The current outbreak of monkeypox (mpox) has become a major public health concern because of the quick spread of this disease across multiple countries. Early detection and diagnosis of mpox is crucial for effective treatment and management. Considering this, the purpose of this research was to detect and validate the best performing model for detecting mpox using deep learning approaches and classification models. To achieve this goal, we evaluated the performance of five common pretrained deep learning models (VGG19, VGG16, ResNet50, MobileNetV2, and EfficientNetB3) and compared their accuracy levels when detecting mpox. The performance of the models was assessed with metrics (i.e., the accuracy, recall, precision, and F1-score). Our experimental results demonstrate that the MobileNetV2 model had the best classification performance with an accuracy level of 98.16%, a recall of 0.96, a precision of 0.99, and an F1-score of 0.98. Additionally, validation of the model with different datasets showed that the highest accuracy of 0.94% was achieved using the MobileNetV2 model. Our findings indicate that the MobileNetV2 method outperforms previous models described in the literature in mpox image classification. These results are promising, as they show that machine learning techniques could be used for the early detection of mpox. Our algorithm was able to achieve a high level of accuracy in classifying mpox in both the training and test sets, making it a potentially valuable tool for quick and accurate diagnosis in clinical settings.

Background/Objectives: One of the key challenges in autism is early diagnosis. Early diagnosis leads to early interventions that improve the condition and not worsen autism in the future. Currently, autism diagnoses are based on monitoring by a doctor or specialist after the child reaches a certain age exceeding three years after the parents observe the child’s abnormal behavior. Methods: The paper aims to find another way to diagnose autism that is effective and earlier than traditional methods of diagnosis. Therefore, we used the Eye Gaze fixes map dataset and Eye Tracking Scanpath dataset (ETSDS) to diagnose Autistic Spectrum Disorder (ASDs), while a subset of the ETSDS was used to recognize autism scores. Results: The experimental results showed that the higher accuracy rate reached 96.1% and 98.0% for the hybrid model on Eye Gaze fixes map datasets and ETSDS, respectively. A higher accuracy rate was reached (98.1%) on the ETSDS used to recognize autism scores. Furthermore, the results showed the outperformer for the proposed method results compared to previous works. Conclusions: This confirms the effectiveness of using artificial intelligence techniques in diagnosing diseases in general and diagnosing autism, in addition to the need to increase research in the field of diagnosing diseases using advanced techniques.

The Internet of Things (IoT) has expanded into a realm of cutting-edge integrated solutions across numerous applications, following three stages of development: communication, authentication, and computing. However, each layer inside the three tier IoT architecture faces a spectrum of security concerns due to the IoT’s openness, scope, and resource limits. Therefore, developing a secure IoT system is vital, shielding against attackers leveraging IoT devices to obtain network access and maintaining data security during transmission between these devices. Despite developments in Intrusion Detection Systems, identifying Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks in real-time remains a significant issue, especially in the environment of IoT devices, which are diverse and resource restricted. This study provides a Convolutional Neural Network-Support Vector Machine (CNN-SVM) hybrid technique, targeted at overcoming these limits by enhancing detection speed and accuracy, thus addressing this substantial gap in the area. This research offers a hybrid model that combines CNN for feature extraction with SVM as a classifier. This model employs a CNN to extract essential elements from the data and classifies attacks as either DDoS or benign. Our results highlight the potential performance of our model, which was trained on the real Canadian institute for cybersecurity (CIC) IoT dataset in 2023. Notably, our hybrid model outperformed classic machine learning (ML) alternatives such as CNN, SVM, K-nearest neighbors, Naïve Bayes, and Logistic Regression. During testing, our model attained a remarkable accuracy rate of 99% and an F 1-score of 99%, outperforming the highest-performing SVM ML model with an accuracy of 98% and the other ML methods.

Swarm intelligence algorithms are meta heuristics that simulates the nature for solving optimization problems, Artificial Bee Colony (ABC) algorithm is one of the most recent nature inspired algorithms which used for problem optimization, numerous research efforts has been concentrated in this particular area. However, the Artificial Bee Colony performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The Artificial Bee Colony is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions, in order to overcome these limitations as well as to broaden the scope and viability of nature inspired algorithms many variations of this algorithm are being presented and the results being very amazing. This paper presents an overview of some of the hybridized meta heuristics with Artificial Bee Colony algorithm for continuous function optimization; many benchmark functions have been used to show the validity of every approach.