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{"title":"Hierarchical representation learning for next basket recommendation","authors":"Wenhua Zeng ,&nbsp;Junjie Liu ,&nbsp;Bo Zhang","doi":"10.1016/j.array.2024.100354","DOIUrl":"https://doi.org/10.1016/j.array.2024.100354","url":null,"abstract":"<div><p>The task of next basket recommendation is pivotal for recommender systems. It involves predicting user actions, such as the next product purchase or movie selection, by exploring sequential purchase behavior and integrating users’ general preferences. These elements may converge and influence users’ subsequent choices. The challenge intensifies with the presence of varied user purchase sequences in the training set, as indiscriminate incorporation of these sequences can introduce superfluous noise. In response to these challenges, we propose an innovative approach: the Selective Hierarchical Representation Model (SHRM). This model effectively integrates transactional data and user profiles to discern both sequential purchase transactions and general user preferences. The SHRM’s adaptability, particularly in employing nonlinear aggregation operations on user representations, enables it to model complex interactions among various influencing factors. Notably, the SHRM employs a Recurrent Neural Network (RNN) to capture extended dependencies in recent purchasing activities. Moreover, it incorporates an innovative sequence similarity task, grounded in a k-plet sampling strategy. This strategy clusters similar sequences, significantly mitigating the learning process’s noise impact. Through empirical validation on three diverse real-world datasets, we demonstrate that our model consistently surpasses leading benchmarks across various evaluation metrics, establishing a new standard in next-basket recommendation.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005624000201/pdfft?md5=78ee4b9a97b496d96fbd334c5bf79bfb&pid=1-s2.0-S2590005624000201-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141485906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive survey on image steganalysis using deep learning","authors":"Ntivuguruzwa Jean De La Croix ,&nbsp;Tohari Ahmad ,&nbsp;Fengling Han","doi":"10.1016/j.array.2024.100353","DOIUrl":"https://doi.org/10.1016/j.array.2024.100353","url":null,"abstract":"<div><p>Steganalysis, a field devoted to detecting concealed information in various forms of digital media, including text, images, audio, and video files, has evolved significantly over time. This evolution aims to improve the accuracy of revealing potential hidden data. Traditional machine learning approaches, such as support vector machines (SVM) and ensemble classifiers (ECs), were previously employed in steganalysis. However, they demonstrated ineffective against contemporary and prevalent steganographic methods. The field of steganalysis has experienced noteworthy advancements by transitioning from traditional machine learning methods to deep learning techniques, resulting in superior outcomes. More specifically, deep learning-based steganalysis approaches exhibit rapid detection of steganographic payloads and demonstrate remarkable accuracy and efficiency across a spectrum of modern steganographic algorithms. This paper is dedicated to investigating recent developments in deep learning-based steganalysis schemes, exploring their evolution, and conducting a thorough analysis of the techniques incorporated in these schemes. Furthermore, the research aims to delve into the current trends in steganalysis, explicitly focusing on digital image steganography. By examining the latest techniques and methodologies, this work contributes to an enhanced understanding of the evolving landscape of steganalysis, shedding light on the advancements achieved through deep learning-based approaches.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005624000195/pdfft?md5=3dd7fe4cac4a2f244f4a326af65ea83d&pid=1-s2.0-S2590005624000195-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141292436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ANALYZE-AD: A comparative analysis of novel AI approaches for early Alzheimer’s detection","authors":"Mritunjoy Chakraborty,&nbsp;Nishat Naoal,&nbsp;Sifat Momen,&nbsp;Nabeel Mohammed","doi":"10.1016/j.array.2024.100352","DOIUrl":"10.1016/j.array.2024.100352","url":null,"abstract":"<div><p>Alzheimer’s disease, characterized by progressive and irreversible deterioration of cognitive functions, represents a significant health concern, particularly among older adults, as it stands as the foremost cause of dementia. Despite its debilitating nature, early detection of Alzheimer’s disease holds considerable advantages for affected individuals. This study investigates machine-learning methodologies for the early diagnosis of Alzheimer’s disease, utilizing datasets sourced from OASIS and ADNI. The initial classification methods consist of a 5-class ADNI classification and a 3-class OASIS classification. Three unique methodologies encompass binary-class inter-dataset models, which involve training on a single dataset and subsequently testing on another dataset for both ADNI and OASIS datasets. Additionally, a hybrid dataset model is also considered. The proposed methodology entails the concatenation of both datasets, followed by shuffling and subsequently conducting training and testing on the amalgamated dataset. The findings demonstrate impressive levels of accuracy, as Light Gradient Boosting Machine (LGBM) achieved a 99.63% accuracy rate for 5-class ADNI classification and a 95.75% accuracy rate by Multilayer Perceptron (MLP) for 3-class OASIS classification, both when hyperparameter tweaking was implemented. The K-nearest neighbor algorithm demonstrated exceptional performance, achieving an accuracy of 87.50% in ADNI-OASIS (2 Class) when utilizing the Select K Best method. The Gaussian Naive Bayes algorithm demonstrated exceptional performance in the OASIS-ADNI approach, attaining an accuracy of 77.97% using Chi-squared feature selection. The accuracy achieved by the Hybrid method, which utilized LGBM with hyperparameter optimization, was 99.21%. Furthermore, the utilization of Explainable AI approaches, particularly Lime, was implemented in order to augment the interpretability of the model.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005624000183/pdfft?md5=e9c710d51ce1b8bb949bd1c6ac280602&pid=1-s2.0-S2590005624000183-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141276619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative study of YOLOv5 and YOLOv8 for corrosion segmentation tasks in metal surfaces","authors":"Edmundo Casas ,&nbsp;Leo Ramos ,&nbsp;Cristian Romero ,&nbsp;Francklin Rivas-Echeverría","doi":"10.1016/j.array.2024.100351","DOIUrl":"10.1016/j.array.2024.100351","url":null,"abstract":"<div><p>This study delves into the comparative efficacy of YOLOv5 and YOLOv8 in corrosion segmentation tasks. We employed three unique datasets, comprising 4942, 5501, and 6136 images, aiming to thoroughly evaluate the models’ adaptability and robustness in diverse scenarios. The assessment metrics included precision, recall, F1-score, and mean average precision. Furthermore, graphical tests offered a visual perspective on the segmentation capabilities of each architecture. Our results highlight YOLOv8’s superior speed and segmentation accuracy across datasets, further corroborated by graphical evaluations. These visual assessments were instrumental in emphasizing YOLOv8’s proficiency in handling complex corroded surfaces. However, in the largest dataset, both models encountered challenges, particularly with overlapping bounding boxes. YOLOv5 notably lagged, struggling to achieve the performance standards set by YOLOv8, especially with irregular corroded surfaces. In conclusion, our findings underscore YOLOv8’s enhanced capabilities, establishing it as a preferable choice for real-world corrosion detection tasks. This research thus offers invaluable insights, poised to redefine corrosion management strategies and guide future explorations in corrosion identification.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005624000171/pdfft?md5=9e4e2adc95d4bf31d6930cbc85e19fa3&pid=1-s2.0-S2590005624000171-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141230560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inter-model interpretability: Self-supervised models as a case study","authors":"Ahmad Mustapha ,&nbsp;Wael Khreich ,&nbsp;Wes Masri","doi":"10.1016/j.array.2024.100350","DOIUrl":"https://doi.org/10.1016/j.array.2024.100350","url":null,"abstract":"<div><p>Since early machine learning models, metrics such as accuracy and precision have been the de facto way to evaluate and compare trained models. However, a single metric number does not fully capture model similarities and differences, especially in the computer vision domain. A model with high accuracy on a certain dataset might provide a lower accuracy on another dataset without further insights. To address this problem, we build on a recent interpretability technique called Dissect to introduce <em>inter-model interpretability</em>, which determines how models relate or complement each other based on the visual concepts they have learned (such as objects and materials). Toward this goal, we project 13 top-performing self-supervised models into a Learned Concepts Embedding (LCE) space that reveals proximities among models from the perspective of learned concepts. We further crossed this information with the performance of these models on four computer vision tasks and 15 datasets. The experiment allowed us to categorize the models into three categories and revealed the type of visual concepts different tasks required for the first time. This is a step forward for designing cross-task learning algorithms.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S259000562400016X/pdfft?md5=33f9642cc8597d6783b926660acecf8c&pid=1-s2.0-S259000562400016X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141095322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network intrusion detection leveraging multimodal features","authors":"Aklil Kiflay,&nbsp;Athanasios Tsokanos,&nbsp;Mahmood Fazlali,&nbsp;Raimund Kirner","doi":"10.1016/j.array.2024.100349","DOIUrl":"10.1016/j.array.2024.100349","url":null,"abstract":"<div><p>Network Intrusion Detection Systems (NIDSes) are essential for safeguarding critical information systems. However, the lack of adaptability of Machine Learning (ML) based NIDSes to different environments could cause slow adoption. In this paper, we propose a multimodal NIDS that combines flow and payload features to detect cyber-attacks. The focus of the paper is to evaluate the use of multimodal traffic features in detecting attacks, but not on a practical online implementation. In the multimodal NIDS, two random forest models are trained to classify network traffic using selected flow-based features and the first few bytes of protocol payload, respectively. Predictions from the two models are combined using a soft voting approach to get the final traffic classification results. We evaluate the multimodal NIDS using flow-based features and the corresponding payloads extracted from Packet Capture (PCAP) files of a publicly available UNSW-NB15 dataset. The experimental results show that the proposed multimodal NIDS can detect most attacks with average Accuracy, Recall, Precision and F<span><math><msub><mrow></mrow><mrow><mn>1</mn></mrow></msub></math></span> scores ranging from 98% to 99% using only six flow-based traffic features, and the first 32 bytes of protocol payload. The proposed multimodal NIDS provides a reliable approach to detecting cyber-attacks in different environments.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005624000158/pdfft?md5=571a5eb4d14694ec615bacb4ecbc6a5f&pid=1-s2.0-S2590005624000158-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141029468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"D2MNet for music generation joint driven by facial expressions and dance movements","authors":"Jiang Huang,&nbsp;Xianglin Huang,&nbsp;Lifang Yang,&nbsp;Zhulin Tao","doi":"10.1016/j.array.2024.100348","DOIUrl":"https://doi.org/10.1016/j.array.2024.100348","url":null,"abstract":"<div><p>In general, dance is always associated with music to improve stage performance effect. As we know, artificial music arrangement consumes a lot of time and manpower. While automatic music arrangement based on input dance video perfectly solves this problem. In the cross-modal music generation task, we take advantage of the complementary information between two input modalities of facial expressions and dance movements. Then we present Dance2MusicNet (D2MNet), an autoregressive generation model based on dilated convolution, which adopts two feature vectors, dance style and beats, as control signals to generate real and diverse music that matches dance video. Finally, a comprehensive evaluation method for qualitative and quantitative experiment is proposed. Compared to baseline methods, D2MNet outperforms better in all evaluating metrics, which clearly demonstrates the effectiveness of our framework.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005624000146/pdfft?md5=57bcf00a132e600642ca5c16a65b9121&pid=1-s2.0-S2590005624000146-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140893510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing face detection efficiency: Utilizing classification networks for lowering false positive incidences","authors":"Jianlin Zhang ,&nbsp;Chen Hou ,&nbsp;Xu Yang ,&nbsp;Xuechao Yang ,&nbsp;Wencheng Yang ,&nbsp;Hui Cui","doi":"10.1016/j.array.2024.100347","DOIUrl":"https://doi.org/10.1016/j.array.2024.100347","url":null,"abstract":"<div><p>The advancement of convolutional neural networks (CNNs) has markedly progressed in the field of face detection, significantly enhancing accuracy and recall metrics. Precision and recall remain pivotal for evaluating CNN-based detection models; however, there is a prevalent inclination to focus on improving true positive rates at the expense of addressing false positives. A critical issue contributing to this discrepancy is the lack of pseudo-face images within training and evaluation datasets. This deficiency impairs the regression capabilities of detection models, leading to numerous erroneous detections and inadequate localization. To address this gap, we introduce the WIDERFACE dataset, enriched with a considerable number of pseudo-face images created by amalgamating human and animal facial features. This dataset aims to bolster the detection of false positives during training phases. Furthermore, we propose a new face detection architecture that incorporates a classification model into the conventional face detection model to diminish the false positive rate and augment detection precision. Our comparative analysis on the WIDERFACE and other renowned datasets reveals that our architecture secures a lower false positive rate while preserving the true positive rate in comparison to existing top-tier face detection models.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005624000134/pdfft?md5=be911996c21c7c166881a8828f984b70&pid=1-s2.0-S2590005624000134-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140825066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive review of explainable AI for disease diagnosis","authors":"Al Amin Biswas","doi":"10.1016/j.array.2024.100345","DOIUrl":"https://doi.org/10.1016/j.array.2024.100345","url":null,"abstract":"<div><p>Nowadays, artificial intelligence (AI) has been utilized in several domains of the healthcare sector. Despite its effectiveness in healthcare settings, its massive adoption remains limited due to the transparency issue, which is considered a significant obstacle. To achieve the trust of end users, it is necessary to explain the AI models' output. Therefore, explainable AI (XAI) has become apparent as a potential solution by providing transparent explanations of the AI models' output. In this review paper, the primary aim is to review articles that are mainly related to machine learning (ML) or deep learning (DL) based human disease diagnoses, and the model's decision-making process is explained by XAI techniques. To do that, two journal databases (Scopus and the IEEE Xplore Digital Library) were thoroughly searched using a few predetermined relevant keywords. The PRISMA guidelines have been followed to determine the papers for the final analysis, where studies that did not meet the requirements were eliminated. Finally, 90 Q1 journal articles are selected for in-depth analysis, covering several XAI techniques. Then, the summarization of the several findings has been presented, and appropriate responses to the proposed research questions have been outlined. In addition, several challenges related to XAI in the case of human disease diagnosis and future research directions in this sector are presented.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005624000110/pdfft?md5=e1abc0e28d1ca274ca3562e4e862960b&pid=1-s2.0-S2590005624000110-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140816446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BT-Net: An end-to-end multi-task architecture for brain tumor classification, segmentation, and localization from MRI images","authors":"Salman Fazle Rabby ,&nbsp;Muhammad Abdullah Arafat ,&nbsp;Taufiq Hasan","doi":"10.1016/j.array.2024.100346","DOIUrl":"10.1016/j.array.2024.100346","url":null,"abstract":"<div><p>Brain tumors are severe medical conditions that can prove fatal if not detected and treated early. Radiologists often use MRI and CT scan imaging to diagnose brain tumors early. However, a shortage of skilled radiologists to analyze medical images can be problematic in low-resource healthcare settings. To overcome this issue, deep learning-based automatic analysis of medical images can be an effective tool for assistive diagnosis. Conventional methods generally focus on developing specialized algorithms to address a single aspect, such as segmentation, classification, or localization of brain tumors. In this work, a novel multi-task network was proposed, modified from the conventional VGG16, along with a U-Net variant concatenation, that can simultaneously achieve segmentation, classification, and localization using the same architecture. We trained the classification branch using the <em>Brain Tumor MRI Dataset</em>, and the segmentation branch using a “<em>Brain Tumor Segmentation</em> dataset. The integration of our method’s output can aid in simultaneous classification, segmentation, and localization of four types of brain tumors in MRI scans. The proposed multi-task framework achieved 97% accuracy in classification and a dice similarity score of 0.86 for segmentation. In addition, the method shows higher computational efficiency compared to existing methods. Our method can be a promising tool for assistive diagnosis in low-resource healthcare settings where skilled radiologists are scarce.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005624000122/pdfft?md5=36c2c4383abffb72e6a44ae52a4e5a0c&pid=1-s2.0-S2590005624000122-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140769030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}