[This retracts the article DOI: 10.1007/s11277-021-08969-0.].

Cancer of bone marrow is classified as Acute Lymphoblastic Leukemia (ALL), an abnormal growth of lymphoid progenitor cells. It affects both children and adults and is the most predominant form of infantile cancer. Currently, there has been significant growth in the identification and therapy of acute lymphoblastic leukemia. Therefore, a method is required that is capable to accurately assessing risk by an appropriate treatment strategy that takes into account all relevant clinical, morphological, cytogenetic, and molecular aspects. However, to enhance survival and quality of life for those afflicted by this aggressive haematological malignancy, more research and clinical trials are required to address the issues associated with resistance, relapse, and long-term toxicity. Consequently, a deep optimized Convolutional Neural Network (CNN) has been proposed for the early diagnosis and detection of ALL. The design of the deep optimized CNN model consisted of five convolutional blocks with thirteen convolutional layers and five max pool layers. The proposed deep optimized CNN model is tuned using the hyperparameters such as 30 epochs, batch size 32 and optimizers, namely Adam and Adamax. Out of the two optimizers, the proposed deep optimized CNN model has outperformed using Adam optimizer with the points of accuracy and precision as 0.96 and 0.95, respectively.

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.

Aim Deep learning models, such as deep convolutional neural networks (CNNs), have undergone extensive scrutiny in the context of food classification because of their exceptional feature extraction capabilities. Background Similarly, ensemble-based learning approaches have exhibited great potential for achieving effective supervised classification. Objective We suggest an innovative approach to improve the effectiveness of deep learning-based food classification. Methods Our proposal involves a novel deep learning ensemble framework that draws inspiration from the fusion of deep learning models with ensemble learning based on random subspaces. The random subspaces play a role in diversifying the ensemble system in a straightforward but impactful way. Moreover, to enhance the classification accuracy even more, we explore transfer learning, employing the migration of acquired weights from a single classifier to another (namely, CNNs). This approach expedites the process of learning. Results Results from experiments conducted using well-established food datasets illustrate that the suggested deep learning ensemble system delivers competitive performance compared to state-of-the-art techniques, as evidenced by its classification accuracy. Conclusion The amalgamation of deep learning and ensemble learning holds substantial promise for dependable food categorization.

Background/Objectives: The integration of digital imaging technologies in dentistry has revolutionized diagnostic and treatment practices, with panoramic radiographs playing a crucial role in detecting impacted teeth. Manual interpretation of these images is time consuming and error prone, highlighting the need for automated, accurate solutions. This study proposes an artificial intelligence (AI)-based model for detecting impacted teeth in panoramic radiographs, aiming to enhance accuracy and reliability. Methods: The proposed model combines YOLO (You Only Look Once) and RT-DETR (Real-Time Detection Transformer) models to leverage their strengths in real-time object detection and learning long-range dependencies, respectively. The integration is further optimized with the Weighted Boxes Fusion (WBF) algorithm, where WBF parameters are tuned using Bayesian optimization. A dataset of 407 labeled panoramic radiographs was used to evaluate the model’s performance. Results: The model achieved a mean average precision (mAP) of 98.3% and an F1 score of 96%, significantly outperforming individual models and other combinations. The results were expressed through key performance metrics, such as mAP and F1 scores, which highlight the model’s balance between precision and recall. Visual and numerical analyses demonstrated superior performance, with enhanced sensitivity and minimized false positive rates. Conclusions: This study presents a scalable and reliable AI-based solution for detecting impacted teeth in panoramic radiographs, offering substantial improvements in diagnostic accuracy and efficiency. The proposed model has potential for widespread application in clinical dentistry, reducing manual workload and error rates. Future research will focus on expanding the dataset and further refining the model’s generalizability.

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 (97.11%+/- 1.02%), F1-score (99.992%+/- 0.004%), and recall (97.48%+/- 2.6556%), highlighting its robust classification capabilities, although its precision (91.27%+/- 45.08%) 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.

Generative Adversarial Networks (GANs) constitute an advanced category of deep learning models that have significantly transformed the domain of generative modelling. They demonstrate a profound capability to produce realistic and high-quality synthetic data across various domains. Recently, GANs have also emerged as a powerful and innovative approach in medical image processing. Numerous scholarly investigations consistently highlight the superiority of GAN-based methodologies in this context. The generation of realistic synthetic images aims to advance segmentation precision, augment image quality, and facilitate multimodal analysis. These enhancements significantly bolster the analytical capabilities of medical professionals, leading to more precise diagnostic evaluations and the formulation of personalized treatment plans, thereby contributing to improved patient prognosis. In this work, we rigorously review the latest advancements in the application of Generative Adversarial Networks (GANs) within the domain of medical imaging, encompassing research published between 2018 and 2024. The corpus of literature selected for this review is derived from the most relevant and authoritative databases, including Elsevier, Springer, IEEE Xplore, and Google Scholar, among others. This review rigorously evaluates scholarly publications employing Generative Adversarial Networks (GANs) for the synthesis and generation of medical images, segmentation of medical imaging data, image-to-image translation in medical contexts, and denoising or reconstruction of medical imagery. The findings of this review present a thorough synthesis of contemporary applications of Generative Adversarial Networks (GANs) in the domain of medical imaging. This investigation serves as a prospective reference in the realm of GAN utilization for medical image processing, offering guidance and insights for current and future research endeavors.

Crowd detection and counting are important tasks in several applications of crowd analysis including traffic management, public safety and event planning. Automatic crowd counting using images and videos is an intriguing but complex issue that has generated considerable interest in computer vision. During the past several years, various learning models have been developed by considering several factors such as model design, input pathways, learning paradigms, computing complexity and accuracy that increases cutting-edge performance. In this work, the most critical advances in the crowd analysis field are reviewed methodically and thoroughly. Numerous crowd counting models have been arranged according to how well these models perform on different datasets using various learning approaches and evaluation metrics like mean average error and mean square error. This work provides insight into the effectiveness of different learning models for crowd analysis. It will be helpful for researchers and practitioners in choosing the appropriate model for their specific applications.

The optimisation of Wireless Sensor Networks (WSNs) is critical in the ever-changing environment of E-commerce. This work describes a unique technique for designing and modelling energy-efficient WSNs adapted to the specific demands of E-commerce applications. This study investigates the complex connection between network performance, data collecting, and energy saving using cutting-edge AI and optimisation approaches. The suggested Multi/Many-optimisation algorithm tries to establish a delicate balance by providing smooth data transmission and collecting in E-commerce settings while minimising energy usage through the incorporation of intelligence algorithms. This research contributes to the advancement of operational efficiency and sustainability within the E-commerce business by addressing this essential part of E-commerce infrastructure, paving the way for more cost-effective and environmentally responsible E-commerce infrastructure. The fundamental goal of this research study is to discover the Pareto-optimal front in Multi/Many-objective optimisation, which contains the subtle trade-offs between Multi/Many-objectives. This allows decision-makers to weigh the frequently contradictory aims of WSN networks. The suggested technique, which is recognised for its adaptability, variety, and multi-objective optimisation capabilities, prepares the way for the full potential of E-commerce applications and services.

There has been a steady increase in gesture-based applications interacting with various electronic devices. Characters and numerals are written in the air using air-writing applications. Due to the lack of stroke information and a reference point on the writing plane in the 3D space, the recognition process of 3D writing is more complex than conventional 2D writing in a harsh environment. However, these complexities can be evaded with thorough modeling of the 3D trajectories. Temporal Convolutional Networks (TCN) have been proposed for an air-writing recognition system. TCNs are variations of convolutional neural network tasks involving sequence modeling. The methodology was applied to three publicly available datasets containing air-written digits and characters by various writers. The results demonstrated the effectiveness of the temporal networks in the recognition process of 3D characters. An accuracy of 99.50% and 99.56% was observed for digits and characters using the TCNs technique.

Diabetic retinopathy is a condition that affects the retina and causes vision loss due to blood vessel destruction. The retina is the layer of the eye responsible for visual processing and nerve signaling. Diabetic retinopathy causes vision loss, floaters, and sometimes blindness; however, it often shows no warning signals in the early stages. Deep learning-based techniques have emerged as viable options for automated illness classification as large-scale medical imaging datasets have become more widely available. To adapt to medical image analysis tasks, transfer learning makes use of pre-trained models to extract high-level characteristics from natural images. In this research, an intelligent recommendation-based fine-tuned EfficientNetB0 model has been proposed for quick and precise assessment for the diagnosis of diabetic retinopathy from fundus images, which will help ophthalmologists in early diagnosis and detection. The proposed EfficientNetB0 model is compared with three transfer learning-based models, namely, ResNet152, VGG16, and DenseNet169. The experimental work is carried out using publicly available datasets from Kaggle consisting of 3,200 fundus images. Out of all the transfer learning models, the EfficientNetB0 model has outperformed with an accuracy of 0.91, followed by DenseNet169 with an accuracy of 0.90. In comparison to other approaches, the proposed intelligent recommendation-based fine-tuned EfficientNetB0 approach delivers state-of-the-art performance on the accuracy, recall, precision, and F1-score criteria. The system aims to assist ophthalmologists in early detection, potentially alleviating the burden on healthcare units.

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by a deficit of dopamine in the brain. This condition has the potential to impact individuals of advanced age. The procedure for diagnosing PD is currently not well established. Diagnostics includes a range of methods, including the identification and evaluation of symptoms, the implementation of clinical trials, and the use of laboratory tests. This research work employs a range of machine learning (ML) algorithms, including k-nearest neighbors (k-NN), support vector machines (SVMs), random forest (RF), logistic regression (LR), and AdaBoost boosting approaches, to predict the occurrence of PD and assist healthcare practitioners in recommending tailored treatment plans. To evaluate the suggested ML methods, it is customary to use a standard dataset consisting of various biological voice measures obtained from individuals afflicted with PD as well as healthy individuals. The experimental results demonstrate that the LR model achieves an accuracy of 86%, the k-NN model achieves an accuracy of 92%, the SVM model achieves an accuracy of 95%, the RF model achieves an accuracy of 95%, and the AdaBoost boosting model achieves an accuracy of 93%. SVM and RF are well acknowledged for their high accuracy in classification tasks. Upon conducting a comparative analysis with other studies, it was shown that the proposed intervention yielded outcomes that were either comparable to or superior to those reported in previous research.

Patients with Parkinson's disease (PD) often manifest motor dysfunction symptoms, including tremors and stiffness. The presence of these symptoms may significantly impact the handwriting and sketching abilities of individuals during the initial phases of the condition. Currently, the diagnosis of PD depends on several clinical investigations conducted inside a hospital setting. One potential approach for facilitating the early identification of PD within home settings involves the use of hand-written drawings inside an automated PD detection system for recognition purposes. In this study, the PD Spiral Drawings public dataset was used for the investigation and diagnosis of PD. The experiments were conducted alongside a comparative analysis using 204 spiral and wave PD drawings. This study contributes by conducting deep learning models, namely DenseNet201 and VGG16, to detect PD. The empirical findings indicate that the DenseNet201 model attained a classification accuracy of 94% when trained on spiral drawing images. Moreover, the model exhibited a receiver operating characteristic (ROC) value of 99%. When comparing the performance of the VGG16 model, it was observed that it attained a better accuracy of 90% and exhibited a ROC value of 98% when trained on wave images. The comparative findings indicate that the outcomes of the proposed PD system are superior to existing PD systems using the same dataset. The proposed system is a very promising technological approach that has the potential to aid physicians in delivering objective and dependable diagnoses of diseases. This is achieved by leveraging important and distinctive characteristics extracted from spiral and wave drawings associated with PD.

An increasing number of people are living with Parkinson’s disease (PD), which has emerged as a major issue in world health in the last several years. The application of artificial intelligence methodologies has demonstrated encouraging outcomes, therefore emerging as a crucial tool in addressing the early diagnosis of PD. This study presents a comprehensive overview of the identification and monitoring of PD using contemporary technology. This research primarily focuses on the development of advanced diagnostic methods for the early detection of PD, with the aim of effectively managing the condition during its first phases. Our proposed approach can assist professionals in continuously monitoring the PD scale, which is a rating system for PD. The machine learning (ML) techniques used for diagnosing PD included random forest (RF), K-nearest neighbors, support vector machine, and artificial neural network. The algorithms were evaluated using a standardized dataset obtained from Kaggle, which includes data on PD, sleep behavior disorder, and healthy control subjects. The dataset was partitioned into a training set comprising 70% of the data and a testing set comprising 30% of the data using the proposed ML method. The empirical results show that the RF algorithms score a high percentage of 96% with respect to accuracy. This system facilitates the enhancement of social healthcare quality through the implementation of timely interventions, the improvement of patient outcomes, and the optimization of resource allocation.