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International Journal of Software Science and Computational Intelligence
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ISSN (Print) 1942-9045 - ISSN (Online) 1942-9037
Published by IGI Global

[146 journals]

A Smart Helmet Framework Based on Visual-Inertial SLAM and Multi-Sensor
Fusion to Improve Situational Awareness and Reduce Hazards in
Mountaineering
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  Authors: Tan; Charles Shi
  Pages: 1 - 19
  Abstract: Sensitivity to surrounding circumstances is essential for the safety of mountain scrambling. In this paper, the authors present a smart helmet prototype equipped with visual SLAM (simultaneous localization and mapping) and barometer multi-sensor fusion (MSF), IMU (inertial measurement unit), omnidirectional camera, and global navigation satellite system (GNSS). They equipped the helmet framework with SLAM to produce 3D semi-dense pointcloud environment maps, which are then discretized into grids. Then, the novel danger metrics they proposed were calculated for each grid based on surface normal analysis. The A* algorithm was applied to generate safe and reliable paths based on minimizing the danger score. This proposed helmet system demonstrated robust performance in mapping mountain environments and planning safe, efficient traversal paths for climbers navigating treacherous mountain landscapes.
  Keywords: Artificial Intelligence; Computer Science & IT; Computational Intelligence
  Citation: International Journal of Software Science and Computational Intelligence (IJSSCI), Volume: 15, Issue: 1 (2023) Pages: 1-19
  PubDate: 2023-01-01T05:00:00Z
  DOI: 10.4018/IJSSCI.333628
  Issue No: Vol. 15, No. 1 (2023)
Machine Learning for Accurate Software Development Cost Estimation in
Economically and Technically Limited Environments
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  Authors: Alauthman; Mohammad, al-Qerem, Ahmad, Alangari, Someah, Ali, Ali Mohd, Nabo, Ahmad, Aldweesh, Amjad, Jebreen, Issam, Almoman, Ammar, Gupta, Brij B.
  Pages: 1 - 24
  Abstract: Cost estimation for software development is crucial for project planning and management. Several regression models have been developed to predict software development costs, using historical datasets of previous projects. Accurate cost estimation in software development is heavily influenced by the relevance and quality of the cost estimation dataset and its suitability to the software development environment. The currently available cost estimation datasets are limited to North American and European environments, leaving a gap in the representation of other economically and technically constrained software industries. In this article, the authors evaluate the performance of regression models using the SEERA dataset, which highly represents these constrained environments. This study provides insights into selecting regression models for cost estimation in software development. It highlights the importance of using appropriate models based on the specific software development model and dataset used in the estimation process. In the performance evaluations of eight regression models, including elastic net, lasso regression, linear regression, neural network, RANSACRegressor, random forest, ride regression, and SVM, for cost estimation in different software models, along with correlation coefficients and accuracy indicators, were reported. The results showed that SVM and random forest indicated superior performance. However, the elastic net, lasso regression, linear regression, neural network, and RANSACRegressor models also demonstrated exemplary performance in cost estimation.
  Keywords: Artificial Intelligence; Computer Science & IT; Computational Intelligence
  Citation: International Journal of Software Science and Computational Intelligence (IJSSCI), Volume: 15, Issue: 1 (2023) Pages: 1-24
  PubDate: 2023-01-01T05:00:00Z
  DOI: 10.4018/IJSSCI.331753
  Issue No: Vol. 15, No. 1 (2023)
Artificial Intelligence in Tongue Image Recognition
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  Authors: Chu; Hongli, Ji, Yanhong, Zhu, Dingju, Ye, Zhanhao, Tan, Jianbin, Hou, Xianping, Lin, Yujie
  Pages: 1 - 25
  Abstract: Tongue image recognition is a traditional Chinese medicine diagnosis method, which uses the shape, color, and texture of the tongue to judge the health of the human body. With the rapid development of artificial intelligence technology, the application of artificial intelligence in the field of tongue recognition has been widely considered. Based on the intelligent analysis of tongue diagnosis in traditional Chinese medicine, this paper reviews the application progress of artificial intelligence in tongue image recognition in recent years and analyzes its potential and challenges in this field. Firstly, this paper introduces three steps of tongue image recognition, including tongue image acquisition, tongue image preprocessing, and tongue image feature analysis. The application of traditional methods and artificial intelligence methods in the whole process of tongue image recognition is reviewed, especially the tongue body segmentation, and the advantages and disadvantages of convolutional neural networks are analyzed and compared. Artificial intelligence can use technologies such as deep learning and computer vision to automatically analyze and extract features from tongue images. By constructing a tongue image recognition model, tongue shape, color, texture, and other features can be accurately recognized and quantitatively analyzed. Finally, this paper summarizes the problems existing in artificial intelligence in tongue image recognition and looks forward to the future developmental direction of this field. It can promote the modernization of TCM diagnostic methods, achieve early disease screening and prevention, personalized medicine and treatment optimization, and support medical research and knowledge accumulation. However, there is still a need for further validation and practice, with a focus on patient privacy and data security.
  Keywords: Artificial Intelligence; Computer Science & IT; Computational Intelligence
  Citation: International Journal of Software Science and Computational Intelligence (IJSSCI), Volume: 15, Issue: 1 (2023) Pages: 1-25
  PubDate: 2023-01-01T05:00:00Z
  DOI: 10.4018/IJSSCI.328771
  Issue No: Vol. 15, No. 1 (2023)
Knowledge Discovery of Hospital Medical Technology Based on Partial
Ordered Structure Diagrams
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  Authors: Zhu; Dingju, Tan, Jianbin, Luo, Guangbo, Gu, Haoxiang, Ye, Zhanhao, Deng, Renfeng, He, Keyi, Yung, KaiLeung, Ip, Andrew W. H.
  Pages: 1 - 16
  Abstract: So far, no research has used the partial order algorithm for the mining of hospital medical technology. This paper proposed a novel knowledge discovery method of hospital medical technology based on partial ordered structure diagrams, constructed attribute partial ordered structure diagram and object partial ordered structure diagram for the formal context constructed by hospital set and medical technology set, and finally analyzed them using the knowledge discovery method. The experiments show that the partial ordered structure diagram can effectively visualize the structural relationships between hospital sets and medical technology sets, and the distribution characteristics of medical technology sets in hospital sets and the rules of medical technology sets owned by hospital sets can be obtained based on the node, branch, and group structure relationships of the partial ordered structure diagram.
  Keywords: Artificial Intelligence; Computer Science & IT; Computational Intelligence
  Citation: International Journal of Software Science and Computational Intelligence (IJSSCI), Volume: 15, Issue: 1 (2023) Pages: 1-16
  PubDate: 2023-01-01T05:00:00Z
  DOI: 10.4018/IJSSCI.320499
  Issue No: Vol. 15, No. 1 (2023)
Artificial Intelligence Techniques to improve cognitive traits of Down
Syndrome Individuals
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  Authors: Leghari; Irfan M., Ali, Syed Asif
  Pages: 1 - 11
  Abstract: Improving the learning process requires to improve the cognitive traits of individuals with low mental skills. The artificial intelligence (AI) has been used to support the different individuals with impairments. People with Down syndrome fall in intellectual impairment. Different AI techniques of convolution neural network, artificial neural network and decision tree are widely applied to address the different cognitive traits. We have summarized the artificial intelligence review utilized for such individuals. The aim of this research article is investigate the usability of computational intelligence for addressing the deficits of cognitive skills and other traits. The individuals with cognitive impairment survive with limited mental challenge, therefore, they hardly perform daily life assignments. The individuals with down syndrome face mild to severe cognitive challenges that affects to their daily life activities, education and performing employment. So, they can have reduced the social and economic burden of their family and to make their live productive. Achieving these goals requires improvement in their cognitive challenge. A survey of (N = 50) of the individuals of Down syndrome has been carried out with the support of team of psychologists and teachers of homogeneous education system.
  Keywords: Artificial Intelligence; Computer Science & IT; Computational Intelligence
  Citation: International Journal of Software Science and Computational Intelligence (IJSSCI), Volume: 15, Issue: 1 (2023) Pages: 1-11
  PubDate: 2023-01-01T05:00:00Z
  DOI: 10.4018/IJSSCI.318677
  Issue No: Vol. 15, No. 1 (2023)
TA-WHI
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  Authors: Bagla; Piyush, Kumar, Kuldeep
  Pages: 1 - 14
  Abstract: The healthcare data available on social media has exploded in recent years. The cures and treatments suggested by non-medical experts can lead to more damage than expected. Assuring the credibility of the information conveyed is an enormous challenge. This study aims to categorize the credibility of online health information into multiple classes. This paper proposes a model named Text Analysis of Web-based Health Information (TA-WHI), based on an algorithm designed for this. It categorizes health-related social media feeds into five categories: sufficient, fabricated, meaningful, advertisement, and misleading. The authors have created their own labeled dataset for this model. For data cleaning, they have designed a dictionary having nouns, adverbs, adjectives, negative words, positive words, and medical terms named MeDF. Using polarity and conditional procedure, the data is ranked and classified into multiple classes. The authors evaluate the performance of the model using deep-learning classifiers such as CNN, LSTM, and CatBoost. The suggested model has attained an accuracy of 98% with CatBoost.
  Keywords: Artificial Intelligence; Computer Science & IT; Computational Intelligence
  Citation: International Journal of Software Science and Computational Intelligence (IJSSCI), Volume: 15, Issue: 1 (2023) Pages: 1-14
  PubDate: 2023-01-01T05:00:00Z
  DOI: 10.4018/IJSSCI.316972
  Issue No: Vol. 15, No. 1 (2023)