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In recent years, the field of biomedical video source identification has witnessed a significant evolution driven by advances in both fuzzy-based systems and machine learning models. This paper presents a comprehensive survey of the current state of the art in this domain, highlighting the transition from traditional fuzzy-based approaches to the emerging dominance of machine learning techniques. Biomedical videos have become integral in various aspects of healthcare, from medical imaging and diagnostics to surgical procedures and patient monitoring. The accurate identification of the sources of these videos is of paramount importance for quality control, accountability, and ensuring the integrity of medical data. In this context, source identification plays a critical role in establishing the authenticity and origin of biomedical videos. This survey delves into the evolution of source identification methods, covering the foundational principles of fuzzy-based systems and their applications in the biomedical context. It explores how linguistic variables and expert knowledge were employed to model video sources, and discusses the strengths and limitations of these early approaches. By surveying existing methodologies and databases, this paper contributes to a broader understanding of the field’s progress and challenges.

Telemarketing is a well-established marketing approach to offering products and services to prospective customers. The effectiveness of such an approach, however, is highly dependent on the selection of the appropriate consumer base, as reaching uninterested customers will induce annoyance and consume costly enterprise resources in vain while missing interested ones. The introduction of business intelligence and machine learning models can positively influence the decision-making process by predicting the potential customer base, and the existing literature in this direction shows promising results. However, the selection of influential features and the construction of effective learning models for improved performance remain a challenge. Furthermore, from the modelling perspective, the class imbalance nature of the training data, where samples with unsuccessful outcomes highly outnumber successful ones, further compounds the problem by creating biased and inaccurate models. Additionally, customer preferences are likely to change over time due to various reasons, and/or a fresh group of customers may be targeted for a new product or service, necessitating model retraining which is not addressed at all in existing works. A major challenge in model retraining is maintaining a balance between stability (retaining older knowledge) and plasticity (being receptive to new information). To address the above issues, this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately. A novel online learning method is proposed for model retraining when new samples are available over time. This newly introduced method equips the proposed approach to deal with dynamic data, leading to improved readiness of the proposed model for practical adoption, and is a highly useful addition to the literature. Extensive experiments with real-world data show that the proposed approach achieves excellent results in all cases (e.g., 98.6% accuracy in classifying customers) and outperforms recent competing models in the literature by a considerable margin of 3% on a widely used dataset.

Windows malware is becoming an increasingly pressing problem as the amount of malware continues to grow and more sensitive information is stored on systems. One of the major challenges in tackling this problem is the complexity of malware analysis, which requires expertise from human analysts. Recent developments in machine learning have led to the creation of deep models for malware detection. However, these models often lack transparency, making it difficult to understand the reasoning behind the model’s decisions, otherwise known as the black-box problem. To address these limitations, this paper presents a novel model for malware detection, utilizing vision transformers to analyze the Operation Code (OpCode) sequences of more than 350000 Windows portable executable malware samples from real-world datasets. The model achieves a high accuracy of 0.9864, not only surpassing the previous results but also providing valuable insights into the reasoning behind the classification. Our model is able to pinpoint specific instructions that lead to malicious behavior in malware samples, aiding human experts in their analysis and driving further advancements in the field. We report our findings and show how causality can be established between malicious code and actual classification by a deep learning model, thus opening up this black-box problem for deeper analysis.