Brain tumor is a deadly disease and its classification is a challenging task for radiologists because of the heterogeneous nature of the tumor cells. Recently, computer-aided diagnosis-based systems have promised, as an assistive technology, to diagnose the brain tumor, through magnetic resonance imaging (MRI). In recent applications of pre-trained models, normally features are extracted from bottom layers which are different from natural images to medical images. To overcome this problem, this study proposes a method of multi-level features extraction and concatenation for early diagnosis of brain tumor. Two pre-trained deep learning models i.e. Inception-v3 and DensNet201 make this model valid. With the help of these two models, two different scenarios of brain tumor detection and its classification were evaluated. First, the features from different Inception modules were extracted from pre-trained Inception-v3 model and concatenated these features for brain tumor classification. Then, these features were passed to softmax classifier to classify the brain tumor. Second, pre-trained DensNet201 was used to extract features from various DensNet blocks. Then, these features were concatenated and passed to softmax classifier to classify the brain tumor. Both scenarios were evaluated with the help of three-class brain tumor dataset that is available publicly. The proposed method produced 99.34 %, and 99.51% testing accuracies respectively with Inception-v3 and DensNet201 on testing samples and achieved highest performance in the detection of brain tumor. As results indicated, the proposed method based on features concatenation using pre-trained models outperformed as compared to existing state-of-the-art deep learning and machine learning based methods for brain tumor classification.

Brain tumors are life-threatening for adults and children. However, accurate and timely detection can save lives. This study focuses on three different types of brain tumors: Glioma, meningioma, and pituitary tumors. Many studies describe the analysis and classification of brain tumors, but few have looked at the problem of feature engineering. Methods are needed to overcome the drawbacks of manual diagnosis and conventional feature-engineering techniques. An automatic diagnostic system is thus necessary to extract features and classify brain tumors accurately. While progress continues to be made, the automatic diagnoses of brain tumors still face challenges of low accuracy and high false-positive results. The model presented in this study, which offers improvements for feature extraction and classification, uses deep learning and machine learning for the assessment of brain tumors. Deep learning is used for feature extraction and encompasses the application of different models such as fine-tuned Inception-v3 and fine-tuned Xception. The classification of brain tumors is explored through deep- and machine-learning algorithms including softmax, Random Forest, Support Vector Machine, K-Nearest Neighbors, and the ensemble technique. The results of these approaches are compared with existing methods. The Inception-v3 model has a test accuracy of 94.34% and achieves the highest performance compared with other recently reported methods. This improvement may be sufficient to support a significant role in clinical applications for brain tumor analysis. Furthermore, this type of approach can be used as an effective decision-support tool for radiologists in medical diagnostics as a second opinion based on the magnetic resonance imaging (MRI) analysis. It may also save valuable time for radiologists who have to manually review numerous MRI images of patients.

Artificial intelligence (AI) has provided significant help in many fields of life. This study proposed a framework that helped in understanding customers’ attitudes about the adoption of Robo-advisors. The role of the Technology Readiness Index moderated as one of the primary relationships. A total of 208 potential users of Robo-advisor services provided the data that confirmed the validity of the model. This model provided the input for structural equation modeling and analysis of the study hypotheses. The results indicated that consumers showed positive attitudes about Robo-advisor services, with the moderating effect of Technology Readiness Index dimensions, namely, contributors and inhibitors. Perceived ease of use, perceived usefulness, and perceived convenience influenced consumers in developing positive attitudes about this service. Financial businesses can design better AI Robo-advisor services to fulfill the requirements of a wide range of consumers. This proposed framework contributes to the consumers’ understanding of behavioral intentions for the use of Robo-advisors in FinTech.

Rice is one of the fundamental food items that comes in many varieties with their associated benefits. It can be sub-categorized based on its visual features like texture, color, and shape. Using these features, the automatic classification of rice varieties has been studied using various machine learning approaches for marketing and industrial use. Due to the outstanding performance of deep learning, several models have been proposed to assist in vision tasks like classification and detection. Regardless of their best results on accuracy metrics, they have been observed as overly excessive for computational resources and expert supervision. To address these challenges, this paper proposes three deep learning models that offer similar performance with 10% lighter computational overhead in comparison to existing best models. Moreover, they have been trained for end-to-end flow to demonstrate minimum expert supervision for pre-processing and feature engineering sub-tasks. The results can be observed as promising for classifying rice among five varieties, namely Arborio, Basmati, Ipsala, Jasmine, and Karacadag. The process and performance of the trained models can be extended for edge and mobile devices for field-specific tasks autonomously.

Due to the rapid advancement of technology, the volume of online text data from numerous various disciplines is increasing significantly over time. Therefore, more work is needed to create systems that can effectively classify text data in accordance with its content, facilitating processing and the extraction of crucial information. Since these non-automated systems use manual feature extraction and classification, which is error-prone and time-consuming by choosing the best appropriate algorithms for feature extraction and classification, traditional procedures are typically resource intensive (computational, human, etc.), which is not a viable solution. To address the shortcomings of traditional approaches, we offer a unique text categorization strategy based on a well-known DL algorithm called BERT. The proposed framework is trained and tested using cutting-edge text datasets, such as the UCI email dataset, which includes spam and non-spam emails, and the BBC News dataset, which includes multiple categories such as tech, sports, politics, business, and entertainment. The system achieved the highest accuracy of 91.4% and can be used by different organizations to classify text-based data with a high performance. The effectiveness of the proposed framework is evaluated using multiple evaluation metrics such as Accuracy, Precision, and Recall.

This study proposes a Generative Adversarial Network (GAN)-based method for virtual furniture replacement within indoor scenes. The proposed method addresses the challenge of accurately positioning new furniture in an indoor space by combining image reconstruction with geometric matching through combining spatial transformer networks and GANs. The system leverages deep learning architectures like Mask R-CNN for executing image segmentation and generating masks, and it employs DeepLabv3+, EdgeConnect algorithms, and ST-GAN networks for carrying out virtual furniture replacement. With the proposed system, furniture shoppers can obtain a virtual shopping experience, providing an easier way to understand the aesthetic effects of furniture rearrangement without putting in effort to physically move furniture. The proposed system has practical applications in the furnishing industry and interior design practices, providing a cost-effective and efficient alternative to physical furniture replacement. The results indicate that the proposed method achieves accurate positioning of new furniture in indoor scenes with minimal distortion or displacement. The proposed system is limited to 2D front-view images of furniture and indoor scenes. Future work would involve synthesizing 3D scenes and expanding the system to replace furniture images photographed from different angles. This would enhance the efficiency and practicality of the proposed system for virtual furniture replacement in indoor scenes.