey wolves hunt. By effectively locating an ideal subset of features that maximise classification or regression performance, it has demonstrated promising results in feature selection challenges. GWO will be used in our investigation to choose the most pertinent imaging features from the longitudinal data, lowering dimensionality and improving the model's ability to predict outcomes.Using machine learning strategies, we will build a predictive model that includes the chosen features and longitudinal imaging data. We hope to equip clinicians with a tool to forecast the course of each patient's Parkinson's disease by utilising this model. By assisting in early diagnosis, treatment planning, and disease progression monitoring, this predictive skill can ultimately improve the overall management of Parkinson's disease and the quality of life for those who are affected. Our method has great promise for expanding the fields of neurodegenerative disease prediction and personalised therapy because it integrates longitudinal imaging data and the Grey Wolf Optimizer-based feature selection method in a novel way. 2024, Ismail Saritas. All rights reserved. -
An improved AI-driven Data Analytics model for Modern Healthcare Environment
AI-driven statistics analytics is a swiftly advancing and impactful era that is transforming the face of healthcare. By leveraging the energy of AI computing and gadget studying, healthcare organizations can speedy gain insights from their huge datasets, offering a greater comprehensive and personalized approach to hospital therapy and populace health management. This paper explores the advantages of AI-driven statistics analytics in healthcare settings, masking key benefits along with progressed analysis and treatment, better-affected person effects, and financial savings. Moreover, this paper addresses the main challenges associated with AI-pushed analytics and offers potential solutions to enhance accuracy and relevance. In the long run, statistics analytics powered by way of AI gives powerful opportunities to improve healthcare outcomes, and its use is expected to expand within the coming years. 2024 IEEE. -
A Novel Approach for Sensitive Crop Disease Prediction Based on Computer Vision Techniques
Agriculture is a vital sector that plays an essential role in ensuring global food security, supporting economic development, and promoting environmental sustainability. Sustainable agriculture is an essential approach that aims to address the diffculties posed by conventional farming practices and ensure the long-term viability of our food production systems. Worldwide, crop leaf diseases seriously threaten food security and agricultural production. Early and accurate detection of crop leaf diseases is essential for effective crop productivity management and food prevention. Computer vision approaches offer promising solutions for automating the identifcation and prediction of crop leaf diseases. Analyzing digital images of plant leaves enables the identifcation of disease characteristics, such as discoloration, lesions, and patterns, which are often imperceptible to the naked eye. Machine Learning (ML) algorithms, such as Convolutional Neural Networks (CNN), have been widely employed in this domain to learn from large datasets of annotated images and accurately classify leaf diseases. The process of crop leaf disease classifcation using computer vision involves several stages. Initially, highresolution images of plant leaves are acquired using cameras or mobile devices. Preprocessing techniques, including image enhancement and noise reduction, are applied to improve image quality. Subsequently, feature extraction approaches extract pertinent data from the images, including texture, shape, and color. Deep Learning (DL) models are then trained and fne-tuned using these extracted features. newlineAlthough computer vision techniques have shown effective results in the classifcation of plant diseases, however, several challenges remain. Tomatoes and Potatoes newlineare widely cultivated and consumed vegetables worldwide and are a primary economic newlinesource for many countries. These sensitive plants are prone to various diseases during newlinegrowth, leading to signifcant losses in productivity and fnancial impact on farmers. -
Customer Segmentation and Future Purchase Prediction using RFM measures
Winning in the E-Commerce business race at a competitive age like this requires proper usage of Customer data. Using that database and grouping it in similar segments in terms of spending expenditure, observation time, sex, and location so that every customer falls in a segment of characteristics. This mechanism is called Customer Segmentation. In the modern era of highly compatible technological advancements, Machine Learning Algorithms are being vastly used to bring solutions to these difficult yet essential services. In the field of research methods like simple clustering based on purchase behaviour, buyer targeting or automated customer promotion mechanism by dividing into two major categories, have been worked on. However, ensemble algorithms have come handy where different clustering algorithms are combined to deliver best segmentation. Lately combination techniques like clustering and classification mechanism have also delivered good results where, not only segmentation is done but also classification of existing and new customers are possible into the clusters. Depending on that an effective customer relationship management can really benefit the company to a huge extent. Unlike other studies where clustering was performed directly on RFM table, a different approach was taken in this study where, one dimensional clustering was done individually on Recency, Frequency, Monetary columns, then an overall score was calculated and customers were classified into three segments. However, for a new customer depending on his purchase behaviour he/she also can be classified into any of the categories. 2022 IEEE. -
PA1 cells containing a truncated DNA polymerase ? protein are more sensitive to gamma radiation
Purpose: DNA polymerase ? (Pol?) acts in the base excision repair (BER) pathway. Mutations in DNA polymerase ? (Pol?) are associated with different cancers. A variant of Pol? with a 97 amino acid de-letion (Pol??), in heterozygous conditions with wild-type Pol?, was identified in sporadic ovarian tumor samples. This study aims to evaluate the gamma radiation sensitivity of Pol?? for possible target therapy in ovarian cancer treatment. Materials and Methods: Pol?? cDNA was cloned in a GFP vector and transfected in PA1 cells. Stable cells (PA1Pol??) were treated with60Co sourced gamma-ray (015 Gy) to investigate their radiation sensitivity. The affinity of Pol?? with DNA evaluated by DNA protein in silico docking experiments. Results: The result showed a statistically significant (p < 0.05) higher sensitivity towards radiation at different doses (015 Gy) and time-point (4872 hours) for PA1Pol?? cells in comparison with nor-mal PA1 cells. Ten Gy of gamma radiation was found to be the optimal dose. Significantly more PA-1Pol?? cells were killed at this dose than PA1 cells after 48 hours of treatment via an apoptotic pathway. The in silico docking experiments revealed that Pol?? has more substantial binding potential towards the dsDNA than wild-type Pol?, suggesting a possible failure of BER pathway that results in cell death. Conclusion: Our study showed that the PA1Pol?? cells were more susceptible than PA1 cells to gamma radiation. In the future, the potentiality of ionizing radiation to treat this type of cancer will be checked in animal models. 2022 The Korean Society for Radiation Oncology.