The increasing urban population has led to challenges in managing crowds in public places, especially in preventing tragic incidents like stampedes. Real-time accurate crowd counting (CC) in AI effectively manages crowd dynamics but faces significant obstacles such as background clutter, perspective variations, and occlusion. This study acknowledges the stated challenges in examining the effectiveness of convolutional arrangements by addressing the encoder–decoder crowd counter network (EDCCN). The model supports an integrated feature extraction process (segmented, edge-oriented, and texture), which makes it capable of calculating precise crowd counts in complex, dense situations. Its novel encoder–decoder arrangement explores low- and high-level crowd features in input images to address occlusion and uneven crowd distribution challenges in samples with different backgrounds. The EDCCN proposes two strategies to enhance people estimation accuracy: first, feature propagation guided without density maps to reduce background interference, and second, a complementary attention mechanism for improved information sharing among convolution layers . The EDCCN model harnesses intra- and inter-depth information representation through a non-increasing-order kernel arrangement, achieving state-of-the-art accuracy in people counting compared to existing methods across free (namely Mall, BRT, SmartCity, Indiana) and surveillance (JHU-Crowd, Venice, STech-B) datasets.

Malicious firmware upgrading represents a critical security vulnerability in Internet of Things (IoT) devices. This study introduces HyCNNAt, a novel hybrid deep learning network for IoT malware detection that synergistically combines Convolutional Neural Networks (CNNs) with transformer attention mechanisms. HyCNNAt’s architecture vertically and horizontally stacks convolution and attention layers, enhancing the network’s generalization capabilities, capacity, and overall effectiveness. We evaluated HyCNNAt using a publicly available IoT firmware dataset, where it demonstrated superior performance with the highest accuracy ([Formula: see text]), F1-score ([Formula: see text]), and recall ([Formula: see text]), highlighting its robust classification capabilities, although its precision ([Formula: see text]) exhibited variability compared to state-of-the-art models such as CoAtNet, MobileViT, MobileNet, and MobileNet variants using transfer learning. These results underscore HyCNNAt’s potential as a robust solution for addressing the pressing challenge of IoT malware detection.

Abstract The visual quality of images captured under sub-optimal lighting conditions, such as over and underexposure may benefit from improvement using fusion-based techniques. This paper presents the Caputo Differential Operator-based image fusion technique for image enhancement. To effect this enhancement, the proposed algorithm first decomposes the overexposed and underexposed images into horizontal and vertical sub-bands using Discrete Wavelet Transform (DWT). The horizontal and vertical sub-bands are then enhanced using Caputo Differential Operator (CDO) and fused by taking the average of the transformed horizontal and vertical fractional derivatives. This work introduces a fractional derivative-based edge and feature enhancement to be used in conjuction with DWT and inverse DWT (IDWT) operations. The proposed algorithm combines the salient features of overexposed and underexposed images and enhances the fused image effectively. We use the fractional derivative-based method because it restores the edge and texture information more efficiently than existing method. In addition, we have introduced a resolution enhancement operator to correct and balance the overexposed and underexposed images, together with the Caputo enhanced fused image we obtain an image with significantly deepened resolution. Finally, we introduce a novel texture enhancing and smoothing operation to yield the final image. We apply subjective and objective evaluations of the proposed algorithm in direct comparison with other existing image fusion methods. Our approach results in aesthetically subjective image enhancement, and objectively measured improvement metrics.

Human skin disease, the most infectious dermatological ailment globally, is initially diagnosed by sight. Some clinical screening and dermoscopic analysis of skin biopsies and scrapings for accurate classification are medically compulsory. Classification of skin diseases using medical images is more challenging because of the complex formation and variant colors of the disease and data security concerns. Both the Convolution Neural Network (CNN) for classification and a federated learning approach for data privacy preservation show significant performance in the realm of medical imaging fields. In this paper, a custom image dataset was prepared with four classes of skin disease, a CNN model was suggested and compared with several benchmark CNN algorithms, and an experiment was carried out to ensure data privacy using a federated learning approach. An image augmentation strategy was followed to enlarge the dataset and make the model more general. The proposed model achieved a precision of 86%, 43%, and 60%, and a recall of 67%, 60%, and 60% for acne, eczema, and psoriasis. In the federated learning approach, after distributing the dataset among 1000, 1500, 2000, and 2500 clients, the model showed an average accuracy of 81.21%, 86.57%, 91.15%, and 94.15%. The CNN-based skin disease classification merged with the federated learning approach is a breathtaking concept to classify human skin diseases while ensuring data security.

Abstract The COVID‐19 pandemic has a significant impact on human health globally. The illness is due to the presence of a virus manifesting itself in a widespread disease resulting in a high mortality rate in the whole world. According to the study, infected patients have distinct radiographic visual characteristics as well as dry cough, breathlessness, fever, and other symptoms. Although, the reverse transcription polymerase‐chain reaction (RT‐PCR) test has been used for COVID‐19 testing its reliability is very low. Therefore, computed tomography and X‐ray images have been widely used. Artificial intelligence coupled with X‐ray technologies has recently shown to be more effective in the diagnosis of this disease. With this motivation, a comparative analysis of fine‐tuned deep learning architectures has been made to speed up the detection and classification of COVID‐19 patients from other pneumonia groups. The models used for this analysis are MobileNetV2, ResNet50, InceptionV3, NASNetMobile, VGG16, Xception, InceptionResNetV2 DenseNet121, which have been fine‐tuned using a new set of layers replaced with the head of the network. This research work has carried out an analysis on two datasets. Dataset‐1 includes the images of three classes: Normal, COVID, and Pneumonia. Dataset‐2, in contrast, contains the same classes with more focus on two prominent pneumonia categories: bacterial pneumonia and viral pneumonia. The research was conducted on 959 X‐ray images (250 of Bacterial Pneumonia, 250 of Viral Pneumonia, 209 of COVID, and 250 of Normal cases). Using the confusion matrix, the required results of different models have been computed. For the first dataset, DenseNet121 has obtained a 97% accuracy, while for the second dataset, MobileNetV2 has performed best with an accuracy of 81%.

Nowaday, emails are used in almost every field, from business to education. Emails have two subcategories, i.e., ham and spam. Email spam, also called junk emails or unwanted emails, is a type of email that can be used to harm any user by wasting his/her time, computing resources, and stealing valuable information. The ratio of spam emails is increasing rapidly day by day. Spam detection and filtration are significant and enormous problems for email and IoT service providers nowadays. Among all the techniques developed for detecting and preventing spam, filtering email is one of the most essential and prominent approaches. Several machine learning and deep learning techniques have been used for this purpose, i.e., Naïve Bayes, decision trees, neural networks, and random forest. This paper surveys the machine learning techniques used for spam filtering techniques used in email and IoT platforms by classifying them into suitable categories. A comprehensive comparison of these techniques is also made based on accuracy, precision, recall, etc. In the end, comprehensive insights and future research directions are also discussed.

The relationship between computing and healthcare has a long history, but adoption of telemedicine is gradual due to political resistance, lack of infrastructure development frameworks, and lack of resources. One of the most rapid technological advancements will be the Internet of Medical Things (IoMT), which is predicted to bring about the greatest technological delivery ever. Edge computing in conjunction with 5G speed is the solution to achieve the requirements of quality of service metrics metrics during the analysis of clinical data. Artificial intelligence with edge computing has made significant contributions to the smart healthcare system's network for ultra-reliable communication in the areas of less delay, widespread device connectivity, and enhanced speed of data transmission. Since the edge-enabled IoMT-based system in the healthcare system offers a number of extraordinary potential, this Special Issue explores those areas of applicability. The aim of Special Issue is to cover the research difficulties associated with the implementation of edge computing-based IoMT systems in the healthcare system and suggests a framework for such a system.

This study presents an effective segmentation method which is based on neutrosophic clustering with the integration of texture features for images. The proposed method transforms the image into the neutrosophic domain and then extracts the texture features using analogies of human preattentive texture discrimination mechanisms. Finally, the neutrosophic clustering is employed to segment the images. This method can handle the indeterminacy of pixels to have strong clusters and to perform segmentation effectively with the noisy images. Experiments are performed with various types of natural and medical images to exhibit the performance of proposed segmentation method. The evaluation of proposed method has been done with other segmentation methods to measure its performance which shows its robustness for noisy and textured images.

Neutrosophy is a useful tool for handling uncertainty associated with the images and widely used in image denoising. Speckle noise is inherent in ultrasound images, which generally tends to reduce resolution and contrast, thereby degrading the diagnostic accuracy. This paper presents a variational method based on Gamma distribution in the neutrosophic domain to improve clinical diagnosis and to enhance quality of ultrasound images. In this method, image is transformed into the neutrosophic (NS) domain via three membership subsets ( true , indeterminate and false ). Then, the filtering operation is applied based on total variation regularisation to reduce the indeterminacy of the image, which is measured by the entropy of an indeterminate set. The proposed speckle reduction method has been assessed on both the artificial speckle simulated images and real US images. The experimental results reveal the superiority of the proposed method in terms of both quantitatively and qualitatively as compared to other speckle reduction methods reported in the literature. Furthermore, the visual evaluation of despeckled images demonstrates that the proposed method suppresses the speckle noise as well as preserves the textures and fine details.