Malaria is a serious and lethal disease that has been reported by the World Health Organization (WHO), with an estimated 219 million new cases and 435,000 deaths globally. The most frequent malaria detection method relies mainly on the specialists who examine the samples under a microscope. Therefore, a computerized malaria diagnosis system is required. In this article, malaria cell segmentation and classification methods are proposed. The malaria cells are segmented using a color-based k-mean clustering approach on the selected number of clusters. After segmentation, deep features are extracted using pre-trained models such as efficient-net-b0 and shuffle-net, and the best features are selected using the Manta-Ray Foraging Optimization (MRFO) method. Two experiments are performed for classification using 10-fold cross-validation, the first experiment is based on the best features selected from the pre-trained models individually, while the second experiment is performed based on the selection of best features from the fusion of extracted features using both pre-trained models. The proposed method provided an accuracy of 99.2% for classification using the linear kernel of the SVM classifier. An empirical study demonstrates that the fused features vector results are better as compared to the individual best-selected features vector and the existing latest methods published so far.

/pdf/an-optimized-features-selection-approach-based-on-manta-ray-3vy0ufw8.pdf

An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification

Weighted variable based numerical scheme for time-lagged semilinear parabolic problems including small parameter

Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed. Among all other deep learning techniques used in medical imaging, U-Net-based variants are the most used models found in the literature to segment medical images with respect to different modalities. Therefore, the goal of this paper is to examine the numerous advancements and innovations in the U-Net architecture, as well as recent trends, with the aim of highlighting the ongoing potential of U-Net being used to better the performance of brain tumor segmentation. Furthermore, we provide a quantitative comparison of different U-Net architectures to highlight the performance and the evolution of this network from an optimization perspective. In addition to that, we have experimented with four U-Net architectures (3D U-Net, Attention U-Net, R2 Attention U-Net, and modified 3D U-Net) on the BraTS 2020 dataset for brain tumor segmentation to provide a better overview of this architecture’s performance in terms of Dice score and Hausdorff distance 95%. Finally, we analyze the limitations and challenges of medical image analysis to provide a critical discussion about the importance of developing new architectures in terms of optimization.

https://www.mdpi.com/2075-4418/13/9/1624/pdf?version=1683173063

U-Net-Based Models towards Optimal MR Brain Image Segmentation


Purpose
Environmental sustainability is quickly becoming one of the most critical issues in industry development. This study aims to conduct a systematic literature review through which the author can provide various research areas to work on for future researchers and provide insight into Industry 4.0 and environmental sustainability.


Design/methodology/approach
This study accomplishes this by performing a backward analysis using text mining on the Scopus database. Latent semantic analysis (LSA) was used to analyze the corpus of 4,364 articles published between 2013 and 2023. The authors generated ten clusters using keywords in the industrial revolution and environmental sustainability domain, highlighting ten research avenues for further exploration.


Findings
In this study, three research questions discuss the role of environmental sustainability with Industry 4.0. The author predicted ten clusters treated as recent trends on which more insight is required from future researchers. The authors provided year-wise analysis, top authors, top countries, top sources and network analysis related to the topic. Finally, the study provided industrialization’s effect on environmental sustainability and the future aspect of automation.


Research limitations/implications
The reliability of the current study may be compromised, notwithstanding the size of the sample used. Poor retrieval of the literature corpus can be attributed to the limitations imposed by the search words, synonyms, string construction and variety of search engines used, as well as to the accurate exclusion of results for which the search string is insufficient.


Originality/value
This research is the first-ever study in which a natural language processing technique is implemented to predict future research areas based on the keywords–document relationship.


Industrial revolution and environmental sustainability: an analytical interpretation of research constituents in Industry 4.0

How to carry out fast and reliable dynamic deformation monitoring of building structures is an important issue for engineers and technicians. Digital close-range photogrammetry technology has the advantages of noncontact, multipoint, and rapid monitoring, and the application of UAVs makes the monitoring task no longer limited by the size of the field, but the current technology can not guarantee the rapid deployment of UAVs in dynamic deformation monitoring, and these methods commonly have higher requirements for image quality, and cannot ensure the stability of accuracy. To this end, we propose a dynamic displacement monitoring method for UAVs based on digital close-range photogrammetry technology and optimize the data processing process for the problem that there is still moving when the UAV hovers. A building with an overhead corridor structure with a large flow of people was selected for the experiment. The experiment used ground digital cameras and drones to monitor separately to compare the monitoring results. The results show that the proposed method is simple and easy to use. The monitoring method can be deployed quickly, which is expected to be applied to emergency monitoring scenarios.

The Dynamic Displacement Monitoring Method Using Unmanned Aerial Vehicles Based on Digital Close-Range Photogrammetry

The quadrotor drone is small in size and light in weight, the mechanical structure is simple, and the requirements for the working environment are low. The development of a quadrotor UAV technology is also the focus of the current technical personnel. The author proposes a four-rotor UAV sensor fault diagnosis and fault-tolerant control based on genetic algorithm and introduces common sensor failures, and based on the improved BP neural network of the GA algorithm, the genetic algorithm is improved. This paper uses a classical BP algorithm, a classical GA-BP algorithm, and an improved GA-BP algorithm for training. Using a total of 150 sets of training data and training function using LevenbregMarquardt (trainlm), MeanSquaredError (performance function using mse), in the same noise background, the improved GA-BP algorithm has the highest detection rate, the classical GA-BP algorithm followed, and classical BP algorithm is the worst. Therefore, using the improved GA-BP algorithm, various errors of the sensor can be detected quickly and accurately.

/pdf/sensor-fault-diagnosis-and-fault-tolerant-control-of-2qiqwfir.pdf

Sensor Fault Diagnosis and Fault Tolerant Control of Quadrotor UAV Based on Genetic Algorithm

Due to the plasmodium parasite, malaria is transmitted mostly through red blood cells. Manually counting blood cells is extremely time consuming and tedious. In a recommendation for the advanced technology stage and analysis of malarial disease, the performance of the XG-Boost, SVM, and neural networks is compared. In comparison to machine learning models, convolutional neural networks provide reliable results when analyzing and recognizing the same datasets. To reduce discrepancies and improve robustness and generalization, we developed a model that analyzes blood samples to determine whether the cells are parasitized or not. Experiments were conducted on 13,750 parasitized and 13,750 parasitic samples. Support vector machines achieved 94% accuracy, XG-Boost models achieved 90% accuracy, and neural networks achieved 80% accuracy. Among these three models, the support vector machine was the most accurate at distinguishing parasitized cells from uninfected ones. An accuracy rate of 97% was achieved by the convolution neural network in recognizing the samples. The deep learning model is useful for decision making because of its better accuracy.

/pdf/detection-of-peripheral-malarial-parasites-in-blood-smears-3kguboy1.pdf

Detection of Peripheral Malarial Parasites in Blood Smears Using Deep Learning Models

Facial expression is one form of communication which being non-verbal in nature precedes verbal communication in both origin and conception. Most of the existing methods for Automatic Facial Expression Recognition (AFER) are mainly focused on global feature extraction assuming that all facial regions contribute equal amount of discriminative information to predict the expression class. The detection and localization of facial regions that have significant contribution to expression recognition and extraction of highly discriminative feature distribution from those regions are not fully explored. The key contributions of the proposed work are developing novel feature distribution upon combining the discriminative power of shape and texture feature; determining the contribution of facial regions and identifying the prominent facial regions that hold abstract and highly discriminative information for expression recognition. The shape and texture features taken into consideration are Local Phase Quantization (LPQ), Local Binary Pattern (LBP), and Histogram of Oriented Gradients (HOG). Multiclass Support Vector Machine (MSVM) is used while one versus one classification. The proposed work is implemented on CK+, KDEF, and JAFFE benchmark facial expression datasets. The recognition rate of the proposed work is 94.2% on CK+ and 93.7% on KDEF, which is significantly more than the existing handcrafted feature-based methods. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.12700

Automatic facial expression recognition combining texture and shape features from prominent facial regions

Highly efficient numerical scheme for solving fuzzy system of linear and non-linear equations with application in differential equations

A significant amount of effort and cost is required to collect training samples for remote sensing image classifications. The study of remote sensing and how to read multispectral images is becoming more important. High-dimensional multispectral images are created by the various bands that show how materials behave. The need for more information about things and the improvement of sensor resolutions have led to the creation of multispectral data with a higher size. In recent years, it has been shown that the high dimensionality of these data makes it hard to preprocess them in multiple ways. Recent research has demonstrated that one of the most crucial methods to address this issue is by adopting a variety of learning strategies. But as the data gets more complicated, these methodologies are not adequate to support. The proposed methodology shows that the classification experiment using remote sensing images indicates the maximum likelihood classifier with different deep learning models; weight vector (WV) AdaBoost and ADAM can greatly limit overfitting, and it obtains high classification accuracy. Proposed VGG16 and Inception v3 increase classification accuracy along with optimization process produce 96.08%.

/pdf/an-efficient-multispectral-image-classification-and-15ud18sa.pdf

M. Shah

Papers

Sensor Fault Diagnosis and Fault Tolerant Control of Quadrotor UAV Based on Genetic Algorithm

Detection of Peripheral Malarial Parasites in Blood Smears Using Deep Learning Models

Automatic facial expression recognition combining texture and shape features from prominent facial regions

Highly efficient numerical scheme for solving fuzzy system of linear and non-linear equations with application in differential equations

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data