cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7989844
title: Data and program codes to reproduce the results of seismic tomography for the Augustine Volcano (Alaska)

description: This file contains the files to reproduce the results presented in the article: by Ivan Koulakov, Saleh Ismail Quasi, and Pavel Izbekov (2023). Structure of shallow magma sources beneath Augustine Volcano (Alaska) inferred from local earthquake tomography, <em>Journal of Geophysical Research, Solid Earth</em>. This file includes: 1. The full folder with the LOTOS code for the passive-source seismic tomography (Koulakov, 2009, BSSA). 2. Folder with the dataset including arrival times of the P and S waves from local seismicity in the area of the Augustine Volcano in Alaska. 3. README_AUGUSTINE.PDF file with the description of the workflow on how to reproduce the tomography models based on experimental and synthetic data presented in the article. Koulakov, I., 2009, LOTOS code for local earthquake tomographic inversion: Benchmarks for testing tomographic algorithms: Bulletin of the Seismological Society of America, v. 99, p. 194–214, https://doi.org/10.1785/0120080013.
DOI: 10.6084/m9.figshare.22770722
title: Bivalves present the largest and most diversified repertoire of Toll-like receptors in the animal kingdom, suggesting broad-spectrum pathogen recognition in marine waters

description: Additional data derived from "Bivalves present the largest and most diversified repertoire of Toll-like receptors in the animal kingdom, suggesting broad-spectrum pathogen recognition in marine waters". TLR sequences indetified in the annotation of 85 metazoan genomes TLR sequences <em>de novo</em> indentified in the genomes of three mussel species Snakemake pipeline and script to calculate saturation curves from the salmon output of the mapping of different mussel SRA transcriptomic samples to the mussel genome Pipeline used to calculate the modulated transcripts for different mussel transcriptomes
DOI: 10.6084/m9.figshare.22770722.v1
title: Bivalves present the largest and most diversified repertoire of Toll-like receptors in the animal kingdom, suggesting broad-spectrum pathogen recognition in marine waters

description: Additional data derived from "Bivalves present the largest and most diversified repertoire of Toll-like receptors in the animal kingdom, suggesting broad-spectrum pathogen recognition in marine waters". TLR sequences indetified in the annotation of 85 metazoan genomes TLR sequences <em>de novo</em> indentified in the genomes of three mussel species Snakemake pipeline and script to calculate saturation curves from the salmon output of the mapping of different mussel SRA transcriptomic samples to the mussel genome Pipeline used to calculate the modulated transcripts for different mussel transcriptomes
DOI: 10.5517/ccdc.csd.cc2bcjhr
title: CCDC 2156034: Experimental Crystal Structure Determination

description: Related Article: Muzzaffar Ahmad Bhat, Owais Ahmad Wani, Sajad Ahmad Bhat, Raymond John Butcher|2023|J.Mol.Struct.|1287|135710|doi:10.1016/j.molstruc.2023.135710
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.22949552
title: 论文样本数据集-《针对电力系统人工智能算法的数据投毒后门攻击方法与检测方案》

description: 样本描述.csv 文件中描述了 样本编号、负荷水平、故障线路、故障首末端位置、故障持续时间、稳定性、训练测试集、4.2.1投毒样本和4.2.3投毒样本 文件夹Samples中为原始数据集 文件夹Samples_Poisoning中为4.2.4小节中相应毒化样本数据 所有样本文件格式为Matlab生成的mat格式。 1）原始数据集中样本的设置描述 新英格兰39母线系统包括39条母线、19台负载、9台发电机和一个理想电压源。在每条母线上均部署相量测量单元(PMU)以监控动态响应过程。通过蒙特卡洛方法共生成2000组样本来模拟不同的运行模式和故障场景，设置负载变化范围为初始值的80%～120%，4%递增。任何线路首末端的20%～80%线路都可能发生三相短路故障，故障持续时间在0.1到0.35秒之间。每个样本的标签是系统是否稳定，通过仿真得到。在生成样本中随机选取1800组样本进行训练，200组样本进行测试。 2）4.2.1节中随机选取的10%的样本构成的投毒数据集样本序号列表 6, 8, 21, 23, 35, 42, 45, 63, 110, 111, 139, 148, 161, 163, 168, 193, 199, 212, 213, 227, 228, 231, 232, 237, 242, 247, 252, 262, 273, 290, 296, 299, 318, 326, 350, 354, 370, 377, 387, 412, 474, 479, 480, 501, 510, 516, 535, 538, 540, 593, 599, 602, 603, 607, 618, 624, 627, 634, 641, 642, 646, 647, 650, 659, 662, 701, 712, 716, 727, 735, 766, 772, 805, 809, 813, 814, 817, 823, 832, 841, 843, 846, 861, 870, 874, 898, 904, 906, 910, 938, 943, 951, 954, 955, 962, 973, 974, 979, 980, 1000, 1001, 1002, 1003, 1026, 1028, 1029, 1040, 1069, 1103, 1104, 1107, 1108, 1156, 1173, 1176, 1178, 1181, 1186, 1189, 1198, 1200, 1232, 1233, 1236, 1241, 1244, 1255, 1258, 1271, 1304, 1306, 1310, 1315, 1333, 1336, 1354, 1357, 1360, 1383, 1393, 1422, 1441, 1443, 1471, 1484, 1485, 1486, 1496, 1504, 1506, 1508, 1518, 1529, 1537, 1538, 1543, 1545, 1565, 1566, 1577, 1587, 1595, 1655, 1658, 1661, 1664, 1667, 1703, 1705, 1710, 1718, 1723, 1728, 1737, 1742, 1743, 1749, 1750, 1768, 1789 3）4.2.3节仿真中，随机选取10%样本的一半样本（修改G1、G2、G3）对应的样本序号列表，以及另一半样本（修改G4、G5、G9）对应的样本序号列表。 修改G1、G2、G3对应的样本序号列表： 6, 8, 21, 23, 35, 42, 45, 63, 110, 111, 139, 148, 161, 163, 168, 193, 199, 212, 213, 227, 228, 231, 232, 237, 242, 247, 252, 262, 273, 290, 296, 299, 318, 326, 350, 354, 370, 377, 387, 412, 474, 479, 480, 501, 510, 516, 535, 538, 540, 593, 599, 602, 603, 607, 618, 624, 627, 634, 641, 642, 646, 647, 650, 659, 662, 701, 712, 716, 727, 735, 766, 772, 805, 809, 813, 814, 817, 823, 832, 841, 843, 846, 861, 870, 874, 898, 904, 906, 910, 938 修改G4、G5、G9对应的样本序号列表： 943, 951, 954, 955, 962, 973, 974, 979, 980, 1000, 1001, 1002, 1003, 1026, 1028, 1029, 1040, 1069, 1103, 1104, 1107, 1108, 1156, 1173, 1176, 1178, 1181, 1186, 1189, 1198, 1200, 1232, 1233, 1236, 1241, 1244, 1255, 1258, 1271, 1304, 1306, 1310, 1315, 1333, 1336, 1354, 1357, 1360, 1383, 1393, 1422, 1441, 1443, 1471, 1484, 1485, 1486, 1496, 1504, 1506, 1508, 1518, 1529, 1537, 1538, 1543, 1545, 1565, 1566, 1577, 1587, 1595, 1655, 1658, 1661, 1664, 1667, 1703, 1705, 1710, 1718, 1723, 1728, 1737, 1742, 1743, 1749, 1750, 1768, 1789
DOI: 10.6084/m9.figshare.22949552.v2
title: 论文样本数据集-《针对电力系统人工智能算法的数据投毒后门攻击方法与检测方案》

description: 文件夹Samples中为原始数据集 文件夹Samples_Poisoning中为4.2.4小节中相应毒化样本数据 所有样本文件格式为Matlab生成的mat格式。 1）原始数据集中样本的设置描述 新英格兰39母线系统包括39条母线、19台负载、9台发电机和一个理想电压源。在每条母线上均部署相量测量单元(PMU)以监控动态响应过程。通过蒙特卡洛方法共生成2000组样本来模拟不同的运行模式和故障场景，设置负载变化范围为初始值的80%～120%。任何线路首末端的20%～80%线路都可能发生三相短路故障，故障持续时间在0.1到0.35秒之间。每个样本的标签是系统是否稳定，通过仿真得到。在生成样本中随机选取1800组样本进行训练，200组样本进行测试。 2）4.2.1节中随机选取的10%的样本构成的投毒数据集样本序号列表 6, 8, 21, 23, 35, 42, 45, 63, 110, 111, 139, 148, 161, 163, 168, 193, 199, 212, 213, 227, 228, 231, 232, 237, 242, 247, 252, 262, 273, 290, 296, 299, 318, 326, 350, 354, 370, 377, 387, 412, 474, 479, 480, 501, 510, 516, 535, 538, 540, 593, 599, 602, 603, 607, 618, 624, 627, 634, 641, 642, 646, 647, 650, 659, 662, 701, 712, 716, 727, 735, 766, 772, 805, 809, 813, 814, 817, 823, 832, 841, 843, 846, 861, 870, 874, 898, 904, 906, 910, 938, 943, 951, 954, 955, 962, 973, 974, 979, 980, 1000, 1001, 1002, 1003, 1026, 1028, 1029, 1040, 1069, 1103, 1104, 1107, 1108, 1156, 1173, 1176, 1178, 1181, 1186, 1189, 1198, 1200, 1232, 1233, 1236, 1241, 1244, 1255, 1258, 1271, 1304, 1306, 1310, 1315, 1333, 1336, 1354, 1357, 1360, 1383, 1393, 1422, 1441, 1443, 1471, 1484, 1485, 1486, 1496, 1504, 1506, 1508, 1518, 1529, 1537, 1538, 1543, 1545, 1565, 1566, 1577, 1587, 1595, 1655, 1658, 1661, 1664, 1667, 1703, 1705, 1710, 1718, 1723, 1728, 1737, 1742, 1743, 1749, 1750, 1768, 1789 3）4.2.3节仿真中，随机选取10%样本的一半样本（修改G1、G2、G3）对应的样本序号列表，以及另一半样本（修改G4、G5、G9）对应的样本序号列表。 修改G1、G2、G3对应的样本序号列表： 6, 8, 21, 23, 35, 42, 45, 63, 110, 111, 139, 148, 161, 163, 168, 193, 199, 212, 213, 227, 228, 231, 232, 237, 242, 247, 252, 262, 273, 290, 296, 299, 318, 326, 350, 354, 370, 377, 387, 412, 474, 479, 480, 501, 510, 516, 535, 538, 540, 593, 599, 602, 603, 607, 618, 624, 627, 634, 641, 642, 646, 647, 650, 659, 662, 701, 712, 716, 727, 735, 766, 772, 805, 809, 813, 814, 817, 823, 832, 841, 843, 846, 861, 870, 874, 898, 904, 906, 910, 938 修改G4、G5、G9对应的样本序号列表： 943, 951, 954, 955, 962, 973, 974, 979, 980, 1000, 1001, 1002, 1003, 1026, 1028, 1029, 1040, 1069, 1103, 1104, 1107, 1108, 1156, 1173, 1176, 1178, 1181, 1186, 1189, 1198, 1200, 1232, 1233, 1236, 1241, 1244, 1255, 1258, 1271, 1304, 1306, 1310, 1315, 1333, 1336, 1354, 1357, 1360, 1383, 1393, 1422, 1441, 1443, 1471, 1484, 1485, 1486, 1496, 1504, 1506, 1508, 1518, 1529, 1537, 1538, 1543, 1545, 1565, 1566, 1577, 1587, 1595, 1655, 1658, 1661, 1664, 1667, 1703, 1705, 1710, 1718, 1723, 1728, 1737, 1742, 1743, 1749, 1750, 1768, 1789
DOI: 10.48328/tudatalib-967
title: Water Distribution in a Socio-Technical System: Resilience Assessment for Critical Events Causing Demand Relocation

description: The data set contains raw simulation data as well as aggregated results for the study "Water Distribution in a Socio-Technical System: Resilience Assessment for Critical Events Causing Demand Relocation" in MDPI water 2021, 13(15) (DOI: https://doi.org/10.3390/w13152062). The data set comprises three hdf5 files with results from simulations for each of the three sheltering schemes as well as three further hdf5 files with results aggregated with regard to disruption origin. One further hdf5 file contains results from the centralied sheltering scheme aggregated with regard to sheltering node. Three further hdf5 file consist of results showing unserviced nodes in the water network. The aggregated results files are the basis for all results graphics in the publication. The plotting scripts can be found in the related software repository.
DOI: 10.5281/zenodo.7989896
title: BeMAGIC_Modelling of various magnetoelectric effects

description: BeMAGIC ITM (GA861145) Modelling of various magnetoelectric effects
DOI: 10.5281/zenodo.7989895
title: BeMAGIC_Modelling of various magnetoelectric effects

description: BeMAGIC ITM (GA861145) Modelling of various magnetoelectric effects
DOI: 10.17632/zc7cjr6532.2
title: Dataset for developing optimal headway-based bus dispatching strategy during epidemic outbreaks 

description: This article presents data from a research study titled "Optimal Headway-Based Bus Dispatching Strategy under the Influence of Epidemic Outbreaks" . The study focuses on the Xi'an Xiaozhai central business district (CBD) street network, which comprises 33 major signalized intersections and 112 bus stops associated with 12 bus routes. The dataset includes details of intersection and bus stop geospatial data, street segment and intersection design, intersection signal timing plans, bus route operational properties such as dispatching frequencies, fleet sizes, loading bay capacities, and bus-specific parameters. It also encompasses data on passenger boarding and alighting counts, as well as travelers’ origin and destination (O-D) locations, routes, and departure times during three time periods: 10:00-11:00 PM, 1:00-2:00 PM, and 7:00-8:00 PM on Monday, June 7, 2021. These times represent off-peak (10:00 PM–1:00 AM the next day), adjacent-to-peak (9:00–11:00 AM, 1:00–4:00 PM, and 8:00–10:00 PM), and peak (7:00–9:00 AM, 11:00 AM–1:00 PM, and 4:00–8:00 PM) periods, respectively. Data collection involves searching government and organizational records, utilizing Alibaba Cloud's Amap platform, conducting onsite measurements, and performing a field survey. The dataset is a valuable resource for studying the integrated operations of various urban mass transit services, including buses, bus rapid transit (BRT), and fixed guideway transit, under both normal and epidemic-affected travel conditions. Additionally, it can be used to investigate multimodal integrated urban passenger services offered by automobiles, transit, ridesharing, and active transportation modes.
DOI: 10.48420/23244854
title: Freeze-casting of Ti3C2 MXene suspension IV

description: Freeze-cast Ti3C2Tz aqueous nanoparticle suspensions to give well-aligned nanoparticles in a sample within the target dimensions and tolerances. <br> For full experimental details and for a description of the dataset, please see the uploaded .pdf.
DOI: 10.48420/23244854.v1
title: Freeze-casting of Ti3C2 MXene suspension IV

description: Freeze-cast Ti3C2Tz aqueous nanoparticle suspensions to give well-aligned nanoparticles in a sample within the target dimensions and tolerances. <br> For full experimental details and for a description of the dataset, please see the uploaded .pdf.
DOI: 10.5258/soton/d2644
title: Dataset supporting the paper "The sound radiation from a periodic array of railway sleepers using a wavenumber domain method".

description: This dataset supports the publication by H. Li, X. Xiao, D. Thompson, G. Squicciarini; "The sound radiation from a periodic array of railway sleepers using a wavenumber domain method", in the Journal of Sound and Vibration. https://doi.org/10.1016/j.jsv.2023.117818 This dataset contains data relating to figures that appear in the article.
DOI: 10.5281/zenodo.7989918
title: A year-round observation of δ13C of dicarboxylic acids and related compounds in fine aerosols: Implications from Central European background site

description: Data for paper of following abstract: Isotopic analysis of specific compounds in aerosols can be a useful tool when studying atmospheric processes. Here, we present the results of stable carbon isotope ratio (δ<sup>13</sup>C) measurements performed on a one-year set (n = 96, Sep. 2013 - Aug. 2014) of dicarboxylic acids and related compounds in PM<sub>1</sub> at a rural Central European background site, Košetice (Czech Republic). The most <sup>13</sup>C enriched acid was oxalic (C<sub>2</sub>, annual average = -16.6±5.0‰) followed by malonic (C<sub>3</sub>, avg. = -19.9±6.6‰) and succinic (C<sub>4</sub>, avg. = -21.3±4.6‰) acids. Thus, δ<sup>13</sup>C values decreased with an increase in carbon numbers. Azelaic acid (C<sub>9</sub>, avg. = -27.2±3.6‰) was found to be the least <sup>13</sup>C enriched. A comparison of δ<sup>13</sup>C of dicarboxylic acids from other background sites, especially in Asia, shows similar values to those from the European site. This comparison also showed that C<sub>2</sub> is more <sup>13</sup>C enriched at background sites than at urban ones. In general, we did not observe significant seasonal differences in δ<sup>13</sup>C values of dicarboxylic acids at the Central European station. We observed statistically significant differences (p value &lt; 0.05) between winter and summer δ<sup>13</sup>C values solely for C<sub>4</sub>, glyoxylic acid (ωC<sub>2</sub>), glutaric acid (C<sub>5</sub>) and suberic acid (C<sub>8</sub>). The only significant correlations between δ<sup>13</sup>C of C<sub>2</sub> and δ<sup>13</sup>C of C<sub>3</sub> were found in spring and summer, suggesting that the oxidation of C<sub>3</sub> to C<sub>2</sub> is significant in these months with a strong contribution from biogenic aerosols. The strongest season-independent annual correlation was observed in δ<sup>13</sup>C values between C<sub>2</sub> and C<sub>4</sub>, the two dominant dicarboxylic acids. Therefore, C<sub>4</sub> appears to be the main intermediate precursor of C<sub>2</sub> throughout the whole year.
DOI: 10.5281/zenodo.7989917
title: A year-round observation of δ13C of dicarboxylic acids and related compounds in fine aerosols: Implications from Central European background site

description: Data for paper of following abstract: Isotopic analysis of specific compounds in aerosols can be a useful tool when studying atmospheric processes. Here, we present the results of stable carbon isotope ratio (δ<sup>13</sup>C) measurements performed on a one-year set (n = 96, Sep. 2013 - Aug. 2014) of dicarboxylic acids and related compounds in PM<sub>1</sub> at a rural Central European background site, Košetice (Czech Republic). The most <sup>13</sup>C enriched acid was oxalic (C<sub>2</sub>, annual average = -16.6±5.0‰) followed by malonic (C<sub>3</sub>, avg. = -19.9±6.6‰) and succinic (C<sub>4</sub>, avg. = -21.3±4.6‰) acids. Thus, δ<sup>13</sup>C values decreased with an increase in carbon numbers. Azelaic acid (C<sub>9</sub>, avg. = -27.2±3.6‰) was found to be the least <sup>13</sup>C enriched. A comparison of δ<sup>13</sup>C of dicarboxylic acids from other background sites, especially in Asia, shows similar values to those from the European site. This comparison also showed that C<sub>2</sub> is more <sup>13</sup>C enriched at background sites than at urban ones. In general, we did not observe significant seasonal differences in δ<sup>13</sup>C values of dicarboxylic acids at the Central European station. We observed statistically significant differences (p value &lt; 0.05) between winter and summer δ<sup>13</sup>C values solely for C<sub>4</sub>, glyoxylic acid (ωC<sub>2</sub>), glutaric acid (C<sub>5</sub>) and suberic acid (C<sub>8</sub>). The only significant correlations between δ<sup>13</sup>C of C<sub>2</sub> and δ<sup>13</sup>C of C<sub>3</sub> were found in spring and summer, suggesting that the oxidation of C<sub>3</sub> to C<sub>2</sub> is significant in these months with a strong contribution from biogenic aerosols. The strongest season-independent annual correlation was observed in δ<sup>13</sup>C values between C<sub>2</sub> and C<sub>4</sub>, the two dominant dicarboxylic acids. Therefore, C<sub>4</sub> appears to be the main intermediate precursor of C<sub>2</sub> throughout the whole year.
DOI: 10.5281/zenodo.7907063
title: Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP

description: Retinoblastoma is a rare and aggressive form of childhood eye cancer that requires prompt diagnosis and treatment to prevent vision loss and even death. Deep learning models have shown promising results in detecting retinoblastoma from fundus images, but their decision-making process is often considered a "black box" that lacks transparency and interpretability. In this project, we explore the use of LIME and SHAP, two popular explainable AI techniques, to generate local and global explanations for a deep learning model based on InceptionV3 architecture trained on retinoblastoma and non-retinoblastoma fundus images. We collected and labeled a dataset of 400 retinoblastoma and 400 non-retinoblastoma images, split it into training, validation, and test sets, and trained the model using transfer learning from the pre-trained InceptionV3 model. We then applied LIME and SHAP to generate explanations for the model's predictions on the validation and test sets. Our results demonstrate that LIME and SHAP can effectively identify the regions and features in the input images that contribute the most to the model's predictions, providing valuable insights into the decision-making process of the deep learning model. In addition, the use of InceptionV3 architecture with spatial attention mechanism achieved high accuracy of 97\% on the test set, indicating the potential of combining deep learning and explainable AI for improving retinoblastoma diagnosis and treatment.
description: {"references": ["Humayun, M., Ashfaq, F., Jhanjhi, N. Z., & Alsadun, M. K. (2022). Traffic management: Multi-scale vehicle detection in varying weather conditions using yolov4 and spatial pyramid pooling network. Electronics, 11(17), 2748.", "Ashfaq, F., Ghoniem, R. M., Jhanjhi, N. Z., Khan, N. A., & Algarni, A. D. (2023). Using Dual Attention BiLSTM to Predict Vehicle Lane Changing Maneuvers on Highway Dataset. Systems, 11(4), 196.", "Aldughayfiq, B., Ashfaq, F., Jhanjhi, N. Z., & Humayun, M. (2023, April). YOLO-Based Deep Learning Model for Pressure Ulcer Detection and Classification. In Healthcare (Vol. 11, No. 9, p. 1222). MDPI."]}
DOI: 10.5281/zenodo.7907064
title: Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP

description: Retinoblastoma is a rare and aggressive form of childhood eye cancer that requires prompt diagnosis and treatment to prevent vision loss and even death. Deep learning models have shown promising results in detecting retinoblastoma from fundus images, but their decision-making process is often considered a "black box" that lacks transparency and interpretability. In this project, we explore the use of LIME and SHAP, two popular explainable AI techniques, to generate local and global explanations for a deep learning model based on InceptionV3 architecture trained on retinoblastoma and non-retinoblastoma fundus images. We collected and labeled a dataset of 400 retinoblastoma and 400 non-retinoblastoma images, split it into training, validation, and test sets, and trained the model using transfer learning from the pre-trained InceptionV3 model. We then applied LIME and SHAP to generate explanations for the model's predictions on the validation and test sets. Our results demonstrate that LIME and SHAP can effectively identify the regions and features in the input images that contribute the most to the model's predictions, providing valuable insights into the decision-making process of the deep learning model. In addition, the use of InceptionV3 architecture with spatial attention mechanism achieved high accuracy of 97\% on the test set, indicating the potential of combining deep learning and explainable AI for improving retinoblastoma diagnosis and treatment.
description: {"references": ["Humayun, M., Ashfaq, F., Jhanjhi, N. Z., & Alsadun, M. K. (2022). Traffic management: Multi-scale vehicle detection in varying weather conditions using yolov4 and spatial pyramid pooling network. Electronics, 11(17), 2748.", "Ashfaq, F., Ghoniem, R. M., Jhanjhi, N. Z., Khan, N. A., & Algarni, A. D. (2023). Using Dual Attention BiLSTM to Predict Vehicle Lane Changing Maneuvers on Highway Dataset. Systems, 11(4), 196.", "Aldughayfiq, B., Ashfaq, F., Jhanjhi, N. Z., & Humayun, M. (2023, April). YOLO-Based Deep Learning Model for Pressure Ulcer Detection and Classification. In Healthcare (Vol. 11, No. 9, p. 1222). MDPI."]}
DOI: 10.5281/zenodo.7968745
title: Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP

description: Retinoblastoma is a rare and aggressive form of childhood eye cancer that requires prompt diagnosis and treatment to prevent vision loss and even death. Deep learning models have shown promising results in detecting retinoblastoma from fundus images, but their decision-making process is often considered a "black box" that lacks transparency and interpretability. In this project, we explore the use of LIME and SHAP, two popular explainable AI techniques, to generate local and global explanations for a deep learning model based on InceptionV3 architecture trained on retinoblastoma and non-retinoblastoma fundus images. We collected and labeled a dataset of 400 retinoblastoma and 400 non-retinoblastoma images, split it into training, validation, and test sets, and trained the model using transfer learning from the pre-trained InceptionV3 model. We then applied LIME and SHAP to generate explanations for the model's predictions on the validation and test sets. Our results demonstrate that LIME and SHAP can effectively identify the regions and features in the input images that contribute the most to the model's predictions, providing valuable insights into the decision-making process of the deep learning model. In addition, the use of InceptionV3 architecture with spatial attention mechanism achieved high accuracy of 97\% on the test set, indicating the potential of combining deep learning and explainable AI for improving retinoblastoma diagnosis and treatment.
description: {"references": ["Humayun, M., Ashfaq, F., Jhanjhi, N. Z., & Alsadun, M. K. (2022). Traffic management: Multi-scale vehicle detection in varying weather conditions using yolov4 and spatial pyramid pooling network. Electronics, 11(17), 2748.", "Ashfaq, F., Ghoniem, R. M., Jhanjhi, N. Z., Khan, N. A., & Algarni, A. D. (2023). Using Dual Attention BiLSTM to Predict Vehicle Lane Changing Maneuvers on Highway Dataset. Systems, 11(4), 196.", "Aldughayfiq, B., Ashfaq, F., Jhanjhi, N. Z., & Humayun, M. (2023, April). YOLO-Based Deep Learning Model for Pressure Ulcer Detection and Classification. In Healthcare (Vol. 11, No. 9, p. 1222). MDPI."]}