Secure-Enhanced Federated Learning for AI-Empowered Electric Vehicle Energy Prediction

Wang, Weizheng; Memon, Fida Hussain; Lian, Zhuotao; Yin, Zhimeng; Pham, Quoc-Viet; Gadekallu, Thippa Reddy; Dev, Kapal; Su, Chunhua

doi:10.1109/mce.2021.3116917

Cited by 72 publications

(30 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Osteosarcoma metastases, which are typically secondary to hematogenous dissemination and the occurrence of lymph system is extremely rare, have been identified to be significantly associated with poor prognosis (1-4, 7, 20, 21). Comparing to the most common lung metastases, osteosarcoma patients with lymph node involvement have a worse prognosis, suggesting that the invasion of the lymph nodes is an important indicator for the assessment of malignancy stage and the selection of a correct treatment protocol (22,23).Meanwhile, studies points out that FDG-PET and 99m Tc-labeled biomineralization (24)(25)(26)(27). Therefore, real-time lymph node surveillance and radical treatment for osteosarcoma patients with a high risk of lymph node metastasis will improve patient survival (8).…”

Section: Discussionmentioning

confidence: 99%

The Construction and Development of a Clinical Prediction Model to Assess Lymph Node Metastases in Osteosarcoma

Dong

Wang

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

Background: This study aimed to construct a clinical prediction model for osteosarcoma patients to evaluate the influence factors for the occurrence of lymph node metastasis (LNM).Methods: In our retrospective study, a total of 1,256 patients diagnosed with chondrosarcoma were enrolled from the SEER (Surveillance, Epidemiology, and End Results) database (training cohort, n = 1,144) and multicenter dataset (validation cohort, n = 112). Both the univariate and multivariable logistic regression analysis were performed to identify the potential risk factors of LNM in osteosarcoma patients. According to the results of multivariable logistic regression analysis, A nomogram were established and the predictive ability was assessed by calibration plots, receiver operating characteristics (ROCs) curve, and decision curve analysis (DCA). Moreover, Kaplan-Meier plot of overall survival (OS) was plot and a web calculator visualized the nomogram.Results: Five independent risk factors [chemotherapy, surgery, lung metastases, lymphatic metastases (M-stage) and tumor size (T-stage)] were identified by multivariable logistic regression analysis. What's more, calibration plots displayed great power both in training and validation group. DCA presented great clinical utility. ROCs curve provided the predictive ability in the training cohort (AUC = 0.805) and the validation cohort (AUC = 0.808). Moreover, patients in LNN group had significantly better survival than that in LNP group both in training and validation group.Conclusion: In this study, we constructed and developed a nomogram with risk factors, which performed well in predicting risk factors of LNM in osteosarcoma patients. It may give a guide for surgeons and oncologists to optimize individual treatment and make a better clinical decision.

show abstract

Section: Discussionmentioning

confidence: 99%

The Construction and Development of a Clinical Prediction Model to Assess Lymph Node Metastases in Osteosarcoma

Dong

Wang

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…FL is a distributed machine learning approach that encourages the participation of all wearable devices and centralized cloud servers to train a shared global model [ 23 , 24 , 25 , 26 , 27 ]. In other words, it is a training method across several decentralized edge devices or servers that keep local data samples without exchanging them [ 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems

Arikumar

Prathiba

Alazab

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Recent technological developments, such as the Internet of Things (IoT), artificial intelligence, edge, and cloud computing, have paved the way in transforming traditional healthcare systems into smart healthcare (SHC) systems. SHC escalates healthcare management with increased efficiency, convenience, and personalization, via use of wearable devices and connectivity, to access information with rapid responses. Wearable devices are equipped with multiple sensors to identify a person’s movements. The unlabeled data acquired from these sensors are directly trained in the cloud servers, which require vast memory and high computational costs. To overcome this limitation in SHC, we propose a federated learning-based person movement identification (FL-PMI). The deep reinforcement learning (DRL) framework is leveraged in FL-PMI for auto-labeling the unlabeled data. The data are then trained using federated learning (FL), in which the edge servers allow the parameters alone to pass on the cloud, rather than passing vast amounts of sensor data. Finally, the bidirectional long short-term memory (BiLSTM) in FL-PMI classifies the data for various processes associated with the SHC. The simulation results proved the efficiency of FL-PMI, with 99.67% accuracy scores, minimized memory usage and computational costs, and reduced transmission data by 36.73%.

show abstract

“…For another, due to the lack of scientific management and scheduling strategy, the production of agricultural products is not reasonable ( 4 ). The exploration of advanced agricultural technology has lasted for at least a 100 years, and some technological breakthroughs have been made in some key fields ( 5 , 6 ). However, this process exerts an imperceptible influence as we all know, which needs to be accumulated over a long period of time to make progress ( 7 ).…”

Section: Introductionmentioning

confidence: 99%

Deep Data Analysis-Based Agricultural Products Management for Smart Public Healthcare

Yan

Zhang

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

Agricultural is an indispensably public healthcare industry for human beings at any time and smart management of it is of great significance. Since substantial technical advance relies on long-term efforts and continuous progress, reasonably scheduling the distribution of agricultural products acts as a key aspect of smart public healthcare. The most intuitive factor affecting the distribution of agricultural products is its dynamic price. Forecasting price fluctuations in advance can optimize the distribution of agricultural products and pave the way to smart public healthcare. Most researchers study the prices of various agricultural products separately, without considering the interaction of different agricultural products in the time dimension. This study introduces a typical deep learning model named graph neural network (GNN) for this purpose and proposes deep data analysis-based agricultural products management for smart public healthcare (named GNN-APM for short). The highlight of GNN-APM is to take latent correlations among multiple types of agricultural products into consideration when modeling evolving rules of price sequences. A case study is set up with the use of real-world data of the agricultural products market. Simulative results reveal that the designed GNN-APM functions well.

show abstract

Secure-Enhanced Federated Learning for AI-Empowered Electric Vehicle Energy Prediction

Cited by 72 publications

References 9 publications

The Construction and Development of a Clinical Prediction Model to Assess Lymph Node Metastases in Osteosarcoma

The Construction and Development of a Clinical Prediction Model to Assess Lymph Node Metastases in Osteosarcoma

FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems

Deep Data Analysis-Based Agricultural Products Management for Smart Public Healthcare

Contact Info

Product

Resources

About