This research comprises experiments with a deep learning framework for fully automating the skull stripping from brain magnetic resonance (MR) images. Conventional techniques for segmentation have progressed to the extent of Convolutional Neural Networks (CNN). We proposed and experimented with a contemporary variant of the deep learning framework based on mask region convolutional neural network (Mask–RCNN) for all anatomical orientations of brain MR images. We trained the system from scratch to build a model for classification, detection, and segmentation. It is validated by images taken from three different datasets: BrainWeb; NAMIC, and a local hospital. We opted for purposive sampling to select 2000 images of T1 modality from data volumes followed by a multi-stage random sampling technique to segregate the dataset into three batches for training (75%), validation (15%), and testing (10%) respectively. We utilized a robust backbone architecture, namely ResNet–101 and Functional Pyramid Network (FPN), to achieve optimal performance with higher accuracy. We subjected the same data to two traditional methods, namely Brain Extraction Tools (BET) and Brain Surface Extraction (BSE), to compare their performance results. Our proposed method had higher mean average precision (mAP) = 93% and content validity index (CVI) = 0.95%, which were better than comparable methods. We contributed by training Mask–RCNN from scratch for generating reusable learning weights known as transfer learning. We contributed to methodological novelty by applying a pragmatic research lens, and used a mixed method triangulation technique to validate results on all anatomical modalities of brain MR images. Our proposed method improved the accuracy and precision of skull stripping by fully automating it and reducing its processing time and operational cost and reliance on technicians. This research study has also provided grounds for extending the work to the scale of explainable artificial intelligence (XAI).

COVID-19 is a transferable disease inherited from the SARS-CoV-2 virus. A total of 594 million people have been infected, and 6.4 million human beings have died due to COVID-19. The fastest way to diagnose the disease is by radiography. Deep learning has been the most popular technique for image classification during the last decade. This paper aims to examine the contributions of machine learning for the detection of COVID-19 using Deep Learning and explores the overall application of convolutional neural networks of some famous state-of-the-art deep learning pre-trained models. In this research, our objective is to explore the various image classification strategies for CXIs and the application of deep learning models for optimization and feature selection. The study presented in this article shows that the accuracy of deep learning models when detecting COVID-19 on the basis of chest X-ray images ranges from 93 percent to above 99 percent.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairments in social interaction, communication, and repetitive behaviors. Electroencephalography (EEG) has gained prominence as a reliable tool for ASD diagnosis, capturing critical behavioral patterns. Researchers have applied various Machine Learning (ML) techniques to enhance ASD detection, achieving notable accuracy. Studies using feature selection with ML classifiers have reported up to 100% accuracy in children as young as 6–12 months. Other approaches integrating EEG with behavioral features such as eye gaze, facial gestures, and body movements have attained classification accuracies as high as 87.5%. Additionally, resting-state EEG studies have explored microstate differences between ASD and neurotypical individuals. Several ML models, including Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and ensemble methods like Random Forest and Naïve Bayes, have demonstrated classification precision between 90% and 99%. However, challenges such as data heterogeneity and limited sample sizes hinder clinical implementation. This review highlights the most notable EEG-based ML studies for ASD diagnosis and emphasizes the need for further research to refine these techniques for broader clinical adoption.

Estimating the effort of mobile applications is essential because many of the applications are now working on mobile platforms. A need exists to understand the difference between Effort Estimation for mobile applications and other computer applications. The last decade has seen a revolution in the use of mobile applications, which has caused in an exponential increase in the total number of mobile phone users worldwide. The first objective of this work is related to the software industry, and that is to identify which techniques are used for calculating the effort of mobile applications. The first objective also dwells into the identification of the accuracy that was achieved by using those techniques. The second objective is to propose an efficient approach for the effort estimation of mobile applications. A 5+1 methodology is suggested which should be accommodated when proposing a model for the effort estimation of mobile applications. The proposed methodology is validated through intensive investigation of the literature and it is believed that if this 5+1 methodology is adopted, the proposed model will surely bring excellent results in terms of accuracy of the predicted effort that the proposed model will attain. A small case study is also mentioned as a starting point for the validation of the 5+1 proposed methodology and it shows how the methodology can be utilized for the effort estimation of a simple mobile application.

Software effort estimation is the process of estimating the amount of time, effort, and money required to develop and manage software. It has an important role in software project planning and management. It impacts activities such as bidding, budgeting, and resource allocation. However, it is not easy to find a model that fits a project to help in its success. Over the past five decades, a wide range of models and techniques have been proposed. However, not all of them were effective enough. The constant development of these models and techniques show how these are important for accurate prediction. Our survey provides a comprehensive overview of some major software estimation approaches. These include parametric models, expert-based techniques, regression-based methods, analogy approaches, fuzzy logic, neural networks, and machine learning-based estimators. We reviewed research papers from 1970 to 2025 to get information of older models. But we mainly focused on recent papers from 2019 to 2025. We traced the progression and popularity trends of these models and discovered the increasing adoption of methods such as COCOMO, regression, and neural networks. Our study aims to help researchers and practitioners in understanding the landscape of software effort estimation models and techniques according to different generations with their pros and cons. It will also help in selecting or developing models ideal for some specific systems. Additionally, it highlights that no one model remains the best option for every software project.