List of Articles Principal component analysis

• Open Access Article
  • Abstract Page
  • Full-Text
  
  1 - Recognition of Attention Deficit/Hyperactivity Disorder (ADHD) Based on Electroencephalographic Signals Using Convolutional Neural Networks (CNNs)
  Sara Motamed Elham Askari
  10.52547/jist.16399.10.39.222
  Impulsive / hyperactive disorder is a neuro-developmental disorder that usually occurs in childhood, and in most cases parents find that the child is more active than usual and have problems such as lack of attention and concentration control. Because this problem might More

  Impulsive / hyperactive disorder is a neuro-developmental disorder that usually occurs in childhood, and in most cases parents find that the child is more active than usual and have problems such as lack of attention and concentration control. Because this problem might interfere with your own learning, work, and communication with others, it could be controlled by early diagnosis and treatment. Because the automatic recognition and classification of electroencephalography (EEG) signals is challenging due to the large variation in time features and signal frequency, the present study attempts to provide an efficient method for diagnosing hyperactive patients. The proposed method is that first, the recorded brain signals of hyperactive subjects are read from the input and in order to the signals to be converted from time range to frequency range, Fast Fourier Transform (FFT) is used. Also, to select an effective feature to check hyperactive subjects from healthy ones, the peak frequency (PF) is applied. Then, to select the features, principal component analysis and without principal component analysis will be used. In the final step, convolutional neural networks (CNNs) will be utilized to calculate the recognition rate of individuals with hyperactivity. For model efficiency, this model is compared to the models of K- nearest neighbors (KNN), and multilayer perceptron (MLP). The results show that the best method is to use feature selection by principal component analysis and classification of CNNs and the recognition rate of individuals with ADHD from healthy ones is equal to 91%. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - Integration of Geological, Geochemical, Alteration and Remote Sensing Data to Introduce the Mineralization Potentials in the Sarbisheh area, South Khorasan
  S. Modabberi M. Azarifar S. Shamsoddin Ahmadi D. Raeisi
  Sarbisheh area is located in the west of Sarbisheh and southeast of Birjand, South Khorasan province. This area is located in the Birjand ophiolite melange zone and is a part of the northern part of the Iranshahr-Birjand metallogenic belt. The lithological units in this More

  Sarbisheh area is located in the west of Sarbisheh and southeast of Birjand, South Khorasan province. This area is located in the Birjand ophiolite melange zone and is a part of the northern part of the Iranshahr-Birjand metallogenic belt. The lithological units in this area include ophiolite melange, flysch facies sediments, pyroclastic rocks and Quaternary sediments. Geochemical studies of stream sediments and identification of geochemical indicators of mineral resources in the region were performed using the results of geochemical analysis and principal component analysis. Remote sensing studies were performed on the ASTER and Landsat satellite images using color composite, selective principal component analysis (crusta) on the Landsat 8 satellite imagery to identify the alteration zones. The lineaments of the region were drawn using the high-pass filter method of the ASTER satellite image and the Google image. Finally, by creating layers of geological units, geochemical data, alteration and lineament and integrating them with fuzzy method, areas with potential mineralization of nickel, chromium, cobalt, copper, lead, zinc and magnesite were identified. Manuscript profile