To decrease workload on lightweight devices, this project accelerates the computation of Convolution Neural Networks (CNNs) and preserves accuracy through modifying the CNNs’ training process. First, this research implements distributed computing to optimally divide the network workload onto both devices and the cloud. To reduce communication latency between devices and the cloud, this research introduces feature pruning by setting elements in the communicated feature to 0. However, naively pruning the feature causes a significant accuracy drop. To compensate for this limitation, this research applies pruning-aware training to preserve the CNNs’ task performance. This research evaluates the proposed methods on multiple datasets and CNN models, like VGG-11 and ResNet-18 with PyTorch. Empirical results demonstrate that the methods can reduce the computational latency by 50-75% with a negligible 1% accuracy loss. Specifically, this research first identifies the system bottleneck by comparing on-device, on-cloud, and communication latencies (on-device: 14.8%, on-cloud: 1.7%, communication: 83.5%). Then, this research compares multiple pruning strategies and observe the superiority of magnitude-based pruning. At 0.992 sparsity, magnitude-based pruning outperforms other strategies by 45% in accuracy. Finally, this research verifies the effectiveness of the proposed pruning-aware training method by comparing it with the baseline at various splitting points and networks. Pruning-aware training decreases the accuracy loss by up to 26% at 0.998 sparsity. In conclusion, even though distributed computing accelerates applications on lightweight devices, compressing the communication cost is crucial and challenging. This research proposed methods effectively reduce communication latency without sacrificing accuracy, conserving the effectiveness of CNN.

The sports industry has witnessed a transformative shift with the advent of big data technologies. This paper explores the profound influence of big data on athlete training and evaluation, examining how it has emerged as a catalyst for empowering athletes from minority backgrounds. By leveraging vast amounts of data, sports organizations and athletes gain valuable insights, make informed decisions, and optimize performance. Traditional, subjective methods of athlete evaluation are being replaced by objective, data-driven approaches that provide a more accurate assessment of performance. Big data also promotes inclusivity within the sports industry by identifying talented individuals from minority backgrounds who may have been overlooked in the past. Several case studies highlight the role of big data in revolutionizing sports, such as its use in basketball for improved decision-making and soccer for optimized training regimens. In conclusion, big data transforms athlete training, evaluation, and inclusivity in sports while shaping the future of the industry.

The advancement of artificial intelligence (AI) gave rise to chatbots, which is a type of AI-powered software that communicates via natural language. Chatbots have been used in diverse contexts, delivering significant convenience to the consumers. Nonetheless, this technology encounters ambivalent attitudes from consumers. Some aspects of the chatbot technology are evoking distrustful attitudes among consumers, while the others are cultivating a sense of trust. Thus, the objective of the current paper is to outline and analyze key factors that affect consumer trust and elucidate strategies that firms can adopt to optimize trust. According to recent studies, consumer distrust primarily stems from algorithmic bias, privacy and security concerns, and the lack of algorithmic transparency; on the other hand, consumer trust is formed due to anthropomorphic attributes of chatbots, particularly warmth and competence. To reduce consumer distrust, companies are advised to first identify and minimize existing real risks in their products, then deliver transparency to the public to establish a trustworthy image. To increase trust, companies are suggested to improve upon the anthropomorphic attributes of chatbots. Contributions and limitations of the paper are also discussed to highlight areas that require further investigation in the field of chatbots as well as AI in general.

Brain tumor has been a serious disease to human beings for a long time. Brain tumors have posed a significant health threat to humanity for many years. If left untreated in its early stages, a brain tumor can become malignant, drastically reducing survival chances. Throughout the decades, numerous individuals have endured the hardships of brain tumors, and tragically, some have succumbed to this condition. However, deep learning techniques offer a promising avenue for precise and efficient brain tumor diagnosis. Utilizing this technology enables the early detection and treatment of benign tumors, potentially saving lives and preventing unnecessary loss. In this review paper, two previous research on how different deep learning models perform on the brain tumor diagnosis would be illustrated. In the first research, the performance of five models would be compared with each other. In the second research, Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) would be compared with each other. Furthermore, the examination of two research methods will delve into how various techniques can enhance model performance. Deep learning techniques also find numerous real-life applications. The two important applications are Home Diagnosis and In-Hospital Assistance, and the benefits of applying deep learning techniques in these two areas would also be illustrated. In addition, several suggestions would be proposed based on the applications of deep learning technique.

Recently we had the fact that cardiovascular disease has become one of the major threats to human life, which leads to the significance of the research around the prevention and cure of such disease. Recently, machine learning algorithms are utilized for the prediction of a certain person who has an illness or not. To verify the effectiveness of predicting cardiovascular disease using machine learning methods, we predict cardiovascular disease given features of a person’s life habits and illness history from the Behavioral Risk Factor Surveillance System. Therefore, 5 models are selected, including SVM, logistic regression, decision tree, fully connected network, and XGBoost to evaluate the performance via confusion matrix and ROG curves. Plus, the dataset is highly unbalanced, so we also implemented SMOTETomek re-sampling algorithms to evaluate the models’ performance on such kinds of datasets. Results exhibited that XGBoost performs the best on the given dataset, hence deep research on improving the performance using XGBoost is highly recommended.

Among the numerous hospitalized patients with chronic diseases, diabetes patients are under a higher readmission rate, which poses challenges and pressures to both patients and the healthcare system. To predict the likelihood of diabetic patients being readmitted within a short amount of time, this paper utilizes various machine learning-based models for performance analysis and comparison. By selecting appropriate datasets, cleaning and preprocessing data, the models were trained to forecast the probability of patient readmission. The paper compares the performance metrics of six classifiers: XGBoost, logistic regression, GBDT, decision tree, random forest, and deep neural network. The metrics include accuracy, f1 score, precision, recall, and ROC curve. Experimental results demonstrate that XGBoost exhibits better adaptability to complex data and achieves higher mean values of Accuracy (64.43%), f1 score (59.16%), recall (55.9%), and ROC (70.14%) in readmission prediction.

Cardiovascular disease is one of the deadliest diseases worldwide, causing millions of deaths every year. Major risk factors include hypertension, hyperlipidemia, smoking, unhealthy diet, and lack of physical activity. To achieve a simple and effective prediction of cardiovascular disease, a study comparing the performance of common machine learning algorithms was conducted. The dataset used in this research consists of a population of 70,000 individuals from Kaggle. During the data processing phase, abnormal values within the feature variables were removed, and a BMI feature variable was added to the dataset to visualize the relationships between the data more intuitively. Deep neural networks were used to predict cardiovascular disease and were compared with eight traditional machine learning algorithms with respect to accuracy, F1 score, PR and ROC. The results indicated that the deep neural network (DNN) is the optimal model for predicting cardiovascular disease.

The swift advancements in artificial intelligence (AI) and machine learning have profoundly impacted the realm of medical research, particularly in the realm of diagnosing and treating intricate conditions such as brain tumors. These tumors, characterized by unregulated cell proliferation, pose significant challenges. The complexities inherent in brain tumor diagnosis stem from the intricate nature of these tumors, symptom overlap with other ailments, and the inherent complexity of the brain itself. Nevertheless, the application of an advanced machine learning algorithm known as Federated Learning (FL) has demonstrated its potential to address data privacy concerns and enhance diagnostic accuracy in this context. This essay discusses the application of FL which is a decentralized training strategy in brain tumor research. FL allows multiple institutions to train the model collaboratively without data sharing. The key advancement includes the improved U-Net model implementation and the utilization of Convolutional Neural Network (CNN) Ensemble Architectures for brain tumor identification. This paper also discusses the potential of FL in optimizing weight sharing for model aggregation in heterogeneous data. Furthermore, it underscores the important role of FL in modern healthcare since FL also solves the privacy concern in smart healthcare. However, challenges such as communication lag, data heterogeneity, and computational cost still exist.

In the ever-evolving field of autonomous driving, vehicles have evolved into mobile computing centres, accumulating and processing vast amounts of data, including environmental variables, driver behaviours, and preferences. Conventional centralized data processing methods face privacy and security vulnerabilities. To address these challenges, federated learning technology has emerged as a promising alternative, with its decentralized, privacy-preserving architecture. This review explores the application of federated learning in autonomous driving, focusing on perception, prediction, and communication scenarios, including research such as using federated learning to enhance the vehicle’s ability to predict steering angle, object detection, and multimodal sensor data fusion. In addition, this review investigates the improvement of communication efficiency through techniques such as Distributed Federated Learning (DFL), Selective Federated Reinforcement Learning (SFRL), and Vehicle-to-everything (V2X) communication. The analysis indicated that federated learning holds great promise in autonomous driving, significantly enhancing vehicle performance in perception, prediction, and communication. However, challenges like data heterogeneity and communication costs persist. Future research should prioritize refining aggregation algorithms, minimizing communication overhead, and adapting federated learning to evolving autonomous driving technologies.

Deep learning is a subfield of artificial intelligence and machine learning, and it is becoming a popular topic in recent years. It is a powerful tool in solving complex tasks and achieve state-of-the-art results in many areas, including language processing, computer vision, and more. This paper briefly introduced two main deep learning models, Convolutional Neural Networks (CNNs) and Generative Adversary Networks (GANs) and their applications in medical imaging. CNNs are often used in image recognition tasks, like separating different organs in one medical image. While GANs are better doing medical image generation tasks, like creating an X-ray image of chest. This study introduced some deep learning methods for image segmentation, image classification and image generation. Not only are the basic CNNs and GANs architectures used, but also some improvements and modification involved. These methods greatly expand the existing medical image datasets. They also save lots of time for doctors and radiologists from labeling and recognizing those medical images. Deep learning methods are super strong in processing complex and numerous medical images. However, there are still some limitations caused by the lack of training datasets and learning models.