Recent Advances in Computer Science and Communications - Volume 17, Issue 4, 2024

The Internet of Medical Things (IoMT) has emerged as a paradigm shift in healthcare, integrating the Internet of Things (IoT) with medical devices, sensors, and healthcare systems. From peripheral devices that monitor vital signs to remote patient monitoring systems and smart hospitals, IoMT provides a vast array of applications that empower healthcare professionals. However, the integration of IoMT presents numerous obstacles, such as data security, privacy concerns, interoperability, scalability, and ethical considerations. For the successful integration and deployment of IoMT, addressing these obstacles is essential. Federated Learning (FL) permits collaborative model training while maintaining data privacy in distributed environments like IoMT. By incorporating Explainable Artificial Intelligence (XAI) techniques, the resulting models become more interpretable and transparent, enabling healthcare professionals to comprehend the underlying decision-making processes. This integration not only improves the credibility of Artificial Intelligence models but also facilitates the detection of biases, errors, and peculiar patterns in the data. The combination of FL and XAI contributes to the development of more privacy-preserving, trustworthy, and explainable AI systems, which are essential for the development of dependable and ethically sound IoMT applications. Hence, the aim of this paper is to conduct a literature review on the amalgamation of FL and XAI for IoMT.

Internet of Medical Things (IoMT) is a technology that encompasses medical devices, wearable sensors, and applications connected to the Internet. In road accidents, it plays a crucial role in enhancing emergency response and reducing the impact of accidents on victims. Smart Transportation uses this technology to improve the efficiency and safety of transportation systems. The current Artificial Intelligence applications lack transparency and interpretability which is of utmost importance in critical transportation scenarios, such as autonomous vehicles, air traffic control systems, and traffic management systems. Explainable Artificial Intelligence (XAI) provides a clear, transparent explanation and actions. Traditional Machine Learning techniques have enabled Intelligent Transportation systems by performing centralized vehicular data training at the server where data sharing is needed, thus introducing privacy issues. To reduce transmission overhead and achieve privacy, a collaborative and distributed machine learning approach called Federated Learning (FL) is used. Here only model updates are transmitted instead of the entire dataset. This paper provides a comprehensive survey on the prediction of traffic using Machine Learning, Deep Learning, and FL. Among these, FL can predict traffic accurately without compromising privacy. We first present the overview of XAI and FL in the introduction. Then, we discuss the basic concepts of FL and its related work, the FL-IoMT framework, and motivations for using FL in transportation. Subsequently, we discuss the applications of using FL in transportation and open-source projects. Finally, we highlight several research challenges and their possible directions in FL.

The Internet of Medical Things (IoMT), a growing field, involves the interconnection of medical devices and data sources. It connects smart devices with data and optimizes patient data with real time insights and personalized solutions. It is mandatory to hold the development of IoMT and join the evolution of healthcare. This integration of Transfer Learning and Explainable AI for IoMT is considered to be an essential advancement in healthcare. By making use of knowledge transfer between medical domains, Transfer Learning enhances diagnostic accuracy while reducing data necessities. This makes IoMT applications more efficient which is considered to be a mandate in today’s healthcare. In addition, explainable AI techniques offer transparency and interpretability to AI driven medical decisions. This can foster trust among healthcare professionals and patients. This integration empowers personalized medicine, supports clinical decision making, and confirms the responsible handling of sensitive patient data. Therefore, this integration promises to revolutionize healthcare by merging the strengths of AI driven insights with the requirement for understandable, trustworthy, and adaptable systems in the IoMT ecosystem.

India has evaluated 77 million people with diabetes, which makes it the second most elaborated disease in the world. Diabetes is a chronic syndrome that occurs with increased sugar levels in the blood cells. Once diabetes is diagnosed and untreated by physicians, it may affect the internal organs slowly, so there is a necessity for early prediction. Popular Machine Learning (ML) techniques existed for the early prediction of diabetes mellitus. A significant perspective is to be considered in total management by machine learning algorithms, but it is not a good enough model to predict DMT2. Therefore, Deep learning (DL) models are utilized to produce enhanced prediction accuracy. The ML methods are evaluated and analyzed distinctly on the inconspicuous test information. DL is a subpart of ML with many data sets recurrently used to train the system. IoT was another emerging technology-based Healthcare Monitoring System (HMS) built to support the vision of patients and doctors in the healthcare domain. This paper aims to survey ML and DL techniques relevant to Dissimilar Disease prediction in Diabetes Mellitus. Finally, by doing a study on it, deep learning methods performed well in predicting the dissimilar diseases related to diabetes and also other disease predictions using m-IoT devices. This study will contribute to future deep-learning ideas that will assist in detecting diabetic-related illnesses with greater accuracy.

Background: Visual question answering (VQA) is a challenging task that requires multimodal reasoning and knowledge. The objective of VQA is to answer natural language questions based on corresponding present information in a given image. The challenge of VQA is to extract visual and textual features and pass them into a common space. However, the method faces the challenge of object detection being present in an image and finding the relationship between objects. Methods: In this study, we explored different methods of feature fusion for VQA, using pretrained models to encode the text and image features and then applying different attention mechanisms to fuse them. We evaluated our methods on the DAQUAR dataset. Results: We used three metrics to measure the performance of our methods: WUPS, Acc, and F1. We found that concatenating raw text and image features performs slightly better than selfattention for VQA. We also found that using text as query and image as key and value performs worse than other methods of cross-attention or self-attention for VQA because it might not capture the bidirectional interactions between the text and image modalities. Conclusion: In this paper, we presented a comparative study of different feature fusion methods for VQA, using pre-trained models to encode the text and image features and then applying different attention mechanisms to fuse them. We showed that concatenating raw text and image features is a simple but effective method for VQA while using text as query and image as key and value is a suboptimal method for VQA. We also discussed the limitations and future directions of our work.

Background: Reddit comments are a valuable source of natural language data where emotion plays a key role in human communication. However, emotion recognition might be a difficult task that requires understanding the context and sentiment of the texts. In this paper, we aim to compare the effectiveness of four Recurrent Neural Network (RNN) models for classifying the emotions of Reddit comments. Methods: We use a small dataset of 4,922 comments labeled with four emotions: approval, disapproval, love, and annoyance. We also use pre-trained Glove.840B.300d embeddings as the input representation for all models. The models we compare are SimpleRNN, Long Shortterm Memory (LSTM), bidirectional LSTM, and Gated Recurrent Unit (GRU). We experiment with different text preprocessing steps, such as removing stopwords and applying stemming, removing negation from stopwords, and the effect of setting the embedding layer as trainable on the models. Results: We find that GRU outperforms all other models, achieving an accuracy of 74%. Bidirectional LSTM and LSTM are close behind, while SimpleRNN performs the worst. We observe that the low accuracy is likely due to the presence of sarcasm, irony, and complexity in the texts. We also notice that setting the embedding layer as trainable improves the performance of LSTM but increases the computational cost and training time significantly. We analyze some examples of misclassified texts by GRU and identify the challenges and limitations of the dataset and the models. Conclusion: In our study GRU was found to be the best model for emotion classification of Reddit comments among the four RNN models we compared. We also discuss some future directions for research to improve the emotion recognition task on Reddit comments. Furthermore, we provide an extensive discussion of the applications and methods behind each technique in the context of the paper.