List of Articles Regression

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  1 - A Robust Statistical Color Edge Detection for Noisy Images
  Mina Alibeigi Niloofar Mozafari Zohre Azimifar Mahnaz Mahmoodian
  10.7508/jist.2015.02.003
  Edge detection is a fundamental tool that plays a significant role in image processing, and performance of high-level tasks such as image segmentation and object recognition depends on its efficiency. Therefore, edge detection is one of the well-studied areas in image p More

  Edge detection is a fundamental tool that plays a significant role in image processing, and performance of high-level tasks such as image segmentation and object recognition depends on its efficiency. Therefore, edge detection is one of the well-studied areas in image processing and computer vision. However, it is clear that accurate edge map generation is more difficult when images are corrupted with noise. Moreover, most of edge detection methods have parameters which must be set manually. In recent years different approaches has been used to address these problems. Here we propose a new color edge detector based on a statistical test, which is robust to noise. Also, the parameters of this method will be set automatically based on image content. To show the effectiveness of the proposed method, four state-of-the-art edge detectors are implemented and the results are compared. Experimental results on five of the most well-known edge detection benchmarks show that the proposed method is robust to noise. The performance of our method for lower levels of noise is very comparable to the existing approaches, whose performances highly depend on their parameter tuning stage. However, for higher levels of noise, the observed results significantly highlight the superiority of the proposed method over the existing edge detection methods, both quantitatively and qualitatively. Manuscript profile
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  2 - A Hybrid Approach based on PSO and Boosting Technique for Data Modeling in Sensor Networks
  hadi shakibian Jalaledin Nasiri
  10.52547/jist.19789.10.40.259
  20.1001.1.23221437.2022.10.40.5.1
  An efficient data aggregation approach in wireless sensor networks (WSNs) is to abstract the network data into a model. In this regard, regression modeling has been addressed in many studies recently. If the limited characteristics of the sensor nodes are omitted from c More

  An efficient data aggregation approach in wireless sensor networks (WSNs) is to abstract the network data into a model. In this regard, regression modeling has been addressed in many studies recently. If the limited characteristics of the sensor nodes are omitted from consideration, a common regression technique could be employed after transmitting all the network data from the sensor nodes to the fusion center. However, it is not practical nor efferent. To overcome this issue, several distributed methods have been proposed in WSNs where the regression problem has been formulated as an optimization based data modeling problem. Although they are more energy efficient than the centralized method, the latency and prediction accuracy needs to be improved even further. In this paper, a new approach is proposed based on the particle swarm optimization (PSO) algorithm. Assuming a clustered network, firstly, the PSO algorithm is employed asynchronously to learn the network model of each cluster. In this step, every cluster model is learnt based on the size and data pattern of the cluster. Afterwards, the boosting technique is applied to achieve a better accuracy. The experimental results show that the proposed asynchronous distributed PSO brings up to 48% reduction in energy consumption. Moreover, the boosted model improves the prediction accuracy about 9% on the average. Manuscript profile
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  3 - Comparative Study of 5G Signal Attenuation Estimation Models
  Md Anoarul Islam Manabendra Maiti Judhajit Sanyal Quazi Md Alfred
  10.52547/jist.32544.11.42.84
  20.1001.1.23221437.2023.11.42.4.1
  Wireless networks functioning on 4G and 5G technology offer a plethora of options to users in terms of connectivity and multimedia content. However, such networks are prone to severe signal attenuation and noise in a number of scenarios. Significant research in recent y More

  Wireless networks functioning on 4G and 5G technology offer a plethora of options to users in terms of connectivity and multimedia content. However, such networks are prone to severe signal attenuation and noise in a number of scenarios. Significant research in recent years has consequently focused on establishment of robust and accurate attenuation models to estimate channel noise and subsequent signal loss. The identified challenge therefore is to identify or develop accurate computationally inexpensive models implementable on available hardware for generation of estimates with low error and validate the solutions experimentally. The present work surveys some of the most relevant recent work in this domain, with added emphasis on rain attenuation models and machine learning based approaches, and offers a perspective on the establishment of a suitable dynamic signal attenuation model for high-speed wireless communication in outdoor as well as indoor environments, presenting the performance evaluation of an autoregression-based machine learning model. Multiple versions of the model are compared on the basis of root mean square error (RMSE) for different orders of regression polynomials to find the best-fit solution. The accuracy of the technique proposed in the paper is then compared in terms of RMSE to corresponding moderate and high complexity machine learning techniques implementing adaptive spline regression and artificial neural networks respectively. The proposed method is found to be quite accurate with low complexity, allowing the method to be practically applicable in multiple scenarios. Manuscript profile
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  4 - Breast Cancer Classification Approaches - A Comparative Analysis
  Mohan Kumar Sunil Kumar Khatri Masoud Mohammadian
  10.52547/jist.33395.11.41.1
  20.1001.1.23221437.2023.11.41.8.3
  Cancer of the breast is a difficult disease to treat since it weakens the patient's immune system. Particular interest has lately been shown in the identification of particular immune signals for a variety of malignancies in this regard. In recent years, several methods More

  Cancer of the breast is a difficult disease to treat since it weakens the patient's immune system. Particular interest has lately been shown in the identification of particular immune signals for a variety of malignancies in this regard. In recent years, several methods for predicting cancer based on proteomic datasets and peptides have been published. The cells turns into cancerous cells because of various reasons and get spread very quickly while detrimental to normal cells. In this regard, identifying specific immunity signs for a range of cancers has recently gained a lot of interest. Accurately categorizing and compartmentalizing the breast cancer subtype is a vital job. Computerized systems built on artificial intelligence can substantially save time and reduce inaccuracy. Several strategies for predicting cancer utilizing proteomic datasets and peptides have been reported in the literature in recent years.It is critical to classify and categorize breast cancer treatments correctly. It's possible to save time while simultaneously minimizing the likelihood of mistakes using machine learning and artificial intelligence approaches. Using the Wisconsin Breast Cancer Diagnostic dataset, this study evaluates the performance of various classification methods, including SVC, ETC, KNN, LR, and RF (random forest). Breast cancer can be detected and diagnosed using a variety of measurements of data (which are discussed in detail in the article) (WBCD). The goal is to determine how well each algorithm performs in terms of precision, recall, and accuracy. The variation of each classification threshold has been tested on various algorithms and SVM turned out to be very promising. Manuscript profile