Osteosarcoma is one of the aggressive bone tumors with numerous histologic patterns. Histopathological inspection is a crucial criterion in the medical diagnosis of Osteosarcoma. Due to the advancement of computing power and hardware technology, pathological image analysis system based on artificial intelligence (AI) were more commonly used. But classifying many intricate pathology images by hand will be challenging for pathologists. The lack of labeling data makes the system difficult to build and costly. This article designs a Honey Badger Optimization with Deep Learning based Automated Osteosarcoma Classification (HBODL-AOC) model. The HBODL-AOC technique’s goal is to identify osteosarcoma’s existence using medical images. In the presented HBODL-AOC technique, image preprocessing is initially performed by contrast enhancement technique. For feature extraction, the HBODL-AOC technique employs a deep convolutional neural network-based Mobile networks (MobileNet) model with an Adam optimizer for hyperparameter tuning. Finally, the adaptive neuro-fuzzy inference system (ANFIS) approach is implemented for the HBO (Honey Badger Optimization) algorithm can tune osteosarcoma classification and the membership function (MF). To demonstrate the enhanced classification performance of the HBODL-AOC approach, a sequence of simulations was performed. The extensive simulation analysis portrayed the improved performance of the HBODL-AOC technique over existing DL models.

Cardiovascular diseases (CVDs) continue to have a negative impact on global health, which highlights the need for accurate and efficient prediction methods. Machine learning (ML) techniques as tools for forecasting CVD has recently showed potential. This paper presents a comprehensive analysis of research trends in the field, focusing on visualizing the impact of ML in cardiovascular disease prediction. We used data visualization techniques to identify patterns and trends in an extensive database of scholarly publications on this subject that were published in Scopus between 1991 and 2023. The analysis reveals a substantial growth in research output, demonstrating the growing demand for ML-based CVD prediction. It reveals essential stakeholders and potential collaborators while highlighting the institutions and authors who have contributed most to this domain. The study also identifies high-impact journals that have published significant research in this domain, facilitating researchers in selecting appropriate outlets for dissemination. The study helps researchers identify the most critical areas for further research and fosters cooperation among subject-matter experts by offering insightful information about machine learning-based cardiovascular disease prediction development. The data is analyzed using the tools VOSviewer and Biblioshiny.

Due to the blooming of Industrial 4.0 such as internet of things (IoT), cloud computing, industrial IoT (IIoT), and artificial intelligence (AI), with their innovative ideas and opportunities, the cyber attacker's modus operandi against the cyber defense triage is incredible. The genre of advanced persistent threat (APT) actors/group are equipped with sophisticated and substantial resources of tools, techniques, and procedure (TTP) at a breakneck pace. The IoT gadgets such as sensors, intelligent devices, and various rapidly emerging resources with energy, memory, and processing power are exponentially prone to multiple vulnerabilities. The nature of IIoT prompts heterogenous and rapid changes ranging the vulnerabilities from simple to complex attacks. APT menace follows the covert TTPs to target the asset of any organization like the government, military, or financial industry.