Automated detection and recognition system for chewable food items using advanced deep learning models.

Identifying and recognizing the food on the basis of its eating sounds is a challenging task, as it plays an important role in avoiding allergic foods, providing dietary preferences to people who are restricted to a particular diet, showcasing its cultural significance, etc. In this research paper, the aim is to design a novel methodology that helps to identify food items by analyzing their eating sounds using various deep learning models. To achieve this objective, a system has been proposed that extracts meaningful features from food-eating sounds with the help of signal processing techniques and deep learning models for classifying them into their respective food classes. Initially, 1200 audio files for 20 food items labeled have been collected and visualized to find relationships between the sound files of different food items. Later, to extract meaningful features, various techniques such as spectrograms, spectral rolloff, spectral bandwidth, and mel-frequency cepstral coefficients are used for the cleaning of audio files as well as to capture the unique characteristics of different food items. In the next phase, various deep learning models like GRU, LSTM, InceptionResNetV2, and the customized CNN model have been trained to learn spectral and temporal patterns in audio signals. Besides this, the models have also been hybridized i.e. Bidirectional LSTM + GRU and RNN + Bidirectional LSTM, and RNN + Bidirectional GRU to analyze their performance for the same labeled data in order to associate particular patterns of sound with their corresponding class of food item. During evaluation, the highest accuracy, precision,F1 score, and recall have been obtained by GRU with 99.28%, Bidirectional LSTM + GRU with 97.7% as well as 97.3%, and RNN + Bidirectional LSTM with 97.45%, respectively. The results of this study demonstrate that deep learning models have the potential to precisely identify foods on the basis of their sound by computing the best outcomes.

Enhancing parasitic organism detection in microscopy images through deep learning and fine-tuned optimizer.

Parasitic organisms pose a major global health threat, mainly in regions that lack advanced medical facilities. Early and accurate detection of parasitic organisms is vital to saving lives. Deep learning models have uplifted the medical sector by providing promising results in diagnosing, detecting, and classifying diseases. This paper explores the role of deep learning techniques in detecting and classifying various parasitic organisms. The research works on a dataset consisting of 34,298 samples of parasites such as Toxoplasma Gondii, Trypanosome, Plasmodium, Leishmania, Babesia, and Trichomonad along with host cells like red blood cells and white blood cells. These images are initially converted from RGB to grayscale followed by the computation of morphological features such as perimeter, height, area, and width. Later, Otsu thresholding and watershed techniques are applied to differentiate foreground from background and create markers on the images for the identification of regions of interest. Deep transfer learning models such as VGG19, InceptionV3, ResNet50V2, ResNet152V2, EfficientNetB3, EfficientNetB0, MobileNetV2, Xception, DenseNet169, and a hybrid model, InceptionResNetV2, are employed. The parameters of these models are fine-tuned using three optimizers: SGD, RMSprop, and Adam. Experimental results reveal that when RMSprop is applied, VGG19, InceptionV3, and EfficientNetB0 achieve the highest accuracy of 99.1% with a loss of 0.09. Similarly, using the SGD optimizer, InceptionV3 performs exceptionally well, achieving the highest accuracy of 99.91% with a loss of 0.98. Finally, applying the Adam optimizer, InceptionResNetV2 excels, achieving the highest accuracy of 99.96% with a loss of 0.13, outperforming other optimizers. The findings of this research signify that using deep learning models coupled with image processing methods generates a highly accurate and efficient way to detect and classify parasitic organisms.

Editorial: Recent Advances in Deep Learning and Medical Imaging for Cancer Treatment.

In the evolving landscape of medical imaging, the escalating need for deep-learningmethods takes center stage, offering the capability to autonomously acquire abstract datarepresentations crucial for early detection and classification for cancer treatment. Thecomplexities in handling diverse inputs, high-dimensional features, and subtle patternswithin imaging data are acknowledged as significant challenges in this technologicalpursuit. This Special Issue, "Recent Advances in Deep Learning and Medical Imagingfor Cancer Treatment", has attracted 19 high-quality articles that cover state-of-the-artapplications and technical developments of deep learning, medical imaging, automaticdetection, and classification, explainable artificial intelligence-enabled diagnosis for cancertreatment. In the ever-evolving landscape of cancer treatment, five pivotal themes haveemerged as beacons of transformative change. This editorial delves into the realms ofinnovation that are shaping the future of cancer treatment, focusing on five interconnectedthemes: use of artificial intelligence in medical imaging, applications of AI in cancerdiagnosis and treatment, addressing challenges in medical image analysis, advancementsin cancer detection techniques, and innovations in skin cancer classification.

In Vitro and In Vivo Imaging-Based Evaluation of Doxorubicin Anticancer Treatment in Combination with the Herbal Medicine Black Cohosh.

As a substitution for hormone replacement therapy, many breast cancer patients use black cohosh (BC) extracts in combination with doxorubicin (DOX)-based chemotherapy. In this study, we evaluated the viability and survival of BC- and DOX-treated MCF-7 cells. A preclinical model of MCF-7 xenografts was used to determine the influence of BC and DOX administration on tumor growth and metabolism. The number of apoptotic cells after incubation with both DOX and BC was significantly increased (~100%) compared to the control. Treatment with DOX altered the potential of MCF-7 cells to form colonies; however, coincubation with BC did not affect this process. In vivo, PET-CT imaging showed that combined treatment of DOX and BC induced a significant reduction in both metabolic activity (29%) and angiogenesis (32%). Both DOX and BC treatments inhibited tumor growth by 20% and 12%, respectively, and combined by 57%, vs. control. We successfully demonstrated that BC increases cytotoxic effects of DOX, resulting in a significant reduction in tumor size. Further studies regarding drug transport and tumor growth biomarkers are necessary to establish the underlying mechanism and potential clinical use of BC in breast cancer patients.

Receptor for advanced glycation end-products: Biological significance and imaging applications.

The receptor for advanced glycation end-products (RAGE or AGER) is a transmembrane, immunoglobulin-like receptor that, due to its multiple isoform structures, binds to a diverse range of endo- and exogenous ligands. RAGE activation caused by the ligand binding initiates a cascade of complex pathways associated with producing free radicals, such as reactive nitric oxide and oxygen species, cell proliferation, and immunoinflammatory processes. The involvement of RAGE in the pathogenesis of disorders such as diabetes, inflammation, tumor progression, and endothelial dysfunction is dictated by the accumulation of advanced glycation end-products (AGEs) at pathologic states leading to sustained RAGE upregulation. The involvement of RAGE and its ligands in numerous pathologies and diseases makes RAGE an interesting target for therapy focused on the modulation of both RAGE expression or activation and the production or exogenous administration of AGEs. Despite the known role that the RAGE/AGE axis plays in multiple disease states, there remains an urgent need to develop noninvasive, molecular imaging approaches that can accurately quantify RAGE levels in vivo that will aid in the validation of RAGE and its ligands as biomarkers and therapeutic targets. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Diagnostic Tools > Biosensing.

Radix-4 CORDIC algorithm based low-latency and hardware efficient VLSI architecture for Nth root and Nth power computations.

In this article, a low-complexity VLSI architecture based on a radix-4 hyperbolic COordinate Rotion DIgital Computer (CORDIC) is proposed to compute the [Formula: see text] root and [Formula: see text] power of a fixed-point number. The most recent techniques use the radix-2 CORDIC algorithm to compute the root and power. The high computation latency of radix-2 CORDIC is the primary concern for the designers. [Formula: see text] root and [Formula: see text] power computations are divided into three phases, and each phase is performed by a different class of the proposed modified radix-4 CORDIC algorithms in the proposed architecture. Although radix-4 CORDIC can converge faster with fewer recurrences, it demands more hardware resources and computational steps due to its intricate angle selection logic and variable scale factor. We have employed the modified radix-4 hyperbolic vectoring (R4HV) CORDIC to compute logarithms, radix-4 linear vectoring (R4LV) to perform division, and the modified scaling-free radix-4 hyperbolic rotation (R4HR) CORDIC to compute exponential. The criteria to select the amount of rotation in R4HV CORDIC is complicated and depends on the coordinates [Formula: see text] and [Formula: see text] of the rotating vector. In the proposed modified R4HV CORDIC, we have derived the simple selection criteria based on the fact that the inputs to R4HV CORDIC are related. The proposed criteria only depend on the coordinate [Formula: see text] that reduces the hardware complexity of the R4HV CORDIC. The R4HR CORDIC shows the complex scale factor, and compensation of such scale factor necessitates the complex hardware. The complexity of R4HR CORDIC is reduced by pre-computing the scale factor for initial iterations and by employing scaling-free rotations for later iterations. Quantitative hardware analysis suggests better hardware utilization than the recent approaches. The proposed architecture is implemented on a Virtex-6 FPGA, and FPGA implementation demonstrates [Formula: see text] less hardware utilization with better error performance than the approach with the radix-2 CORDIC algorithm.

A Machine-Learning-Based Approach to Prediction of Biogeographic Ancestry within Europe.

Data obtained with the use of massive parallel sequencing (MPS) can be valuable in population genetics studies. In particular, such data harbor the potential for distinguishing samples from different populations, especially from those coming from adjacent populations of common origin. Machine learning (ML) techniques seem to be especially well suited for analyzing large datasets obtained using MPS. The Slavic populations constitute about a third of the population of Europe and inhabit a large area of the continent, while being relatively closely related in population genetics terms. In this proof-of-concept study, various ML techniques were used to classify DNA samples from Slavic and non-Slavic individuals. The primary objective of this study was to empirically evaluate the feasibility of discerning the genetic provenance of individuals of Slavic descent who exhibit genetic similarity, with the overarching goal of categorizing DNA specimens derived from diverse Slavic population representatives. Raw sequencing data were pre-processed, to obtain a 1200 character-long binary vector. A total of three classifiers were used-Random Forest, Support Vector Machine (SVM), and XGBoost. The most-promising results were obtained using SVM with a linear kernel, with 99.9% accuracy and F1-scores of 0.9846-1.000 for all classes.

Advanced deep learning techniques for early disease prediction in cauliflower plants.

Agriculture plays a pivotal role in the economies of developing countries by providing livelihoods, sustenance, and employment opportunities in rural areas. However, crop diseases pose a significant threat to both farmers' incomes and food security. Furthermore, these diseases also show adverse effects on human health by causing various illnesses. Till date, only a limited number of studies have been conducted to identify and classify diseased cauliflower plants but they also face certain challenges such as insufficient disease surveillance mechanisms, the lack of comprehensive datasets that are properly labelled as well as are of high quality, and the considerable computational resources that are necessary for conducting thorough analysis. In view of the aforementioned challenges, the primary objective of this manuscript is to tackle these significant concerns and enhance understanding regarding the significance of cauliflower disease identification and detection in rural agriculture through the use of advanced deep transfer learning techniques. The work is conducted on the four classes of cauliflower diseases i.e. Bacterial spot rot, Black rot, Downy Mildew, and No disease which are taken from VegNet dataset. Ten deep transfer learning models such as EfficientNetB0, Xception, EfficientNetB1, MobileNetV2, EfficientNetB2, DenseNet201, EfficientNetB3, InceptionResNetV2, EfficientNetB4, and ResNet152V2, are trained and examined on the basis of root mean square error, recall, precision, F1-score, accuracy, and loss. Remarkably, EfficientNetB1 achieved the highest validation accuracy (99.90%), lowest loss (0.16), and root mean square error (0.40) during experimentation. It has been observed that our research highlights the critical role of advanced CNN models in automating cauliflower disease detection and classification and such models can lead to robust applications for cauliflower disease management in agriculture, ultimately benefiting both farmers and consumers.

Effects of interventions targeting the systemic inflammatory response to cardiac surgery on clinical outcomes in adults.

BACKGROUND: Organ injury is a common and severe complication of cardiac surgery that contributes to the majority of deaths. There are no effective treatment or prevention strategies. It has been suggested that innate immune system activation may have a causal role in organ injury. A wide range of organ protection interventions targeting the innate immune response have been evaluated in randomised controlled trials (RCTs) in adult cardiac surgery patients, with inconsistent results in terms of effectiveness. OBJECTIVES: The aim of the review was to summarise the results of RCTs of organ protection interventions targeting the innate immune response in adult cardiac surgery. The review considered whether the interventions had a treatment effect on inflammation, important clinical outcomes, or both. SEARCH METHODS: CENTRAL, MEDLINE, Embase, conference proceedings and two trial registers were searched on October 2022 together with reference checking to identify additional studies. SELECTION CRITERIA: RCTs comparing organ protection interventions targeting the innate immune response versus placebo or no treatment in adult patients undergoing cardiac surgery where the treatment effect on innate immune activation and on clinical outcomes of interest were reported. DATA COLLECTION AND ANALYSIS: Searches, study selection, quality assessment, and data extractions were performed independently by pairs of authors. The primary inflammation outcomes were peak IL-6 and IL-8 concentrations in blood post-surgery. The primary clinical outcome was in-hospital or 30-day mortality. Treatment effects were expressed as risk ratios (RR) and standardised mean difference (SMD) with 95% confidence intervals (CI). Meta-analyses were performed using random effects models, and heterogeneity was assessed using I2. MAIN RESULTS: A total of 40,255 participants from 328 RCTs were included in the synthesis. The effects of treatments on IL-6 (SMD -0.77, 95% CI -0.97 to -0.58, I2 = 92%) and IL-8 (SMD -0.92, 95% CI -1.20 to -0.65, I2 = 91%) were unclear due to heterogeneity. Heterogeneity for inflammation outcomes persisted across multiple sensitivity and moderator analyses. The pooled treatment effect for in-hospital or 30-day mortality was RR 0.78, 95% CI 0.68 to 0.91, I2 = 0%, suggesting a significant clinical benefit. There was little or no treatment effect on mortality when analyses were restricted to studies at low risk of bias. Post hoc analyses failed to demonstrate consistent treatment effects on inflammation and clinical outcomes. Levels of certainty for pooled treatment effects on the primary outcomes were very low. AUTHORS' CONCLUSIONS: A systematic review of RCTs of organ protection interventions targeting innate immune system activation did not resolve uncertainty as to the effectiveness of these treatments, or the role of innate immunity in organ injury following cardiac surgery.

Old wine into new wineskins? "Legacy data" in research on Roman Period East Germanic iron smelting.

This paper discusses the use of "legacy data" in research on Roman Period iron smelting in the territory of the Przeworsk Culture in Magna Germania. The dataset includes results of 240 analyses of smelting slag and iron ores chemistry. A majority of these analyses were conducted in the 1950s-1980s. The quality of these data is far below present-day standards. Only major elements were reported, analytical methods were often not specified (although optical emission spectroscopy and wet chemical analyses can be assumed in such cases) and information on detection limits, precision and accuracy of the results is missing. In spite of this, a Principal Component Analysis-Agglomerative Hierarchical Clustering treatment successfully isolated observations from the three main iron smelting regions of the Przeworsk Culture (the Holy Cross Mountains, Masovia and Silesia). These results to a degree confirm a theory proposed in the 1960s by J. Piaskowski, according to whom it was possible to distinguish iron produced in the Holy Cross Mountains from the iron produced elsewhere in the territory of what is now Poland on the basis of metal characteristics. These findings will pave the way to the ongoing research project on the Przeworsk Culture metallurgy. It is also argued that, apart from a search for new methods in iron provenance studies, more attention should be paid to results of earlier analyses and to a compilation of legacy databases. The other result is an open and flexible Agglomerative Hierarchical Clustering R code to examine discrimination between production areas and to propose artefact provenance patterns in a convenient interactive way using the R development environment.

Amphiphilic Molecules Exhibiting Zwitterionic Excited-State Intramolecular Proton Transfer and Near-Infrared Emission for the Detection of Amyloid ß Aggregates in Alzheimer's Disease.

Chromophores with zwitterionic excited-state intramolecular proton transfer (ESIPT) have been shown to have larger Stock shifts and red-shifted emission wavelengths compared to the conventional π-delocalized ESIPT molecules. However, there is still a dearth of design strategies to expand the current library of zwitterionic ESIPT compounds. Herein, a novel zwitterionic excited-state intramolecular proton transfer system is reported, enabled by addition of 1,4,7-triazacyclononane (TACN) fragments on a dicyanomethylene-4H-pyran (DCM) scaffold. The solvent-dependent steady-state photophysical studies, pKa measurements, and computational analysis strongly support that the ESIPT process is more efficient with two TACN groups attached to the DCM scaffold and not affected by polar protic solvents. Impressively, compound DCM-OH-2-DT exhibits a near-infrared (NIR) emission at 740 nm along with an uncommonly large Stokes shift. Moreover, DCM-OH-2-DT shows high affinity towards soluble amyloid ß (Aß) oligomers in vitro and in 5xFAD mouse brain sections, and we have successfully applied DCM-OH-2-DT for the in vivo imaging of Aß aggregates and demonstrated its potential use as an early diagnostic agent for AD. Overall, this study can provide a general molecular design strategy for developing new zwitterionic ESIPT compounds with NIR emission in vivo imaging applications.

Exploiting Dynamic Vector-Level Operations and a 2D-Enhanced Logistic Modular Map for Efficient Chaotic Image Encryption.

Over the past few years, chaotic image encryption has gained extensive attention. Nevertheless, the current studies on chaotic image encryption still possess certain constraints. To break these constraints, we initially created a two-dimensional enhanced logistic modular map (2D-ELMM) and subsequently devised a chaotic image encryption scheme based on vector-level operations and 2D-ELMM (CIES-DVEM). In contrast to some recent schemes, CIES-DVEM features remarkable advantages in several aspects. Firstly, 2D-ELMM is not only simpler in structure, but its chaotic performance is also significantly better than that of some newly reported chaotic maps. Secondly, the key stream generation process of CIES-DVEM is more practical, and there is no need to replace the secret key or recreate the chaotic sequence when handling different images. Thirdly, the encryption process of CIES-DVEM is dynamic and closely related to plaintext images, enabling it to withstand various attacks more effectively. Finally, CIES-DVEM incorporates lots of vector-level operations, resulting in a highly efficient encryption process. Numerous experiments and analyses indicate that CIES-DVEM not only boasts highly significant advantages in terms of encryption efficiency, but it also surpasses many recent encryption schemes in practicality and security.

Influence of augmentation on the performance of the double ResNet-based model for chest X-ray classification.

Purpose: A pandemic disease elicited by the SARS-CoV-2 virus has become a serious health issue due to infecting millions of people all over the world. Recent publications prove that artificial intelligence (AI) can be used for medical diagnosis purposes, including interpretation of X-ray images. X-ray scanning is relatively cheap, and scan processing is not computationally demanding. Material and methods: In our experiment a baseline transfer learning schema of processing of lung X-ray images, including augmentation, in order to detect COVID-19 symptoms was implemented. Seven different scenarios of augmentation were proposed. The model was trained on a dataset consisting of more than 30,000 X-ray images. Results: The obtained model was evaluated using real images from a Polish hospital, with the use of standard metrics, and it achieved accuracy = 0.9839, precision = 0.9697, recall = 1.0000, and F1-score = 0.9846. Conclusions: Our experiment proved that augmentations and masking could be important steps of data pre-processing and could contribute to improvement of the evaluation metrics. Because medical professionals often tend to lack confidence in AI-based tools, we have designed the proposed model so that its results would be explainable and could play a supporting role for radiology specialists in their work.

The Fears and Hopes of Ukrainian Migrant Workers in Poland in the Pandemic Era.

Due to the COVID-19 pandemic, many immigrants found themselves in extremely unstable situations. The recent contributions show that employment decline in the first several months of the lockdown was higher for migrant workers than for natives. At the same time, migrants were less likely to find new employment in the recovery months. Such circumstances may result in an increased level of anxiety about one's economic situation. On the other hand, an unfavorable environment may induce resources that could help to overcome it. The paper aims to reveal migrants' concerns together with ambitions connected with the economic activity during the pandemic. The study is based on 30 individual in-depth interviews with Ukrainian migrant workers from Poland. The research approach was based on Natural Language Processing techniques. We employed sentiment analysis algorithms, and on a basis of selected lexicons, we extracted fears and hopes that appear in migrants' narrations. We also identified major topics and associated them with specific sentiments. Pandemic induced several matters connected with e.g., the stability of employment, discrimination, relationships, family, and financial situation. These affairs are usually connected on the basis of a cause-and-effect relationship. In addition, while several topics were common for both male and female participants, some of them were specific for each group.

3D Road Lane Classification with Improved Texture Patterns and Optimized Deep Classifier.

The understanding of roads and lanes incorporates identifying the level of the road, the position and count of lanes, and ending, splitting, and merging roads and lanes in highway, rural, and urban scenarios. Even though a large amount of progress has been made recently, this kind of understanding is ahead of the accomplishments of the present perceptual methods. Nowadays, 3D lane detection has become the trending research in autonomous vehicles, which shows an exact estimation of the 3D position of the drivable lanes. This work mainly aims at proposing a new technique with Phase I (road or non-road classification) and Phase II (lane or non-lane classification) with 3D images. Phase I: Initially, the features, such as the proposed local texton XOR pattern (LTXOR), local Gabor binary pattern histogram sequence (LGBPHS), and median ternary pattern (MTP), are derived. These features are subjected to the bidirectional gated recurrent unit (BI-GRU) that detects whether the object is road or non-road. Phase II: Similar features in Phase I are further classified using the optimized BI-GRU, where the weights are chosen optimally via self-improved honey badger optimization (SI-HBO). As a result, the system can be identified, and whether it is lane-related or not. Particularly, the proposed BI-GRU + SI-HBO obtained a higher precision of 0.946 for db 1. Furthermore, the best-case accuracy for the BI-GRU + SI-HBO was 0.928, which was better compared with honey badger optimization. Finally, the development of SI-HBO was proven to be better than the others.

A Robust Deep Learning Framework Based on Spectrograms for Heart Sound Classification.

Heart sound analysis plays an important role in early detecting heart disease. However, manual detection requires doctors with extensive clinical experience, which increases uncertainty for the task, especially in medically underdeveloped areas. This paper proposes a robust neural network structure with an improved attention module for automatic classification of heart sound wave. In the preprocessing stage, noise removal with Butterworth bandpass filter is first adopted, and then heart sound recordings are converted into time-frequency spectrum by short-time Fourier transform (STFT). The model is driven by STFT spectrum. It automatically extracts features through four down sample blocks with different filters. Subsequently, an improved attention module based on Squeeze-and-Excitation module and coordinate attention module is developed for feature fusion. Finally, the neural network will give a category for heart sound waves based on the learned features. The global average pooling layer is adopted for reducing the model's weight and avoiding overfitting, while focal loss is further introduced as the loss function to minimize the data imbalance problem. Validation experiments have been conducted on two publicly available datasets, and the results well demonstrate the effectiveness and advantages of our method.

A smart privacy preserving framework for industrial IoT using hybrid meta-heuristic algorithm.

Industrial Internet of Things (IIoT) seeks more attention in attaining enormous opportunities in the field of Industry 4.0. But there exist severe challenges related to data privacy and security when processing the automatic and practical data collection and monitoring over industrial applications in IIoT. Traditional user authentication strategies in IIoT are affected by single factor authentication, which leads to poor adaptability along with the increasing users count and different user categories. For addressing such issue, this paper aims to implement the privacy preservation model in IIoT using the advancements of artificial intelligent techniques. The two major stages of the designed system are the sanitization and restoration of IIoT data. Data sanitization hides the sensitive information in IIoT for preventing it from leakage of information. Moreover, the designed sanitization procedure performs the optimal key generation by a new Grasshopper-Black Hole Optimization (G-BHO) algorithm. A multi-objective function involving the parameters like degree of modification, hiding rate, correlation coefficient between the actual data and restored data, and information preservation rate was derived and utilized for generating optimal key. The simulation result establishes the dominance of the proposed model over other state-of the-art models in terms of various performance metrics. In respect of privacy preservation, the proposed G-BHO algorithm has achieved 1%, 15.2%, 12.6%, and 1% enhanced result than JA, GWO, GOA, and BHO, respectively.

Malaria Detection Using Advanced Deep Learning Architecture.

Malaria is a life-threatening disease caused by parasites that are transmitted to humans through the bites of infected mosquitoes. The early diagnosis and treatment of malaria are crucial for reducing morbidity and mortality rates, particularly in developing countries where the disease is prevalent. In this article, we present a novel convolutional neural network (CNN) architecture for detecting malaria from blood samples with a 99.68% accuracy. Our method outperforms the existing approaches in terms of both accuracy and speed, making it a promising tool for malaria diagnosis in resource-limited settings. The CNN was trained on a large dataset of blood smears and was able to accurately classify infected and uninfected samples with high sensitivity and specificity. Additionally, we present an analysis of model performance on different subtypes of malaria and discuss the implications of our findings for the use of deep learning in infectious disease diagnosis.