Fuzzy clustering based scheduling algorithm for minimizing the tasks completion time in cloud computing environment.

This paper explores the complexity of project planning in a cloud computing environment and recognizes the challenges associated with distributed resources, heterogeneity, and dynamic changes in workloads. This research introduces a fresh approach to planning cloud resources more effectively by utilizing fuzzy waterfall techniques. The goal is to make better use of resources while cutting down on scheduling costs.

Soft computing analysis of the factors associated with stress, anxiety, and depression.

Stress, Anxiety, and Depression (SAD) are pervasive mental health issues that have substantial impacts on individual and societal well-being. This paper identifies eight key factors of SAD, workplace pressure to poor sleep quality ( - ), and explores six targeted interventions ( to ) designed to mitigate these effects. Among these causes, poor sleep and chronic fatigue are emphasized for their profound impact on mental health, as they disrupt emotional resilience and cognitive functioning.

Image dataset of healthy and infected fig leaves with Ficus leaf worm.

This work presents an extensive dataset comprising images meticulously obtained from diverse geographic locations within Iraq, depicting both healthy and infected fig leaves affected by Ficus leafworm. This particular pest poses a significant threat to economic interests, as its infestations often lead to the defoliation of trees, resulting in reduced fruit production. The dataset comprises two distinct classes: infected and healthy, with the acquisition of images executed with precision during the fruiting season, employing state-of-the-art high-resolution equipment, as detailed in the specifications table.

Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model.

Esophageal cancer (EC) is a commonly occurring malignant tumor that significantly affects human health. Earlier recognition and classification of EC or premalignant lesions can result in highly effective targeted intervention. Accurate detection and classification of distinct stages of EC provide effective precision therapy planning and improve the 5-year survival rate.