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.

Plant diseases can have profound effects on the economy, impacting both local and global scales. These diseases can lead to substantial losses in agricultural productivity, affecting crop yields and quality. In this context, deep learning algorithms are widely acknowledged as effective solutions. However, the use of these black-box approaches raises concerns about trust in interpreting and validating the decisions generated by the models. This study proposes an explainable artificial intelligence (XAI) based plant disease classification system to classify and identify distinct ailments with improved accuracy. The system correctly identifies 38 different plant diseases with accuracy, precision, and recall as 99.69%, 98.27%, and 98.26%, respectively. These predictions are subjected to additional analysis employing the local interpretable model-agnostic explanations (LIME) framework to produce visual explanations aligning with prior beliefs and adhering to established best practices in explanations. This system will serve as a promising avenue for revolutionizing disease detection, fostering informed decision-making, and ultimately contributing to global food security.

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.

Skin diseases are common in human beings because of significant changes in surrounding environments.The most of these diseases are curable if diagnosed at initial stages. Therefore, earlydiagnosis can spare people’s precious lives. To address these issues, we proposed a novel model based on deep learning to diagnose the skin disease at a preliminary stage using classification. The developedmodel correctly identifies six different skin diseases namely, actinic keratosis, benign keratosis, melanoma,basal cell carcinoma, insects bite and skin acne. Several state-of-the-art algorithms are examinedon benchmark datasets (International Skin Imaging Collaboration (ISIC) 2019 dataset andUCI Data Center) for accuracy, precision, recall and F1-score metrics. The results show that convolutionalneural network (CNN) has a distinct superiority over its peers with accuracy rate of97%, precision 91%, recall 91% and F1-score 91%. This system will provide skin care handlingservices that are precise and accurate and help the dermatologist in early diagnosis of skin diseases

The software project scheduling (SPS) problem deals with optimal allocation of employees to tasks such that the project is completed within budget and schedule. This is well addressed in the absence of dynamic events and has been demonstrated in SPS research with the static scenarios only. The software development methodologies have evolved where customer requirements are not initially locked but are flexible to take into account ongoing additional requirements or changes. In this regard, ‘new employee addition’ has emerged as one of the critical dynamic events that could significantly disrupt the project budget and schedule. In this paper, the SPS is modelled as multi-objective optimization problem and a novel approach is presented to deal with ‘new employee addition’ dynamic event; that uses domain knowledge to generate a robust schedule. The proposed heuristic is further evaluated on 18 dynamic benchmark scenarios and 3 real-world instances. The results show that the proposed heuristic besides its capability to handle dynamic events also demonstrates good convergence while maintaining good distribution of solutions in a dynamic environment. The principal target audience for this research are project managers in software development organizations who will get optimal project reschedule under ‘new employee addition’ dynamic event particularly in complex, large scale and multi-project settings.