The software project scheduling (SPS) is a project-scheduling problem where limited human resources are assigned to the tasks in multi-team project settings. Besides other dynamic events, employees experience evolution has direct influence in completing large-scale projects within budget and time. In this paper, a new SPS model is developed as a dynamic multi-objective optimization problem, which incorporates employees experience evolution with their learning ability over time. The experimental results on 24 problem instances (including six real-world instances) show that the developed SPS model reduces project duration by 40% while being within budget. The results provide evidence that consideration of experience evolution while tasks reallocation under dynamic events significantly optimizes project schedules. Moreover, the developed SPS model is evaluated with six state-of-the-art algorithms as bi-criterion evolution (BCE), NSGA-II, NSGA-III, Two_Arch2, OMOPSO, speed-constrained multi-objective particle swarm optimization (SMPSO) where BCE demonstrated distinct superiority for 63% data instances.

The skin lesion types result in delayed diagnosis due to high similarity in early stages of the skin cancer. In this regard, deep learning algorithms are well-recognized solutions; however, these black box approaches result in lack of trust as dermatologists are unable to interpret and validate the decisions made by the models. In this paper, an explainable artificial intelligence (XAI) based skin lesion classification system is proposed to improve the skin lesion classification accuracy. This will help the dermatologists to make rational diagnosis in the early stages of skin cancer. The proposed XAI model is validated using International Skin Imaging Collaboration (ISIC) 2019 dataset. The developed model correctly identifies the eight types of skin lesions (dermatofibroma, squamous cell carcinoma, benign keratosis, melanocytic nevus, vascular lesion, actinic keratosis, basal cell carcinoma and melanoma) with classification accuracy, precision, recall and F1 score as 94.47%, 93.57%, 94.01%, and 94.45% respectively. These predictions are further analyzed using the local interpretable model-agnostic explanations (LIME) framework to generate visual explanations that match a prior belief and general explanation best practices. The explainability integrated within our model will enhance its applicability in real clinical practice.

In conventional healthcare, real-time monitoring of patient records and information mining for timely diagnosis of chronic diseases under certain health conditions is a crucial process. Chronic diseases, if not diagnosed in time, may result in patients’ death. In modern medical and healthcare systems, Internet of Things (IoT) driven ecosystems use autonomous sensors to sense and track patients’ medical conditions and suggest appropriate actions. In this paper, a novel IoT and machine learning (ML)-based hybrid approach is proposed that considers multiple perspectives for early detection and monitoring of 6 different chronic diseases such as COVID-19, pneumonia, diabetes, heart disease, brain tumor, and Alzheimer’s. The results from multiple ML models are compared for accuracy, precision, recall, F1 score, and area under the curve (AUC) as a performance measure. The proposed approach is validated in the cloud-based environment using benchmark and real-world datasets. The statistical analyses on the datasets using ANOVA tests show that the accuracy results of different classifiers are significantly different. This will help the healthcare sector and doctors in the early diagnosis of chronic diseases.

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