Listar vol. 6, nº 7, september 2021 por título 

Listar vol. 6, nº 7, september 2021 por título

Mostrando ítems 5-12 de 12

    • Design and Development of an Energy Efficient Multimedia Cloud Data Center with Minimal SLA Violation 

      Biswas, Nirmal Kr.; Banerjee, Sourav; Biswas, Utpal (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 09/2021)
      Multimedia computing (MC) is rising as a nascent computing paradigm to process multimedia applications and provide efficient multimedia cloud services with optimal Quality of Service (QoS) to the multimedia cloud users. ...

    • Editor's Note 

      Chun-Wei Lin, Jerry; Srivastava, Gautam; Tseng, Vicent S. (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 09/2021)
      In today’s world, we have witnessed an onset of multimedia content being uploaded/downloaded and shared through a multitude of platforms both online and offline. In support of this trend, multimedia processing and analyzing ...

    • Imputation of Rainfall Data Using the Sine Cosine Function Fitting Neural Network 

      Chan Chiu, Po; Selamat, Ali; Krejcar, Ondrej; Kuok Kuok, King; Herrera-Viedma, Enrique; Fenza, Giuseppe (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 09/2021)
      Missing rainfall data have reduced the quality of hydrological data analysis because they are the essential input for hydrological modeling. Much research has focused on rainfall data imputation. However, the compatibility ...

    • Integration of Genetic Programming and TABU Search Mechanism for Automatic Detection of Magnetic Resonance Imaging in Cervical Spondylosis 

      Juan, Chun-Jung; Wang, Chen-Shu; Lee, Bo-Yi; Chiang, Shang-Yu; Yeh, Chun-Chang; Cho, Der-Yang; Shen, Wu-Chung (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 09/2021)
      Cervical spondylosis is a kind of degenerative disease which not only occurs in elder patients. The age distribution of patients is unfortunately decreasing gradually. Magnetic Resonance Imaging (MRI) is the best tool to ...

    • Modeling of Performance Creative Evaluation Driven by Multimodal Affective Data 

      Wu, Yufeng; Zhang, Longfei; Ding, Gangyi; Xue, Tong; Zhang, Fuquan (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 09/2021)
      Performance creative evaluation can be achieved through affective data, and the use of affective featuresto evaluate performance creative is a new research trend. This paper proposes a “Performance Creative—Multimodal ...

    • Modified YOLOv4-DenseNet Algorithm for Detection of Ventricular Septal Defects in Ultrasound Images 

      Chen, Shih-Hsin; Wang, Chun-Wei; Tai, I-Hsin; Weng, Ken-Pen; Chen, Yi-Hui; Hsieh, Kai-Sheng (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 09/2021)
      Doctors conventionally analyzed echocardiographic images for diagnosing congenital heart diseases (CHDs). However, this process is laborious and depends on the experience of the doctors. This study investigated the use of ...

    • Optimal QoE Scheduling in MPEG-DASH Video Streaming 

      Chang, Shin-Hung; Tsai, Min-Lun; Lee, Meng-Huang; Ho, Jan-Ming (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 09/2021)
      DASH is a popular technology for video streaming over the Internet. However, the quality of experience (QoE), a measure of humans’ perceived satisfaction of the quality of these streamed videos, is their subjective opinion, ...

    • Pulmonary Nodule Classification in Lung Cancer from 3D Thoracic CT Scans Using fastai and MONAI 

      Kaliyugarasan, Satheshkumar; Lundervold, Arvid; Lundervold, Alexander Selvikvåg (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 09/2021)
      We construct a convolutional neural network to classify pulmonary nodules as malignant or benign in the context of lung cancer. To construct and train our model, we use our novel extension of the fastai deep learning ...