Current Issue : October-December
Volume : 2021
Issue Number : 4
Articles : 5 Articles
'ere is an enormous budget and financial plan in software development projects, and it is required that they take a huge
investment to carry on. When looked at, the costs depend on the global substantial information about software development: in
1985, $150 billion; in 2010, $2 trillion; in 2015, $5 trillion; and in 2020, over $7 trillion. Additionally, on the first new days of 2021, a
day-by-day Apple Store’s quantity has been approximately $500 million. In spite of the expenditures and the margins that are
dramatically expanding and increasing each year, the phase of software development accomplishment is not high enough. In light
of the “CHAOS” report arranged in 2015, just 17% of the software projects were finished in an opportune way, in the allotted
financial plan, and as per the necessities. However, 53% of the software projects were finished in the long run or potentially over a
spending plan as well as without satisfying the prerequisites precisely. In addition, software development projects were not
completed and were dropped out as well in the ratio of 30%. Also, the “CHAOS” report published in 2020 has figured out that only
33% of the software projects were completed successfully all over the world. In order to cope with these unsuccessful and failure
results, an effective method for software risk assessment and management has to be specified, designated, and applied. In this way,
before causing trouble that has the power of preventing successful accomplishment of software development projects, software
risks are able to be noticed and distinguished on time. In this study, a new and original rule set, which could be used and carried
out effectively in software risk assessment and management, has been designed and developed based on the implementation of
fuzzy approached technique integrated with machine learning algorithm—Adaptive Neuro-Fuzzy Inference System (ANFIS). By
this approach and technique, machines (computers) are able to create several software risk rules not to be seen, not to be
recognized, and not to be told by human beings. In addition, this fuzzy inference approach aims to decrease risks in the software
development process in order to increase the success rate of the software projects. Also, the experimental results of this approach
show that rule-based software risk assessment and management method has a valid and accurate model with a high accuracy rate
and low average testing error....
Motivation. +e worldwide incidence and mortality rates of melanoma are on the rise recently. Melanoma may develop from
benign lesions like skin moles. Easy-to-use mole detection software will help find the malignant skin lesions at the early stage.
Results. +is study developed mole detection and segmentation software DiaMole using mobile phone images. DiaMole utilized
multiple deep learning algorithms for the object detection problem and mole segmentation problem. An object detection algorithm
generated a rectangle tightly surrounding a mole in the mobile phone image. Moreover, the segmentation algorithm
detected the precise boundary of that mole. +ree deep learning algorithms were evaluated for their object detection performance.
+e popular performance metric mean average precision (mAP) was used to evaluate the algorithms. Among the utilized algorithms,
the Faster R-CNN could achieve the best mAP� 0.835, and the integrated algorithm could achieve the mAP� 0.4228.
Although the integrated algorithm could not achieve the best mAP, it can avoid the missing of detecting the moles. A popular Unet
model was utilized to find the precise mole boundary. Clinical users may annotate the detected moles based on their experiences.
Conclusions. DiaMole is user-friendly software for researchers focusing on skin lesions. DiaMole may automatically detect and
segment the moles from the mobile phone skin images. +e users may also annotate each candidate mole according to their own
experiences. +e automatically calculated mole image masks and the annotations may be saved for further investigations....
Car-sharing economy has caused new driving safety and usability problems, which have not been well studied. 'is study aims at
analyzing the effects of users age and the user experience (UX) of the car-sharing software (e.g., DiDi travel app) on overall
usability and the level of distraction for drivers. To this end, 48 experienced Chinese drivers were recruited to perform various
tasks with the car-sharing software using a driving simulator. 'e variables of driving safety and usability were analyzed by twoway
analysis of variance (ANOVA) and independent sample Kruskal–Wallis nonparametric test. As expected, it was found that
car-sharing software has a significant negative impact on driving distraction and usability. 'e overall performance of young
drivers is better than that of the elderly, but it seems that young drivers are more likely to be led to errors by car-sharing software.
In most aspects, experienced drivers perform better than inexperienced drivers and have a better in-depth understanding of carsharing
software weaknesses. However, inexperienced drivers performed better regarding braking time and interaction time.
Although young inexperienced drivers performed worst in driving safety, they exhibited the lowest cognitive load and the highest
interaction efficiency. 'e experience of using car-sharing software may improve driver’s ability to deal with driving distractions.
'e above conclusions provide theoretical support for optimizing the UX of car-sharing software and some references for driver’s
screening and training....
Software project development is very crucial, and measuring the exact cost and effort of development is becoming tedious and
challenging. Organizations are trying to wind up their project of software development within the agreed budget and schedule
successfully. Traditional practices are inadequate to achieve the current needs of the software industry. Underestimation and
overestimation of software development effort lead to financial implications in the form of resources, cost of staffing, and budget of
developing the software project. Soft computing (SC) approaches and tools deliver an addition of techniques for anticipating
resistance to the deception, defect, incomplete truth for traceability and ambiguity, low arrangement cost, and strength. A large
amount of SC approaches is prevailing in the literature to accomplish way-out to difficulties precisely, practically, and speedily.
.e approaches of SC can give better prediction, high performance, and dynamic behavior. SC deals with computational intelligence
which integrates the concept of agent paradigm and SC. .e proposed study presents a systematic literature review
(SLR) of the approaches, tools, and techniques of SC used in the literature. .e study presented a comprehensive review by
searching the defined keywords in the popular libraries, filtered the paper, and obtained most relevant papers. After the selection
of the papers, the quality assessment process of the included papers has been done in order to determine the relevancy of the
papers. .e study will help researchers in the area of research to devise novel ideas and solutions to overcome the existing issue on
the basis of this study as evidence of the literature....
Stereology is the tridimensional interpretation of bidimensional sections of a structure,
widely used in fields such as mineralogy, medicine, and biology. This paper proposes a general
software to do stereological analysis, called STERapp, with a friendly graphical interface to enable
expert supervision. It includes a module to estimate fish fecundity (number of mature oocytes in
the ovary), which has been used by experts in fish biology in two Spanish marine research centers
since 2020 to estimate the fecundity of five fish species with different reproductive strategies and
oocytes characteristics. This module encloses advanced computer vision and machine learning
techniques to automatically recognize and classify the cells in histological images of fish gonads.
The automatic recognition algorithm achieved a sensitivity of 55.6%, a specificity of 64.8%, and an
average precision of 43.1%. The accuracies achieved for oocyte classification were 84.5% for the
maturity stages and 78.5% for the classification regarding presence/absence of the nucleus. This
facilitates the analysis and saves experts’ time. Hence, the SUS questionnaire reported a mean score
of 81.9, which means that the system was perceived from good to excellent to develop stereological
analysis for the estimation of fish fecundity....
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