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Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by notable challenges in cognitive function, understanding language, recognizing objects, interacting with others, and communicating effectively. Its origins are mainly genetic, and identifying it early and intervening promptly can reduce the necessity for extensive medical treatments and lengthy diagnostic procedures for those impacted by ASD. This research is designed with two types of experimentation for ASD analysis. In the first set of experiments, authors utilized three feature engineering techniques (Chi-square, backward feature elimination, and PCA) with multiple machine learning models for autism presence prediction in toddlers. The proposed XGBoost 2.0 obtained 99% accuracy, F1 score, and recall with 98% precision with chi-square significant features. In the second scenario, main focus shifts to identifying tailored educational methods for children with ASD through the assessment of their behavioral, verbal, and physical responses. Again, the proposed approach performs well with 99% accuracy, F1 score, recall, and precision. In this research, cross-validation technique is also implemented to check the stability of the proposed model along with the comparison of previously published research works to show the significance of the proposed model. This study aims to develop personalized educational strategies for individuals with ASD using machine learning techniques to meet their specific needs better.

IntroductionAdvanced traffic monitoring systems face significant challenges in vehicle detection and classification. Conventional methods often require substantial computational resources and struggle to adapt to diverse data collection methods.MethodsThis research introduces an innovative technique for classifying and recognizing vehicles in aerial image sequences. The proposed model encompasses several phases, starting with image enhancement through noise reduction and Contrast Limited Adaptive Histogram Equalization (CLAHE). Following this, contour-based segmentation and Fuzzy C-means segmentation (FCM) are applied to identify foreground objects. Vehicle detection and identification are performed using EfficientDet. For feature extraction, Accelerated KAZE (AKAZE), Oriented FAST and Rotated BRIEF (ORB), and Scale Invariant Feature Transform (SIFT) are utilized. Object classification is achieved through a Convolutional Neural Network (CNN) and ResNet Residual Network.ResultsThe proposed method demonstrates improved performance over previous approaches. Experiments on datasets including Vehicle Aerial Imagery from a Drone (VAID) and Unmanned Aerial Vehicle Intruder Dataset (UAVID) reveal that the model achieves an accuracy of 96.6% on UAVID and 97% on VAID.DiscussionThe results indicate that the proposed model significantly enhances vehicle detection and classification in aerial images, surpassing existing methods and offering notable improvements for traffic monitoring systems.

Honeybees play a vital role in preservation of an healthy environment. Bees not only provide pollination services but also produce honey, beeswax, and royal jelly. Beekeeping has a rich history and substantial economic potential worldwide, but swarming remains a crucial challenge for maintaining profitability. Swarming, a typical colony reproductive process in honeybees, significantly impacts beekeepers profitability by lowering the number of bees in hives and thus effecting honey production. Monitoring of these beehives is therefore of paramount importance to keep an eye on their irregular behavior. Swarm prediction can be done by visually inspecting hives, monitoring temperature, or analyzing acoustic features with machine learning. Acoustic monitoring is instrumental in detecting changes in colony behavior since it overcomes the constraints of visual inspections and is not affected by external factors like temperature. In this paper, we aim to evaluate various state-of-the-art machine learning and deep learning models for swarm prediction by studying wave plot features, Mel Spectrogram, and Melfrequency Cepstral coefficients (MFCC). We use Naive Bayes, K-nearest Neighbors (KNN), and Support Vector Machines (SVM) as machine learning models and Convolution Neural Networks (CNN), Long Short Term Memory (LSTM), and Transformers as deep learning models for comparison purposes. We apply these models on a well-known honey bees audio dataset provided by the NU-hive project and consider classification metrics such as accuracy, precision, recall, and F1 score for the comparative evaluation of our models. Our evaluation demonstrates SVM as the best-performing machine learning algorithm. In particular, SVM with Mel Spectrogram as input data, achieved an accuracy of around 97%. On the other hand, CNN outperformed all the models and achieved an accuracy of 99%, using MFCC features as input data. As a result of these encouraging outcomes, we understand that our results can help the researchers to choose which AI model is more suitable for them to design beehive monitoring systems for accurate identification of abnormal situations in beehives.

Cybersecurity challenges pose a significant threat to Healthcare Cyber Physical Systems (CPS) because they heavily rely on wireless communication. Particularly, jamming attacks can severely disrupt the integrity of these CPS networks. This research introduces a decentralized system to address this issue. Therefore, this paper suggested a system that leverages trust and blockchain technology to detect jamming attacks in healthcare CPS effectively. It proposes a layered model to improve CPS networks’ lifetime and performance. In smart healthcare environments, it ensures secure and reliable communication between sensor nodes, wearable sensors, medical devices, and monitoring systems. Results show that the suggested approach outperforms the baseline model in identifying and minimizing jamming assaults, with an average percentage difference of 15.71% more detection rate, 20.21% less packet loss rates, 16.65% less node-level energy consumption, reduced network latency of 8.29%, and 9.63% more network throughput.

Abnormal crops image data play crucial role in controlling crop diseases and pest for smart agriculture. However, current agricultural image acquisition methods suffer from low-value data. This article presents a new strategy for collect high-quality image data for abnormal crops. First, a novel agricultural Internet of Things (IoT) image acquisition system is proposed, that integrates edge intelligence, motion–static synergy, which enables both coarse and fine crop image acquisition. To enhance image acquisition efficiency and data value in the agricultural IoT, this article proposes an image acquisition method based on edge intelligence and static and motion collaboration, using banana plantations as the example object. The method comprises three phases. In the first phase, the edge server deploys the YOLO-FDAC target detection model to detect abnormal crops from the images captured by static nodes. In the second phase, the coordinate solution method of abnormal crops and the quantification method of the degree of abnormality is presented. In the third phase, based on the severity of abnormality and the ant colony optimization, a path optimization algorithm for the image acquisition robot is designed. Finally, this article evaluates the performance of each level of the proposed method by comparing it with traditional methods. The experimental results demonstrate that the proposed image acquisition strategy has high acquisition efficiency and high image data value.

The acquisition, processing, mining, and visualization of sensory data for knowledge discovery and decision support has recently been a popular area of research and exploration. Its usefulness is paramount because of its relationship to the continuous involvement in the improvement of healthcare and other related disciplines. As a result of this, a huge amount of data have been collected and analyzed. These data are made available for the research community in various shapes and formats; their representation and study in the form of graphs or networks is also an area of research which many scholars are focused on. However, the large size of such graph datasets poses challenges in data mining and visualization. For example, knowledge discovery from the Bio–Mouse–Gene dataset, which has over 43 thousand nodes and 14.5 million edges, is a non-trivial job. In this regard, summarizing the large graphs provided is a useful alternative. Graph summarization aims to provide the efficient analysis of such complex and large-sized data; hence, it is a beneficial approach. During summarization, all the nodes that have similar structural properties are merged together. In doing so, traditional methods often overlook the importance of personalizing the summary, which would be helpful in highlighting certain targeted nodes. Personalized or context-specific scenarios require a more tailored approach for accurately capturing distinct patterns and trends. Hence, the concept of personalized graph summarization aims to acquire a concise depiction of the graph, emphasizing connections that are closer in proximity to a specific set of given target nodes. In this paper, we present a faster algorithm for the personalized graph summarization (PGS) problem, named IPGS; this has been designed to facilitate enhanced and effective data mining and visualization of datasets from various domains, including biosensors. Our objective is to obtain a similar compression ratio as the one provided by the state-of-the-art PGS algorithm, but in a faster manner. To achieve this, we improve the execution time of the current state-of-the-art approach by using weighted, locality-sensitive hashing, through experiments on eight large publicly available datasets. The experiments demonstrate the effectiveness and scalability of IPGS while providing a similar compression ratio to the state-of-the-art approach. In this way, our research contributes to the study and analysis of sensory datasets through the perspective of graph summarization. We have also presented a detailed study on the Bio–Mouse–Gene dataset, which was conducted to investigate the effectiveness of graph summarization in the domain of biosensors.

Data security has become crucial to most enterprise and government applications due to the increasing amount of data generated, collected, and analyzed. Many algorithms have been developed to secure data storage and transmission. However, most existing solutions require multi-round functions to prevent differential and linear attacks. This results in longer execution times and greater memory consumption, which are not suitable for large datasets or delay-sensitive systems. To address these issues, this work proposes a novel algorithm that uses, on one hand, the reflection property of a balanced binary search tree data structure to minimize the overhead, and on the other hand, a dynamic offset to achieve a high security level. The performance and security of the proposed algorithm were compared to Advanced Encryption Standard and Data Encryption Standard symmetric encryption algorithms. The proposed algorithm achieved the lowest running time with comparable memory usage and satisfied the avalanche effect criterion with 50.1%. Furthermore, the randomness of the dynamic offset passed a series of National Institute of Standards and Technology (NIST) statistical tests.