International Journal of Intelligent Systems Technologies and Applications
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International Journal of Intelligent Systems Technologies and Applications (5 papers in press)
Rotation vector and directional cosine matrix in problems of satellite attitude control by Y.V. Kim Abstract: For many years, three rotation angles of moving vehicles: roll, pitch, and yaw have been used for attitude determination and control. However, in the last years due to appearance of new airspace applications such as strapdown inertial navigation systems (INS) and spacecrafts new quaternion (Q)-based methods appeared. Conventional and new modern methods have some features that can attract or repel developers. Nevertheless, some new techniques of classical mechanics can be tried to use them for vehicle attitude determination and control purposes with the expectation to get more effective results. The article presents a rotation vector (RV) and proposes a way of using it for satellite attitude control. This method is compared with conventional method of attitude control using three rotation angles. The ratio is shown between RV control and modern method of satellite attitude control using quaternion. Analytical and simulation results are presented. Keywords: rigid body dynamics; attitude determination and control; Euler angles; quaternion; directional cosine matrix; DCM; rotation angles; rotation vector; Euler’s axis. DOI: 10.1504/IJASSE.2021.10036566
Thresholding based Decision Map for CT-MRI fusion in wavelet domain by Vijayarajan Rajangam, Sangeetha N, Kethepalli Mallikarjuna Abstract: Medical image fusion methods combine the salient details of medical images from the same modality or multiple modalities to reduce the redundancy in clinical pathology. This paper proposes a threshold-based decision map for the discrete wavelet coefficients of the source medical images. The fusion rules are implemented for both detail and approximate coefficients based on the decision map and image fusion is carried out in the wavelet domain. The performance of the proposed method is evaluated using non-reference metrics such as peak signal to noise ratio, quality index, fusion factor, and figure of merit. The objective metrics substantiate the performance of the fusion algorithm for the same modality and multimodality image fusion. Keywords: DWT; Otsu’s method; threshold; multimodal fusion; figure of merit.
A Review on Lung Carcinoma Segmentation and Classification Using CT Image based on Deep Learning by Poonkodi Shanmugam, Kanchana M Abstract: Lung carcinoma was the first leading cause of death when compared to all other cancer. At an early-stage detection of a lung nodule, is an important step to prevent death and to increase the survival rate of patients with lung cancer. Various types of radiology techniques are used to acquire the image of lung nodules. Among the radiology techniques, Computed Tomography (CT) is an effective method for diagnosing lung carcinoma at its early stage, thus reducing the mortality rate. Radiologists have faced a challenging task, that is, to calculate the accuracy of images due to the exponential growth of CT images. Nowadays, various computer vision techniques are available for the prediction and detection of carcinoma. Deep Learning (DL) model provides a high level of services to the healthcare sector. Deep Learning techniques are becoming more efficient to detect and predict diseases at an early stage. This study reviews current work on the segmentation and classification of lung nodules in CT using deep learning models. Keywords: CT; PET-CT; Lung Carcinoma; Deep Learning; Classification; Segmentation.
A Feature-level Attention based Deep Neural Network Model for Sentence Embedding by Amal Bouraoui, Salma Jamoussi, Abdelmajid Ben Hamadou Abstract: Building a model to represent the semantic of a sentence is crucial for going beyond a sequence of words to a more abstract yet relevant representation. The relevance of such models is ubiquitous in various natural language processing tasks. Following this context, we capitalise on deep learning for proposing a new model based attention mechanism. Our contribution aims at embedding the sentence meaning in an unsupervised iterative way. The word and the sentence embeddings therefore influence each other. Our model is inspired from the recursive auto-encoders. We coupled our model with a novel attention mechanism computed at the feature-level. This mechanism aims to increase representation power by focusing on important features of words within a sentence to refine the constructed meaning representation of this sentence. To highlight our newly proposed contribution, we carried out an exhaustive experimental study for evaluating the quality of the learned representations on semantic similarity task. The obtained results demonstrate the faithfulness of our learned semantic representations. Keywords: Sentence embedding; Semantics; Recursive auto-encoders; Attention mechanism; Semantic similarity; Feature-level attention.
Performance Analysis towards GUI based Vehicle Detection and Tracking using YOLOv3 and SORT Algorithm by N. Kavitha, D.N. Chandrappa Abstract: This study presents a pragmatic approach towards developing and analyzing a GUI-based system performance to detect and count tracked multi-type vehicles in mixed traffic conditions using the improved You Only Look Once v3 (YOLOv3) model. It addresses the issue related to accurately localizing smaller vehicles in an occluded scenario and environmental conditions. In the training phase of the proposed work stochastic gradient descent (SGD) optimizes the network. Further, to detect small vehicles four different scales feature mapping are performed to extract more fine-grained features for accurate detection, followed by concatenating upsampled layers with lower layers to improve the detection performance of low-resolution images. The Intersection over Union (IoU) approach detects every vehicle in the subsequent frame by assigning a unique ID to classify detected vehicles into five specific classes bus, truck, car, motorbike, and bicycle. Further, the SORT algorithm tracks and counts the detected vehicles. Experimental result on the Common Objects in Context (COCO) dataset shows an improvement in the mean average precision (mAP) by 11% compared to the existing YOLOv3 technique. Keywords: Keywords: YOLOv3 model; COCO dataset; SORT algorithm; vehicle classification; vehicle counting.