Parkinson’s disease remains a major clinical issue in terms of early detection,especially during its prodromal stage when symptoms are not evident or not distinct.To address this problem,we proposed a new deep learni...Parkinson’s disease remains a major clinical issue in terms of early detection,especially during its prodromal stage when symptoms are not evident or not distinct.To address this problem,we proposed a new deep learning 2-based approach for detecting Parkinson’s disease before any of the overt symptoms develop during their prodromal stage.We used 5 publicly accessible datasets,including UCI Parkinson’s Voice,Spiral Drawings,PaHaW,NewHandPD,and PPMI,and implemented a dual stream CNN–BiLSTM architecture with Fisher-weighted feature merging and SHAP-based explanation.The findings reveal that the model’s performance was superior and achieved 98.2%,a F1-score of 0.981,and AUC of 0.991 on the UCI Voice dataset.The model’s performance on the remaining datasets was also comparable,with up to a 2–7 percent betterment in accuracy compared to existing strong models such as CNN–RNN–MLP,ILN–GNet,and CASENet.Across the evidence,the findings back the diagnostic promise of micro-tremor assessment and demonstrate that combining temporal and spatial features with a scatter-based segment for a multi-modal approach can be an effective and scalable platform for an“early,”interpretable PD screening system.展开更多
College classes are becoming increasingly large.A critical component in scaling class size is the collaboration and interactions among instructors,teaching assistants,and students.We develop a prototype of an intellig...College classes are becoming increasingly large.A critical component in scaling class size is the collaboration and interactions among instructors,teaching assistants,and students.We develop a prototype of an intelligent voice instructorassistant system for supporting large classes,in which Amazon Web Services,Alexa Voice Services,and self-developed services are used.It uses a scraping service for reading the questions and answers from the past and current course discussion boards,organizes the questions in JavaScript object notation format,and stores them in the database,which can be accessed by Amazon web services Alexa skills.When a voice question from a student comes,Alexa is used for translating the voice sentence into texts.Then,Siamese deep long short-term memory model is introduced to calculate the similarity between the question asked and the questions in the database to find the best-matched answer.Questions with no match will be sent to the instructor,and instructor’s answer will be added into the database.Experiments show that the implemented model achieves promising results that can lead to a practical system.Intelligent voice instructor-assistant system starts with a small set of questions.It can grow through learning and improving when more and more questions are asked and answered.展开更多
VoIP (Voice over IP) is a rapidly growing area with great market potential. To promote it for both commercial and research purposes, a prototype VoIP system based on state-of-the-art Motorola communication techniques ...VoIP (Voice over IP) is a rapidly growing area with great market potential. To promote it for both commercial and research purposes, a prototype VoIP system based on state-of-the-art Motorola communication techniques has been developed. It is a gateway system integrating a PBX and a VoIP module. All components that H.323 defines to support VoIP are implemented in the VoIP module, though in a simplified manner. As an embedded system, the system features real timeness and task distributiveness. A number of additional techniques are used to improve the performance, including noise suppression, zero copy, and buffer structure optimization. When refined in interoperability, the system will also readily serve as a product.展开更多
基金supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2025/03/32440).
文摘Parkinson’s disease remains a major clinical issue in terms of early detection,especially during its prodromal stage when symptoms are not evident or not distinct.To address this problem,we proposed a new deep learning 2-based approach for detecting Parkinson’s disease before any of the overt symptoms develop during their prodromal stage.We used 5 publicly accessible datasets,including UCI Parkinson’s Voice,Spiral Drawings,PaHaW,NewHandPD,and PPMI,and implemented a dual stream CNN–BiLSTM architecture with Fisher-weighted feature merging and SHAP-based explanation.The findings reveal that the model’s performance was superior and achieved 98.2%,a F1-score of 0.981,and AUC of 0.991 on the UCI Voice dataset.The model’s performance on the remaining datasets was also comparable,with up to a 2–7 percent betterment in accuracy compared to existing strong models such as CNN–RNN–MLP,ILN–GNet,and CASENet.Across the evidence,the findings back the diagnostic promise of micro-tremor assessment and demonstrate that combining temporal and spatial features with a scatter-based segment for a multi-modal approach can be an effective and scalable platform for an“early,”interpretable PD screening system.
基金The authors wish to thank their colleagues and students who were involved in this study and provided valuable implementation and technical support.The research is partly supported by general funding at IoT and Robotics Education Lab and FURI program at Arizona State University and is partly supported by China Scholarship Council,Guangdong Science and Technology Department,under Grant Number 2016A010101020,2016A010101021,and 2016A010101022Guangzhou Science and Information Bureau under Grant Number 201802010033.
文摘College classes are becoming increasingly large.A critical component in scaling class size is the collaboration and interactions among instructors,teaching assistants,and students.We develop a prototype of an intelligent voice instructorassistant system for supporting large classes,in which Amazon Web Services,Alexa Voice Services,and self-developed services are used.It uses a scraping service for reading the questions and answers from the past and current course discussion boards,organizes the questions in JavaScript object notation format,and stores them in the database,which can be accessed by Amazon web services Alexa skills.When a voice question from a student comes,Alexa is used for translating the voice sentence into texts.Then,Siamese deep long short-term memory model is introduced to calculate the similarity between the question asked and the questions in the database to find the best-matched answer.Questions with no match will be sent to the instructor,and instructor’s answer will be added into the database.Experiments show that the implemented model achieves promising results that can lead to a practical system.Intelligent voice instructor-assistant system starts with a small set of questions.It can grow through learning and improving when more and more questions are asked and answered.
文摘VoIP (Voice over IP) is a rapidly growing area with great market potential. To promote it for both commercial and research purposes, a prototype VoIP system based on state-of-the-art Motorola communication techniques has been developed. It is a gateway system integrating a PBX and a VoIP module. All components that H.323 defines to support VoIP are implemented in the VoIP module, though in a simplified manner. As an embedded system, the system features real timeness and task distributiveness. A number of additional techniques are used to improve the performance, including noise suppression, zero copy, and buffer structure optimization. When refined in interoperability, the system will also readily serve as a product.
