Skin cancer, particularly the malignant melanoma subtype, is widely recognized as a highly lethal form of cancer characterized by abnormal melanocyte cell growth. However, diagnosing and classifying skin lesions, as well as automatically recognizing malignant tumors from dermoscopy images, present significant challenges. To address this challenge, our study employs variants of Convolutional Neural Networks (CNNs) to effectively diagnose and classify various skin lesion types using the latest benchmark datasets ISIC 2019 and 2020. The dataset underwent rigorous preprocessing, which involves employing advanced Generative Artificial Intelligence (AI) techniques i.e., Generative Adversarial Networks (GANs) and Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN), for augmentation. These generative techniques are carefully evaluated and compared for their effectiveness. Our CNN-based approach involves aggregating results from multiple transfer learning models, including VGG16, VGG19, SVM along with a hybrid model in combination of VGG19 and SVM. On ISIC 2019, we have achieved promising accuracies of 92% for VGG16 and 93% for VGG19. Notably, the hybrid VGG19+SVM model exhibits the highest accuracy of 96%. On ISIC 2020, VGG16, VGG19, and SVM achieves accuracies of 90%, 92%, and 92%, respectively. Our findings underscore the potential of generative AI for augmentation, and the efficacy of CNN-based transfer learning models in improving skin cancer classification accuracy.

Over the past decade, an abundance of research has been conducted in the area of agricultural technology and innovations. The Internet of Things (IoT) has demonstrated its ability to connect numerous agricultural equipment, sensors, and specialists, boosting agricultural procedures in off-the-grid regions. Agriculture has experienced considerable improvements in production, cost reductions, service accessibility, and operational efficiency. With an emphasis on security, developments and trends in the sector, and technological implementation, this research paper offers an up-to-date analysis of existing and projected IoT applications in agriculture. In this article, enabling technologies, agricultural applications based on cutting-edge machine learning models, and services are all examined in relation to the development of IoT deployment in tackling diverse agricultural concerns. In the IoT-based agriculture system, potential challenges and limitations are also addressed. In its conclusion, this research provides an extensive review of the various aspects of IoT in agriculture, with the goal of empowering future researchers enthusiastic to make contributions to and advancement in their quest for a more in-depth comprehension of this field of study. A total of 96 papers were chosen for the selection from 2018 to 2023, and each was categorized using predetermined standards. The research’s results have been thoroughly examined, providing an overview of IoT in agriculture.

Agricultural systems are being revolutionized due to emerging technologies that aim to make improvements in the traditional agriculture system. The major goal is not just to enhance agricultural output per hectare but also to enhance crop quality while protecting the natural environment. Weeds pose a significant threat to crops as they consume nutrients, water, and light, thereby reducing crop productivity. Spraying the entire field uniformly to control weeds not only incurs high costs but also has adverse environmental effects. To address the limitations of conventional weed control methods, in this research, Machine Learning (ML) and Deep Learning (DL) based techniques are proposed to identify and categorize weeds in crops. For ML-based techniques, several statistical and texture-based features are extracted, including central image and Hu moments, mean absolute deviation, Shannon entropy, gray level co-occurrence matrix (GLCM) and local binary patterns (LBP), contrast, energy, homogeneity, dissimilarity, correlation, and summarized local binary pattern histogram. YOLOv8m is employed to identify weeds and for weed classification, features extracted from two standard benchmark datasets, CottonWeedID15 and Earlycrop-weed are fed to Support Vector Machine (SVM), Random Forest, and Artificial Neural Network (ANN) while employing Synthetic Minority Oversampling Technique (SMOTE) to balance the classes. In addition to ML-based techniques, Deep learners such as VGG16, VGG19, Xception, DenseNet121, DenseNet169, DenseNet201, and ConvNeXtBase are trained on raw data with balanced classes for automated feature extraction and classification. Among the ML-based techniques, SVM with a polynomial kernel achieves 99.5% accuracy on the early crop weed dataset, and Artificial neural network attains 89% accuracy on the Cottonweedid15 dataset. Meanwhile, the combined employment of ConvNeXt and Random Forest results in the highest accuracy among DLs, specifically 98% on the early crop weed dataset and 90% on the Cottonweedid15 dataset. The high accuracy achieved underscores the practical viability of these methods, offering a sustainable and cost-effective solution for modern agriculture.

Osteoarthritis (OA) is a prevalent musculoskeletal disorder, predominantly affecting the knee joint and resulting in significant pain and functional limitations. The Kellgren-Lawrence (KL) grading system, traditionally utilized by radiologists, assesses OA severity through radiographic evaluation of knee bones on both sides. Recent advancements in computer-aided methods have focused on enhancing diagnostic precision and efficiency, leveraging automated classification models with Knee X-rays which represent a cornerstone imaging modality for OA severity assessment. In this study, a novel hybrid model combining Convolutional Neural Networks (CNN) and VGG16 architectures is proposed to achieve accurate OA detection. Various neural networks, including CNN, VGG16, VGG19, ResNet50, and CNN-ResNet, are implemented and compared alongside the proposed method. Additionally, data augmentation techniques are employed to address class imbalance, leading to enhanced accuracies across all models. Analysis demonstrates robust performance of all models on the training set. The proposed hybrid method (CNN-ResNet50) outperforms other state-of-the-art approaches, providing precise results across all five stages of OA according to the KL grading system by achieving an accuracy exceeding 93% on training, validation, and testing datasets.

Anomaly detection in Aviation CPS is critical to ensuring safety and reliability. This systematic literature review explores the landscape of machine learning techniques used for anomaly detection in Aviation CPS, analyzing studies published between 2014 and 2024. The review identifies a strong preference for unsupervised learning methods, driven by the challenges of acquiring labeled data in aviation contexts. Additionally, it highlights the emerging trend of hybrid models that combine supervised and unsupervised techniques, offering improved detection accuracy and robustness. However, the review also reveals significant obstacles, such as the limited availability of publicly accessible datasets, which hampers research progress and the ability to benchmark models. Moreover, while accuracy is the most commonly reported performance metric, the need for a broader evaluation framework that includes precision, recall, and other metrics is emphasized. The findings suggest several directions for future research, including developing standardized datasets, optimizing hybrid models, and integrating explainable AI (XAI) to enhance model interpretability. This review contributes to the field by synthesizing current knowledge and providing insights that could guide the development of more effective and reliable anomaly detection systems for Aviation CPS.

The integration of wireless antenna sensors for cyber-physical systems has become increasingly prevalent in various biosimilar applications due to the escalating need for monitoring techniques that are efficient, accurate, and reliable. The primary objective of this comprehensive investigation is to offer a scholarly examination of the present advancements, challenges, and potentialities in the realm of wireless antenna sensor technology for monitoring biosimilars. Specifically, the focus will be on the current state of the art in wireless antenna sensor design, manufacturing, and implementation along with the discussion of cyber security trends. The advantages of wireless antenna sensors, including increased sensitivity, real-time data gathering, and remote monitoring, will next be discussed in relation to their use in a variety of biosimilar applications. Furthermore, we will explore the challenges of deploying wireless antenna sensors for biosimilar monitoring, such as power consumption, signal integrity, and biocompatibility concerns. To wrap things off, there will be a discussion about where this subject is headed and why collaborative work is essential to advancing wireless antenna sensor technology and its applications in biosimilar monitoring. Providing an in-depth overview of the present landscape and potential developments, this article aims to be an asset for academics and professionals in the fields of antenna sensors, biosimilar development, wireless communication technologies, and cyber physical systems.