Cross-Content Recommendation between Movie and Book using Machine Learning

Nawar, Afra; Toma, Nazia Tabassum; Mamun, Shamim Al; Kaiser, M. Shamim; Mahmud, Mufti; Rahman, Muhammad Arifur

doi:10.1109/aict52784.2021.9620432

Cited by 29 publications

(4 citation statements)

References 40 publications

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“…In recent years artificial intelligence (AI) has been applied in diverse problem domains to solve various challenging problems, including text classification [13,14,15,16], cyber security [17,18,19,20], neurological disease detection [21,22,23,12] and management [24,25,26,27,28,29], elderly care [30,31], fighting pandemic [32,33,34,35,36,37,38], and healthcare service delivery [39,40,41]. In particular, deep learning (DL) has attracted a lot of attention [6,7].…”

Section: Related Workmentioning

confidence: 99%

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

Hajamohideen

Shaffi

Mahmud

et al. 2022

Preprint

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Alzheimer's disease (AD) is a neurodegenerative disease that causes irreversible damage to several brain regions, including the hippocampus causing impairment in cognition, function, and behaviour. Early diagnosis of the disease will reduce the suffering of the patients and their family members. Towards this aim, in this paper, we propose a Siamese Convolutional Neural Network (CNN) architecture that employs the triplet-loss function for the representation of input MRI images as k-dimensional embeddings. We used both pre-trained and non-pre-trained CNNs to transform images into the embedding space. These embeddings are subsequently used for the 4-way classification of Alzheimer's Disease. The model efficacy was tested using the ADNI and OASIS datasets which produced an accuracy of 91.83% and 93.85%, respectively. Furthermore, obtained results are compared with similar methods proposed in the literature.

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Section: Related Workmentioning

confidence: 99%

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

Hajamohideen

Shaffi

Mahmud

et al. 2022

Preprint

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“…Artificial intelligence (AI) and machine learning (ML) have been widely used in diverse research areas, including anomaly detection, disease diagnosis, text mining, cyber security, etc. [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40]. Artificial Neural Networks (ANN) has been used in image segmentation and classification [41], [42].…”

Section: Introductionmentioning

confidence: 99%

Detecting COVID-19 From Lung Computed Tomography Images: A Swarm Optimized Artificial Neural Network Approach

et al. 2023

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COVID-19 has affected many people across the globe. Though vaccines are available now, early detection of the disease plays a vital role in the better management of COVID-19 patients. An Artificial Neural Network (ANN) powered Computer Aided Diagnosis (CAD) system can automate the detection pipeline accounting for accurate diagnosis, overcoming the limitations of manual methods. This work proposes a CAD system for COVID-19 that detects and classifies abnormalities in lung CT images using Artificial Bee Colony (ABC) optimised ANN (ABCNN). The proposed ABCNN approach works by segmenting the suspicious regions from the CT images of non-COVID and COVID patients using an ABC optimised region growing process and extracting the texture and intensity features from those suspicious regions. Further, an optimised ANN model whose input features, initial weights and hidden nodes are optimised using ABC optimisation classifies those abnormal regions into COVID and non-COVID classes. The proposed ABCNN approach is evaluated using the lung CT images collected from the public datasets. In comparison to other available techniques, the proposed ABCNN approach achieved a high classification accuracy of 92.37% when evaluated using a set of 470 lung CT images.INDEX TERMS Artificial neural networks, artificial bee colony algorithm, multilayer perceptron, resilient backpropagation, texture features, feature extraction, classification accuracy.

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“…In recent years artificial intelligence (AI) has been applied in diverse problem domains to solve various challenging problems including student engagement [10], virtual reality exposure therapy [11], text classification [12,13,14,15], cyber security [16,17,18,19], neurological disease detection [20,21,22] and management [23,24,25,26,27,28], elderly care [29,30], biological data mining [31,32], fighting pandemic [33,34,35,36,37,38,39], and healthcare service delivery [40,41,42]. In particular, convolutional neural network (CNN) has recently accomplished remarkable achievements in Pathology Informatics [43,44,45] and Bioinformatics [46,47,48], suggesting that CNN has the ability to solve these challenges.…”

Section: Introductionmentioning

confidence: 99%

Detection of Osteosarcoma using Deep Feature Extraction with Federated Learning and MLP from Histopathological Images

Chowdhury,

Mahmud,

Elahe

et al. 2023

Preprint

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Background: The most common type of cancerous bone tumour is Osteosarcoma. Microscopic analysis of coloured slides is mostly used for Osteosarcoma diagnosis. As Osteosarcoma is highly heterogeneous, pathologists struggle to get a consensus diagnosis. Moreover, manual analysis of microscopic images is laborious, time-consuming, and vulnerable to bias. Method: In this research, a framework that extracts features based on CNN is developed to predict Osteosarcoma to address previous studies' performance gaps and shortcomings. The dataset used for this experiment is obtained from pathology archives at Children's Medical Center in Dallas, which includes 1144 histology glass slides. First, data augmentation is used to extend the dataset size. This study found Blur, Contrast, and Random augmentation perform well for the Osteosarcoma dataset. Next, we primarily considered six pre-trained transfer learning (TL) models and modified the models to extract features. Then, we examined feature selectors like Genetic Algorithm, Principal Component Analysis, Recursive Feature Elimination and Baruto and found PCA selects the optimal feature set. Finally, we fed the selected features into a fine-tuned MLP called Grid search MLP (GsMLP) and utilised federated learning (FL) to ensure the diversity and privacy of the dataset. Results: Experiments have revealed that the modified Xception model performs better in extracting features for Osteosarcoma prediction. Also, the proposed framework performed better than state-of-the-art techniques such as Convolutional Neural Network, VGG19, Random Forest, and AdaBoost with 98.20% accuracy and 97.5% Receiver Operating Characteristic - Area Under the Curve.

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Cross-Content Recommendation between Movie and Book using Machine Learning

Cited by 29 publications

References 40 publications

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

Detecting COVID-19 From Lung Computed Tomography Images: A Swarm Optimized Artificial Neural Network Approach

Detection of Osteosarcoma using Deep Feature Extraction with Federated Learning and MLP from Histopathological Images

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