Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area.

Feature modeling is a common approach for configuring and capturing commonalities and variations among different Software Product Lines (SPL) products. This process is carried out by a set of SPL design teams, each working on a different configuration of the desired product. The integration of these configurations leads to inconsistencies in the final product design. The typical solution involves extensive deliberation and unnecessary resource usage, which makes SPL inconsistency resolution an expensive and unoptimized process. We present the first comprehensive evaluation of swarm intelligence (using Particle Swarm Optimization) to the problem of resolving inconsistencies in a configured integrated SPL product. We call it o-SPLIT (optimization-based Software Product LIne Tool) and validate o-SPLIT with standard ERP, SPLOT (Software Product Lines Online Tools), and BeTTy (BEnchmarking and TesTing on the analYsis) product configurations along with diverse feature set sizes. The results show that Particle Swarm Optimization can successfully optimize SPL product configurations. Finally, we implement o-SPLIT as a decision-support tool in a real, local SPL setting and acquire subjective feedback from SPL designers which shows that the teams are convinced of the usability and high-level decision support provided by o-SPLIT.

Length of stay (LoS) prediction is deemed important for a medical institution’s operational and logistical efficiency. Sound estimates of a patient’s stay increase clinical preparedness and reduce aberrations. Various statistical methods and techniques are used to quantify and predict the LoS of a patient based on pre-operative clinical features. This study evaluates and compares the results of Bayesian (simple Bayesian regression and hierarchical Bayesian regression) models and machine learning (ML) regression models against multiple evaluation metrics for the problem of LoS prediction of cardiac patients admitted to Tabba Heart Institute, Karachi, Pakistan (THI) between 2015 and 2020. In addition, the study also presents the use of hierarchical Bayesian regression to account for data variability and skewness without homogenizing the data (by removing outliers). LoS estimates from the hierarchical Bayesian regression model resulted in a root mean squared error (RMSE) and mean absolute error (MAE) of 1.49 and 1.16, respectively. Simple Bayesian regression (without hierarchy) achieved an RMSE and MAE of 3.36 and 2.05, respectively. The average RMSE and MAE of ML models remained at 3.36 and 1.98, respectively.

Breast cancer is one of the most prevalent cancers affecting women globally. Early diagnosis is crucial for effective treatment and improved survival rates. Imaging techniques such as mammography and ultrasound are widely used conventional diagnostic methods. However, these methods have limitations, including low sensitivity and specificity, especially in patients with dense breast tissue. For instance, mammograms miss approximately 20% of breast cancer cases, leading to false negatives and delayed treatment that can have fatal consequences. To address these challenges, artificial intelligence (AI)-based diagnostic tools have been developed to assist healthcare professionals in accurately detecting breast cancer. These tools work in conjunction with human radiologists to improve diagnostic outcomes. In addition, biomarkers present a promising non-invasive, more convenient alternative for the early detection of breast cancer, potentially overcoming the limitations of traditional screening methods. Various biomarkers, such as circulating tumor cells, cell-free tumor nucleic acids, and microRNAs, have shown promise in early breast cancer diagnosis. A systematic literature review is needed to consolidate ongoing efforts in molecular biology and biomedical sciences aimed at achieving early breast cancer diagnosis. One of the limitations of previously published research is the heterogeneity of methodologies, which can compromise the credibility of comparisons due to potential inaccuracies in the original data. Hence, future studies should prioritize using consistent datasets and developing robust techniques to manage missing values, outliers, and class imbalances to improve the reliability of breast cancer detection models. This literature review seeks to bridge the knowledge gap by reporting recent high-performing AI models and effective biomarkers that can serve as diagnostic tools in clinical practice.