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• Human-Centered A.I. and Security Primitives

  Alex Mathew

  32-35

  
  Article ID: 2534
  1268  (Abstract) 151  (Download)
  

  Abstract: The paper reviews how human-centered artificial intelligence and security primitive have influenced life in the modern world and how it’s useful in the future. Human-centered A.I. has enhanced our capabilities by the way of intelligence, human informed technology. It has created a technology that has made machines and computer intelligently carry their function. The... More

  2020-11-26

• Intrusion Detection through DCSYS Propagation Compared to Auto-encoders

  Fatima Isiaka, Zainab Adamu

  42-49

  
  Article ID: 3578
  501  (Abstract) 144  (Download)
  

  Abstract: In network settings, one of the major disadvantages that threaten the network protocols is the insecurity. In most cases, unscrupulous people or bad actors can access information through unsecured connections by planting software or what we call malicious software otherwise anomalies. The presence of anomalies is also one of the disadvantages, internet users are... More

  2021-08-27

• High-Resolution Traffic Flow Prediction Model Based on Deep Learning

  Zhihong Yao, Yibing Wang

  1-9

  
  Article ID: 381
  640  (Abstract) 159  (Download)
  

  Abstract: The traditional platoon dispersion model is based on the hypothesis of probability distribution, and the time resolution of the existing traffic flow prediction model is too big to be applied to the adaptive signal timing optimization. Based on the view of the platoon dispersion model, the relationship between vehicle arrival at downstream intersection and vehicle... More

  2019-01-22

• Bidirectional Recurrent Nets for ECG Signal Compression

  Eman AL-Saidi, Khalil El Hindi

  15-25

  
  Article ID: 5204
  412  (Abstract) 281  (Download)
  

  Abstract: Electrocardiogram (ECG) is a commonly used tool in biological diagnosis of heart diseases. ECG allows the representation of electrical signals which cause heart muscles to contract and relax. Recently, accurate deep learning methods have been developed to overcome manual diagnosis in terms of time and effort. However, most of current automatic medical diagnosis use... More

  2022-12-05

• Improving U-Net Performance for Tumor Segmentation Using Attention Mechanisms

  Zetai Wu, Weifa Liu, Jing Chang

  66-72

  
  Article ID: 7271
  562  (Abstract) 121  (Download)
  

  Abstract: U-Net is a widely recognized neural network model for medical image segmentation, renowned for its efficiency in extracting features from both current and past input data. However, traditional U-Net models exhibit limitations in extracting edge features, particularly in medical CT images characterized by complex gray distributions and close pixel intervals. This leads to suboptimal performance,... More

  2024-09-27

• Optimization of Mobile Robot Delivery System Based on Deep Learning

  Xiaoyang Chen, Yunxiang Gan, Shuguang Xiong

  51-65

  
  Article ID: 7197
  344  (Abstract) 100  (Download)
  

  Abstract: In modern logistics and delivery systems, mobile robot delivery systems have garnered significant attention due to their efficiency and flexibility. However, existing robot delivery systems still face numerous challenges in complex and dynamically changing environments. For instance, traditional algorithms exhibit low efficiency when processing high-dimensional and unstructured data, making it difficult to adapt to real-time... More

  2024-09-27