Acute Myeloid Leukemia (AML) is a kind of fatal blood cancer with a high death rate caused by abnormal cells’ rapid growth in the human body. The usual method to detect AML is the manual microscopic examination of the blood sample, which is tedious and time‐consuming and requires a skilled medical operator for accurate detection. In this work, we proposed an AlexNet‐based classification model to detect Acute Myeloid Leukemia (AML) in microscopic blood images and compared its performance with LeNet‐5‐based model in Precision, Recall, Accuracy, and Quadratic Loss. The experiments are conducted on a dataset of four thousand blood smear samples. The results show that AlexNet was able to identify 88.9% of images correctly with 87.4% precision and 98.58% accuracy, whereas LeNet‐5 correctly identified 85.3% of images with 83.6% precision and 96.25% accuracy.

Driver’s stress detection is a critical research area that helps reduce the likelihood of traffic accidents and driver’s health complexities due to prolonged stress. Previous work in this area is heavily based on traditional machine learning models that classify the driver’s stress levels using handcrafted features extraction techniques. Extracting the best features using these approaches is always a challenging task. Recently, deep learning techniques have emerged for constructing reliable features automatically and classifying the classes with high accuracy. However, large deep learning models face gradient exploding or vanishing problems. Moreover, acquiring a large dataset for training an entire network from scratch is also a challenging task. This paper is based on the deep transfer learning technique to avoid these problems and to reduce computational cost and time. Seven models are proposed for real-world driver’s stress levels detection using Electrocardiogram (ECG) signals. Different Convolutional Neural Network (CNN)-based pre-trained networks are used to classify the driver’s three stress levels. The time-frequency ECG components for the three stress levels are obtained as scalogram images using a normalized Continuous Wavelet Transform (CWT) filter bank and Morse wavelet. Results show that Model 5 based on Xception outperforms the GoogLeNet, DarkNet-53, ResNet-101, InceptionResNetV2, DenseNet-201, and InceptionV3 based models by 11.32%, 11.32%, 9.45%, 7.54%, 5.66%, and 1.88% respectively and achieves 98.11% overall validation accuracy. Ranking estimation using fuzzy logic approach shows that Xception based Model 5 achieves the highest rank for driver’s high and medium stress levels, while DenseNet-201 based Model 4 achieves the highest rank for low-stress level detection among the other models.

The physical characteristics of ultrasonically sprayed indium-doped zinc oxide (ZnO:In) thin films, with electrical resistivity as low as 3.42 × 10⁻³ Ω·cm and high optical transmittance, in the visible range, of 50%–70% is presented. Zinc acetylacetonate and indium chloride were used as the organometallic zinc precursor and the doping source, respectively, achieving ZnO:In thin films with growth rate in the order of 100 nm/min. The effects of both indium concentration and the substrate temperature on the structural, morphological, optical, and electrical characteristics were measured. All the films were polycrystalline, fitting well with hexagonal wurtzite type ZnO. A switching in preferential growth, from (002) to (101) planes for indium doped samples were observed. The surface morphology of the films showed a change from hexagonal slices to triangle shaped grains as the indium concentration increases. Potential applications as transparent conductive electrodes based on the resulting low electrical resistance and high optical transparency of the studied samples are considered.

A study on the propane gas-sensing properties of Cu-doped ZnO thin films is presented in this work. The films were deposited on glass substrates by sol-gel and dip coating methods, using zinc acetate as a zinc precursor, copper acetate and copper chloride as precursors for doping. For higher sensitivity values, two film thickness values are controlled by the six and eight dippings, whereas for doping, three dippings were used, irrespective of the Cu precursor. The film structure was analyzed by X-ray diffractometry, and the analysis of the surface morphology and film composition was made through scanning electron microscopy (SEM) and secondary ion mass spectroscopy (SIMS), respectively. The sensing properties of Cu-doped ZnO thin films were then characterized in a propane atmosphere, C₃H₈, at different concentration levels and different operation temperatures of 100, 200 and 300 °C. Cu-doped ZnO films doped with copper chloride presented the highest sensitivity of approximately 6 × 10⁴, confirming a strong dependence on the dopant precursor type. The results obtained in this work show that the use of Cu as a dopant in ZnO films processed by sol-gel produces excellent catalysts for sensing C₃H₈ gas.

Globally, cyberattacks are growing and mutating each month. Intelligent Intrusion Network Detection Systems are developed to analyze and detect anomalous traffic to face these threats. A way to address this is by using network flows, an aggregated version of communications between devices. Network Flow datasets are used to train Artificial Intelligence (AI) models to classify specific attacks. Training these models requires threat samples usually generated synthetically in labs as capturing them on operational network is a challenging task. As threats are fast-evolving, new network flows are continuously developed and shared. However, using old datasets is still a popular procedure when testing models, hindering a more comprehensive characterization of the advantages and opportunities of recent solutions on new attacks. Moreover, a standardized benchmark is missing rendering a poor comparison between the models produced by algorithms. To address these gaps, we present a benchmark with fourteen recent and preprocessed datasets and study seven categories of algorithms for Network Intrusion Detection based on Network Flows. We provide a centralized source of pre-processed datasets to researchers for easy download. All dataset are also provided with a train, validation and test split to allow a straightforward and fair comparison between existing and new solutions. We selected open state-of-the-art publicly available algorithms, representatives of diverse approaches. We carried out an experimental comparison using the Macro F1 score of these algorithms. Our results highlight each model operation on dataset scenarios and provide guidance on competitive solutions. Finally, we discuss the main characteristics of the models and benchmarks, focusing on practical implications and recommendations for practitioners and researchers.