Internet of things (IoT) and data-driven techniques are creating greater opportunities for smart dairy farming. The demand for milk is continuously increasing due to increasing population of the world. The consumption of the dairy products is more in developed countries as compared to developing countries. To meet this increased demand for milk products, better technological techniques for improving milk yield are required. It is expected that the use of IoT and different AI techniques can assist a farmer to overcome different traditional farming challenges and increase the milk production. In this research, the authors address different challenges that a dairy farmer has to face in daily life. Brief introduction of smart dairy farming (SDF) is presented with respect to the innovation in production and the processes of smart dairy farming. This review focuses on different aspects of smart dairy farming, and finally a state-of-the-art framework that can assist the farmers to increase the milk yield by using different latest technologies has been proposed. These technological methods can decrease the factors negatively affecting milk production and increase those positively affecting production with minimal resources.

Context: Recently, research community of certain domain showing their eagerness towards the use of social media networks to gain constructive knowledge in decision making and automation, such as aid to perform software development activities, crypto-currencies usage, network community detection and recommendation and so on. Recently, besides other domains of eHealth, the use of social media and big data analytics has become hot topic to predict the patient of mental illness involved in either depression, schizophrenia, eating disorders, anxiety or addictive behaviors. Problem: Traditional methods either need enough historic data or to keep the regular monitoring on patient activities for identification of a patient associated with a mental illness disease. Method: In order to address this issue, we propose a methodology to classify the patients associated with chronic mental illness diseases (i.e. Anxiety, Depression, Bipolar, and ADHD (Attention Deficit Hyperactivity Disorder) based on the data extracted from the Reddit, a well-known network community platform. The proposed method is employed through Co-training (type of semi-supervised learning approach) technique by incorporating the discriminative power of widely used classifiers namely Random Forrest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB). We used Reddit API to download posts and top five associated comments for construction of a feature space. Results: The experimental results indicate the effectiveness of Co-training based classification rather than the state of the art classifiers by a margin of 3% on average in par with every state of art technique. In future, the proposed method could be employed to investigate any classification problem of any domain by extracting date from the social media.

Due to the successful application of machine learning techniques in several fields, automated diagnosis system in healthcare has been increasing at a high rate. The aim of the study is to propose an automated skin cancer diagnosis and triaging model and to explore the impact of integrating the clinical features in the diagnosis and enhance the outcomes achieved by the literature study. We used an ensemble‐learning framework, consisting of the EfficientNetB3 deep learning model for skin lesion analysis and Extreme Gradient Boosting (XGB) for clinical data. The study used PAD‐UFES‐20 data set consisting of six unbalanced categories of skin cancer. To overcome the data imbalance, we used data augmentation. Experiments were conducted using skin lesion merely and the combination of skin lesion and clinical data. We found that integration of clinical data with skin lesions enhances automated diagnosis accuracy. Moreover, the proposed model outperformed the results achieved by the previous study for the PAD‐UFES‐20 data set with an accuracy of 0.78, precision of 0.89, recall of 0.86, and F1 of 0.88. In conclusion, the study provides an improved automated diagnosis system to aid the healthcare professional and patients for skin cancer diagnosis and remote triaging.

In recent years, Explainable Artificial Intelligence (XAI) has attracted considerable attention from the research community, primarily focusing on elucidating the opaque decision-making processes inherent in complex black-box machine learning systems such as deep neural networks. This spike in interest originates from the widespread adoption of black-box models, particularly in critical domains like healthcare and fraud detection, highlighting the pressing need to understand and validate their decision-making mechanisms rigorously. In addition, prominent XAI techniques, including LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (Shapley Additive exPlanations), rely on heuristics and cannot guarantee the correctness of the explanations provided. This article systematically addresses this critical issue associated with machine learning and deep learning models, underscoring XAI’s pivotal role in promoting model transparency to enhance decision-making quality. Furthermore, this study advocates integrating Formal Methods to provide correctness guarantees for black-box internal decision-making. The proposed methodology unfolds in three pivotal stages: firstly, training black-box models using neural networks to generate synthetic datasets; secondly, employing LIME and SHAP techniques to interpret the models and visualize their internal decision-making processes; and finally, training decision trees on the synthetic datasets to implement Formal Methods for ensuring the correctness of the black-box model’s decision-making. To validate this proposed approach, experimentation was conducted on four widely recognized medical datasets, including the Wisconsin Breast Cancer and Thyroid Cancer (TC) datasets, which are available in the UCI Machine Learning Repository. Specifically, this research represents a significant contribution by pioneering a novel approach that seamlessly integrates XAI and Formal Methods, thereby furnishing correctness guarantees for internal decision-making processes within the healthcare domain.

State-of-the-art software technologies have enabled Higher Education Institutions to record and store large amounts of student data. Analyzing this large amount of data can facilitate the decision-making process. Decisions made in higher education institutions affect policies, strategies and actions that improve education quality. It can be argued that machine learning algorithms demonstrate a remarkable ability to recognize models and predict results based on data. However, machine learning outcomes were subject to bias and erroneous labelling. Consequently, to mitigate the impact of such errors in decision making, it is necessary to put in place mechanisms to correct decision rules. This article presents an approach to learn decision rules through supervised machine learning and proves the correctness of these rules with hierarchical Coloured Petri Nets. The use of formalism in the proposed methodology ensures that the decision rules are correct and comprehensible. Empirical results show that we improved correctness with 98.68% accuracy on decision rules. This research work contributes to the improvement of the decision-making process for the academic administration of Higher Education Institutes.

Recently, the rate of data generation has reached unprecedented levels, leading to a huge amount of data volume. In addition, modern-day computing systems generate data in diverse formats, ranging from unstructured to structured and semi-structured. As technological advancements experience exponential growth, novel trends, and strategies are emerging in the field of Big Data to enhance data quality and derive valuable insights, particularly in industries like healthcare. The primary objective of this study is to investigate the challenges and applications of Big Data in healthcare, with a specific focus on improving clinical decision-making. By analyzing 185 papers published between 2012 and 2023, this review article aims to provide a comprehensive overview of the techniques and methods employed in utilizing Big Data Analytics in the healthcare domain. Furthermore, the article aspires to assist the research community in identifying suitable approaches and methodologies for their healthcare-related studies.