1 Khatam Al-Nabieen University 37 Special Software Project Management Using Machine Learning Technique — A Review Haidari Habibullah 1 6 2024 2 2 3 69 24 03 2026 19 06 2024 Project management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learning in software project management. Besides, this paper presents an extensive literature analysis of (1) machine learning, (2) software project management, and (3) techniques from three main libraries, Web Science, Science Directs, and IEEE Explore. One-hundred and eleven papers are divided into four categories in these three repositories. The first category contains research and survey papers on software project management. The second category includes papers that are based on machine-learning methods and strategies utilized on projects; the third category encompasses studies on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in the production, and the promotion of machine-learning project prediction. Our contribution also offers a more comprehensive perspective and a context that would be important for potential work in project risk management. In conclusion, we have shown that project risk assessment by machine learning is more successful in minimizing the loss of the project, thereby increasing the likelihood of the project success, providing an alternative way to efficiently reduce the project failure probabilities, and increasing the output ratio for growth, and it also facilitates analysis on software fault prediction based on accuracy.
38 Special Applications of artificial neural network in geotechnical engineering - a review study Mobalegh Morteza c c Master's degree in geotechnical engineering, Shahid Rajaee University, Tehran, Iran 1 6 2024 2 2 71 86 25 03 2026 19 06 2024 Over the past years, the use of artificial neural networks has increased in many engineering fields. In particular, artificial neural networks have been applied to many geotechnical engineering problems and remarkable results have been obtained. The review of the background of the subject shows that artificial neural networks have been successfully used in predicting pile bearing capacity, soil retaining structures, settlement of structures, stability of slopes, design of tunnels and underground openings, liquefaction, soil compaction, swelling and classification of soils. The purpose of this article is to provide an overview of some applications of artificial neural network to solve some geotechnical engineering problems; Also, the strengths and weaknesses of this method will be examined in comparison with other modeling solutions. 39 Special Human identification and tracking from images Jafari Mohammad Nazim d Tahiri Mohammad Rahim e d Kateb university, Kabul, Afghanistan e Kateb university, Kabul, Afghanistan 1 6 2024 2 2 87 98 25 03 2026 19 06 2024 The method used to identify and track humans in this research is based on edge detection. After subtracting the foreground from the background, using the edging method, the edges of the foreground areas are obtained; Then, the identification algorithm identifies people from top to bottom and removes them from the image. Finally, by using the Gaussian probability distribution, people are tracked according to their position in the previous form. This algorithm works well in crowded places and does not suffer from overlaps. Considering that stereo images are used in this study, the distance of people to the camera can also be determined. 40 Special Investigation of target tracking methods based on particle filter Khaleghi Najibullah Qanei Yakhdan Hasan 1 6 2024 2 2 99 133 25 03 2026 19 06 2024 Target tracking requires simultaneous estimation of its position, speed and acceleration. There are different methods with different algorithms for target tracking; Particle filter is a new method to obtain posterior probability distribution function based on Bayesian theory. The particle filter algorithm is based on chain Monte Carlo methods, in which the particle representation of the probability density is used to estimate arbitrary distribution parameters. Target tracking is the estimation of the posterior density function in each sweep for the target in the observed environment. Some things make this difficult, which include: the lack of full disclosure of the target, the existence of false targets, uncertainty in how to allocate data to the existing target, and non-linear equations and non-Gaussian noises - which makes it possible to use the Kalman filter and its families (extended and intangible Kalman ) limits.- Recently, the efficiency of Monte Carlo methods and particle filters on top of them in solving the mentioned cases has been proven. Monte Carlo methods of multi-objective tracking have replaced classical methods; But they still have room for improvement. In the conventional methods of tracking aerial targets, the distance to the target and the angle to the target side, which are a nonlinear function of the system states, are measured; But they have noise, so it is necessary to use estimation and filtering methods. The generalized Kalman filter has a good performance for dealing with nonlinear systems and Gaussian noises; But in practical implementation, we face non-Gaussian noises (Glint) that particle filters have good performance. Particle filter performance, despite many advantages, also has disadvantages; Because with the initial selection of a large number of particles, no particle may be placed near the correct state; This weakness is known as the problem of deterioration. Re-sampling is used to reduce degradation in a standard particle filter. Re-sampling, while being vital, causes another phenomenon called poverty of samples, where the diversity among particles is lost and in the worst case, all particles fall to a point in the state space. Researchers have proposed different versions of the particle filter (auxiliary, regularized, and traceless) to improve resampling. 41 Special A New CAC Method for IEEE802.11 Access Point based on Session Initiation Protocol Frotan Mohammad Dawood h Omid Mohammad Shah i h Computer Science Faculty, Kateb University, Kabul, Afghanistan i Computer Science Faculty, Kateb University, Kabul, Afghanistan 1 6 2024 2 2 135 148 25 03 2026 19 06 2024 Call admission control CAC in wireless networks has been receiving a great deal of attention during the last two decades due to the growing popularity of wireless communications. CAC method plays central role in QoS provisioning in terms of the signal quality, call blocking and dropping probabilities, packet delay, jitter, loss rate and bandwidth. Due to the limited capacity of the network, making more calls through the network which increases the transmission delay and packet delay and cause a decline in service quality. So, in this project considering the network capacity, Call Admission Control prevents the new Calls to Provide the appropriate service. It is difficult to detect the access point capacity, so we use a simple CAC method in this thesis, according to ACK Failure rate in Access Point. ACK Failure rate is equal to failed ACK in a unit time which indicates network congestion. In the proposed project the module is on OpenSip software and it receives the values of errors from access point and makes decision for SIP server whether to accept or reject the calls. The results show that the proposed method of call admission control provides appropriate quality to users. 42 Special Seismic protection of structures by rubber-soil mixture as geotechnical seismic isolation Fasihi Hadi Ahmadi Rajab Ali Alizada Alireza 1 6 2024 2 2 149 165 25 03 2026 19 06 2024 In this article, geotechnical seismic isolation is studied with the help of finite difference numerical modeling. This work is done by placing a layer of rubber-soil mixture with more damping properties than normal soils under the foundation of the structure. The results of complete dynamic analyze showed that this method can be an effective solution to reduce the amount of acceleration transferred to the structure. The results of complete dynamic analyze showed that this method can be an effective solution to reduce the amplification transferred to the structure. The analyzes showed that the higher the thickness of the damping layer, the higher the deamplification will be. The deamplification value for two different earthquakes used in this research was observed to be almost the same. Also, the analysis of the direct soil structure interaction showed that in the maximum range of earthquake input acceleration from 0.1g to 0.6g, the use of a damping layer leads to a reduction of 1.4 to 1.9 times the amount of drift on the floor.