Identification Process Research Articles

Europe between Russia’s self and others: continuity and transformation in Russia’s perceptions of the European Union

ABSTRACT This article aims at unpacking changing Russian attitudes towards Europe and the European Union (EU) over the past two decades. The idea of Europe has been an integral part of Russia’s Self/Other identification process since imperial times. However, over the course of 20-plus years of growing East–West tensions, the meaning of ‘Europeanness’ in the Russian lexicon has changed; with it, Russia’s perception of (relation with) Europe/the EU has shifted, too. By employing a constructivist framework, we argue that such image shift was triggered by crisis episodes, which exposed perception and expectation gaps that, in turn, prompted Russia(ns) to re-evaluate their attitudes towards Europe/the EU – yet not the fundamental pillars of the Russian identity. Notably, the changing dynamics of Russia’s Self/Other identification were accompanied by outbursts of negative emotions, which layered up at each new crisis. This article delves into these processes at both the elite and popular level by drawing on primary narrative sources and rich polling datasets, including the Analysis of the Perception of the EU and of EU’s Policies Abroad.

Exploring the role of organizational identification in the nexus of psychological capital and organizational-oriented citizenship behaviors: evidence from healthcare professionals in India.

The purpose of this study is to explore the mediation of organizational identification (OI) in the relationship between employees' psychological capital (PsyCap) and their engagement in organizational-oriented citizenship behaviors (OCBO). We examine the proposed theoretical model through the lens of the job demand and resources (JD-R) model and social identity theory (SIT), focusing specifically on the healthcare setting. Employing a structured questionnaire, responses were collected from 240 healthcare professionals working in multinational pharmacovigilance firms in select Tier-I Indian cities using purposive sampling. Model testing constituted performing statistical analyses using partial least squares-structural equation modelling (PLS-SEM) in the R environment, an open-source software. Results revealed a significant positive association between PsyCap and OCBO. In addition, we found that OI mediated the relationship between PsyCap and OCBO. This suggests that practitioners can stimulate extra-role performance in employees through enhancing PsyCap and fostering psychological oneness with the organization. The research emphasizes the significant role of employees' personal resources, manifested in the form of PsyCap, in having a bearing on their attitudes and feelings towards the organization. We explore the under-researched latent mechanism of the PsyCap and OCBO relationship through employees' social identification process. In this research, we uncover the underlying mechanism of PsyCap and OCBO association by highlighting the latent role of OI on a unique set of healthcare professionals.

Antimicrobial resistance pattern of the Bacteroides fragilis group strains isolated atateaching hospital in China.

The study was conducted in the microbiology laboratory of the First Affiliated Hospital of Hainan Medical University, Haikou, China, from January 2019 to December 2023. A total of 316 consecutive non-duplicate isolates were collected and identified, that belonged to the Bacteroides fragilis group. Identification of the isolated strains was performed by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). The minimum inhibitory concentration (MIC) of seven antibiotics was determined by agar dilution method. The presence of cfiA, ermF, and nim genes was determined by polymerase chain reaction (PCR). Correlations between the presence of resistance genes and the MIC values of antibiotics were determined using the Pearson correlation coefficient. In the identification process, 214 isolates (67.7%) were identified as B. fragilis, 52 (16.4%) as Bacteroides thetaiotaomicron, 17 (5.4%) as Bacteroides ovatus, 12 (3.8%) as Bacteroides uniformis, 10 (3.2%) as Phocaeicola vulgatus (=Bacteroides vulgatus), 7 (2.2%) as Bacteroides stercoris, and 4 (1.3%) as Parabacteroides distasonis. The presence of cfiA gene moderately correlated with the MIC of imipenem and meropenem (r = 0.34 and r = 0.42, respectively), while resistance to clindamycin and the presence of ermF gene exhibited a very strong correlation (r = 0.72). In the current study, the most active antimicrobial agents against B. fragilis group bacteria were found to be meropenem, imipenem, metronidazole, and piperacillin/tazobactam; however, resistance to clindamycin renders its empirical use inappropriate.

N-Bipolar Soft Expert Sets and Their Applications in Robust Multi-Attribute Group Decision-Making

This paper presents N-bipolar soft expert (N-BSE) sets, a novel framework designed to enhance multi-attribute group decision-making (MAGDM) by incorporating expert input, bipolarity, and non-binary evaluations. Existing MAGDM approaches often lack the ability to simultaneously integrate positive and negative assessments, especially in nuanced, multi-valued evaluation spaces. The proposed N-BSE model addresses this limitation by offering a comprehensive, mathematically rigorous structure for decision-making (DM). Fundamental operations of the N-BSE model are defined and analyzed, ensuring its theoretical consistency and applicability. To demonstrate its practical utility, the N-BSE model is applied to a general case study on sustainable energy solutions, illustrating its effectiveness in handling complex DM scenarios. An algorithm is proposed to streamline the DM process, enabling systematic and transparent identification of optimal alternatives. Additionally, a comparative analysis emphasizes the advantages of the N-BSE model over existing MAGDM frameworks, highlighting its capacity to integrate diverse expert opinions, evaluate both positive and negative attributes, and support multi-valued assessments. By bridging the gap between theoretical development and practical application, this paper contributes to advancing DM methodologies.

Automated Fungal Identification with Deep Learning on Time-Lapse Images

The identification of species within filamentous fungi is crucial in various fields such as agriculture, environmental monitoring, and medical mycology. Traditional identification methods based on morphology have a low demand for advanced equipment usage and heavily depend on manual observation and expertise. However, this approach may struggle to differentiate between species in a genus due to their potential visual similarities, making the process time-consuming and subjective. In this study, we present an AI-based fungal species recognition model that utilizes deep learning techniques applied to time-lapse images. The training dataset, derived from fungi strains in the IBT Culture Collection, comprised 26,451 high-resolution images representing 110 species from 35 genera. The dataset was divided into a training set and validation subsets. We implemented three advanced deep learning architectures—ResNet50, DenseNet-121, and Vision Transformer (ViT)—to assess their effectiveness in accurately classifying fungal species. By utilizing images from early growth stages (days 2–3.5) for training and testing and later stages (days 4–7) for validation, our approach shortens the fungal identification process by 2–3 days, significantly reducing the associated workload and costs. Among the models, the Vision Transformer achieved the highest accuracy of 92.6%, demonstrating the effectiveness of our method. This work contributes to the automation of fungal identification, providing a reliable and efficient solution for monitoring fungal growth and diversity over time, which would be useful for culture collections or other institutions that handle a large number of new isolates in their daily work.

Machine learning of the dynamics of strain hardening based on contact transformations

Abstract Dislocation density-based models offer a physically grounded approach to modeling strain hardening in metal forming. Since these models are typically defined by Ordinary Differential Equations (ODEs), their accuracy is constrained by both, the model formulation and the parameter identification process. Machine Learning (ML) provides an alternative by allowing models to be constructed directly from experimental data, bypassing the accuracy limitations of explicitly defined models. However, applying ML to ODEs introduces the need for novel training techniques. This work presents a new approach for developing neural ODE models for flow curve description, utilizing a contact transformation to simplify the problem of learning an ODE into a learning a multivariate function. Graphical abstract

The First Case of the Identification of a Microorganism Directly from Whole Blood Using MALDI-TOF Mass Spectrometry in an Onco-Hematological Pediatric Patient with Bloodstream Infection

Background: Bloodstream infections affect up to 20% of pediatric cancer patients receiving intensive care, contributing significantly to morbidity and mortality, with infection-related mortality rates reported to be as high as 16%. Methods: The identification of microorganisms directly from whole blood is difficult due to several factors, such as interference from host genomic material, low bacterial load, the endogenous components of whole blood and exogenous substances, which can interfere with the identification process. Nevertheless, rapid microbial diagnosis remains of paramount importance in these patients. Results and Conclusion: Here, we present the first case of bacterial pathogen identification directly from whole blood using MALDI-TOF mass spectrometry in an onco-hematological pediatric patient affected by sepsis and admitted to Bambino Gesù Children’s Hospital (IRCCS) in Rome, Italy.

IMPLEMENTASI YOLOV5 UNTUK DETEKSI KARTU DEBIT: STUDI KASUS PADA KLASIFIKASI BRITAMA DAN SIMPEDES

This study aims to develop an object detection model based on YOLOv5 to classify debit card types. With the advancement of financial technology, the need for automated systems to identify debit cards has become essential to enhance transaction efficiency and security. The research methodology involves five main stages: dataset collection, data preprocessing through labeling and resizing to 640 x 640, dataset augmentation, YOLOv5 model training, and model evaluation. The dataset used consists of three categories of debit cards, with a total of 300 images. The results demonstrate that the YOLOv5 model achieves excellent performance with a mean average precision (mAP) of 92.7% and an object loss value of 0.08. The high mAP value indicates the model’s capability to accurately recognize objects, while the low object loss value reflects minimal detection errors during testing. In conclusion, YOLOv5 has proven to be reliable for application in debit card detection systems. This study provides significant contributions to the development of automation systems in the financial sector, particularly in improving the efficiency and accuracy of identification processes. It is hoped that this research will serve as a foundation for further studies with broader datasets, the application of more advanced augmentation techniques, and the utilization of more sophisticated hardware to enhance model performance.

Molecular characterization of rice blast fungus (Pyricularia oryzae) from West Sumatra and their virulence to several rice cultivars

Global rice production is severely impacted by rice blast disease, which is a devastating condition caused by the fungal pathogen Pyricularia oryzae. Many countries have reported blast diseases associated with rice yield loss. In Indonesia, this disease also destroys rice production in several rice fields in West Java, East Java, Central Java, Sumatra, and Sulawesi. The objectives of this study were to identify rice blast fungal isolates in West Sumatra and assess the resistance level of eight local and modern rice genotypes to this disease. Additionally, the rice genotypes were also evaluated for the presence of Pi genes associated with blast resistance by utilizing 25 simple sequence-repeats (SSR) markers known to be linked to rice blasts. The seedlings of all rice genotypes were exposed to fungal isolates to determine their blast resistance capacity based on leaf morphological traits. Before conducting blast resistance testing, the local isolates of rice blast fungus were molecularly identified by aligning sequences in a pairwise comparison. Based on the identification process, three P. oryzae (MoK19-49, MoK19-45, and MoK19-28) fungal isolates were identified. The confirmed P. oryzae isolate MoK19-28 was then used to assess the blast resistance of the rice genotypes using the SES-blast test. The resistance levels of all genotypes were evaluated by observing the morphology of infected leaves for 24 days. Four genotypes, namely Cantik Manis (CM), Bungo Sungkai (BS), Inpari 48 Blas (IB), and Inpago 9 (Ip9), exhibited resistance to leaf blast caused by P. oryzae isolate MoK19-28. Meanwhile, Pulau Batu (PB), Mundam Putiah (MP), and IR64-Sub1 (IR) displayed intermediate resistance to the disease. In contrast, the blast fungus P. oryzae isolate MoK19-28 severely impacted the Kuriak (K) genotype. The rice variety IB contains several Pi genes, specifically Pi1, Pi9, Pikh, and Pi37, which are associated with enhanced resistance to leaf blast caused by P. oryzae isolate MoK19-28. This genetic characteristic sets it apart from susceptible genotypes. This work has successfully identified multiple blast-resistant rice genotypes that could potentially be used in future rice improvement initiatives.

Progress in Aptamer Research and Future Applications.

Aptamers are short, single-stranded DNA, RNA or synthetic XNA molecules that bind to target molecules with high specificity and affinity. These intrinsically structured RNA or DNA oligonucleotides are not only substitutes for antibodies, but also show great potential for applications in diagnostics, specific drug delivery, and treatment of certain diseases. While the process of aptamer identification and its core functional mechanism known as systematic evolution of exponentially enriched ligands (SELEX), SELEX involves a number of single processes, each contributing to the success or failure of aptamer generation. Today, aptamers are widely used to facilitate basic research discoveries and clinical diagnostics. In addition, aptamers play a promising role as clinical diagnostic and therapeutic agents. This review provides recent advances in this rapidly growing field of research, with special emphasis on aptamer generation and screening, small molecule aptamers, the development of aptamer applications, and applications in clinical medicine. And it also discusses the problems that still exist today with aptamers.

Preparation of Novel ACE Inhibitory Peptides from Skimmed Goat Milk Hydrolyzed by Multi-Enzymes: Process Optimization, Purification, and Identification

This study optimizes the process conditions for preparing angiotensin-converting enzyme (ACE) inhibitory peptides from skimmed goat milk (SGM) hydrolyzed by multi-enzymes using response surface methodology. When the enzymatic hydrolysis time was 90 min, the optimal hydrolysis conditions were a pH of 8.49, enzyme-to-substrate ratio (E/S ratio) of 8.04%, and temperature of 61.54 °C. The hydrolysis degree and ACE inhibitory activity were 65.39% ± 0.01% and 84.65% ± 0.03%, respectively. After purification by ultrafiltration, macroporous resin, and gel filtration, the ACE inhibitory activity of F2-2 in the two components of F2 was higher, with the ACE inhibitory rate of 93.97% ± 0.15% and IC50 of 0.121 ± 0.004 mg/mL. The content of hydrophobic amino acids, fatty amino acids, and aromatic amino acids in component F2-2 accounts for 73.17%, 33.86%, and 33.72%, respectively. Eleven peptides were isolated and identified from the F2-2 components of the enzymatic hydrolysate of SGM, including two peptides without an established database. The peptides mainly came from β casein, αS1 casein, and αS2 casein.

Dibenzothiophenium Salts: Practical Alternatives to Hypervalent I(III)-Based Reagents.

ConspectusDuring the past few years, the interest among organic synthesis practitioners in the use of sulfonium salts has exponentially growth. This can arguably be attributed to a series of specific factors: (a) The recent development of more direct and efficient protocols for the synthesis of these species, which make sulfonium reagents of a wide structural variety easily available in multigram scale. (b) The recognition that the reactivity of these salts resembles that of hypervalent iodine compounds, and therefore, they can be used as effective replacement of such species in most of their applications. (c) Their intrinsic thermal stability and tolerance to air and moisture, which clearly surpass that of I(III)-reagents of analogue reactivity, and facilitate their purification, isolation as well-defined species, storage, and safely handling on larger scale. (d) Finally, the possibility to further functionalize sulfonium salts once the sulfur-containing platform has been incorporated. Specifically, this last synthetic approach is not trivial when working with hypervalent I(III)-species and facilitates the access to sulfonium salts with no counterpart in the I(III) realm.This renewed interest in sulfonium salts has led to the improvement of already existing transformations as well as to the discovery of unprecedented ones; in particular, by the development of protocols that incorporate sulfonium salts as partners in traditional cross-coupling and C-H activation steps or combine them with more modern technologies such as photocatalysis or electrosynthesis. In this Account, the reactivity of a series of sulfonium salts originally prepared in our laboratory will be outlined and compared to their I(III)-counterparts. Some of these reagents are now commercially available, and their use has started to spread widely across the synthetic chemistry community, helping to speed the process of identification of potentially bioactive products or new functionaliced materials. However, challenges still remain. The development of sulfonium reagents characterized by an optimal balance between reactivity and site-selectivity, or showing broader compatibility toward sensitive functional groups is still a need. In addition, the intrinsic stability of sulfonium salts often makes necessary the use of (sophisticated) catalysts that activate the latent reactivity hidden in their structures. Although a priori one can see this fact as a disadvantage, it might actually be decisive to harvest the full synthetic potential of sulfonium salts because their thermal stability will surely facilitate the preparation of operational reagents with no counterpart in the context of I(III)-chemistry. If this becomes true, sulfonium salts may contribute to the expediting of retrosynthetic disconnections that, to date, are impossible.

Impact of thermal exposure on deciduous teeth: A comparative analysis with permanent teeth.

Children are particularly vulnerable to fatalities from fire incidents, and the crucial role of dentition in the identification processes is well known. Despite this, research on burned deciduous teeth is lacking, often assuming similarities with burned permanent teeth, disregarding their morphological and chemical distinctions. This goal of this study was to investigate the effect of burning on deciduous teeth, focusing on post-burning tooth color assessment. Sixty deciduous teeth were divided into two temperature groups (400 °C and 700 °C), each further subdivided into three burn time subgroups. Data from a prior study on permanent maxillary molars was also included for comparison. Using quantitative color analysis, differences in color measurements were observed between unburned and burned deciduous teeth, as well as between burn times, at both temperatures. Crown and root color variations were significant between temperatures for all burn times, however, no significant differences in color were noted between various burn times. When comparing deciduous and permanent teeth burned at 400 °C, the sole significant difference was in crown color. Contrarily, for teeth burned at 700 °C, the only significant difference was in root color. Graphical analysis indicated comparable color changes between post-burn deciduous and permanent teeth, despite the statistical differences. Significantly, all permanent teeth subjected to 700 °C demonstrated extensive enamel fragmentation, whereas such damage occurred in only half of the deciduous teeth samples. These observations indicate that despite similar color changes in deciduous and permanent teeth upon heating, variations in fragmentation patterns suggest that differences in morphology and microstructure influence the resilience of teeth to thermal exposure.

Fatty acid profiles and tolerance to temperature extremes in Daphnia pulex.

Identification of physiological processes setting thermal tolerance limits is essential to describing adaptive response to temperature changes. We used the North American Daphnia pulex complex, which makes a remarkable model for comparative physiology as it is composed of clones differing in heat tolerance and ploidies, and with a wide geographic distribution. The fatty acid composition of 18 diploid and triploid D. pulex clones acclimated to 16°C and 24°C was measured and compared with their tolerance to extreme high and low temperatures (CTmax and CTmin, respectively). Eicosapentaenoic acid (EPA) relative content showed a strong negative relationship with CTmax and a clear association with CTmin. Higher unsaturation and peroxidation indices were associated with better cold tolerance, whereas saturated fatty acids and monounsaturated fatty acids were associated with lower cold tolerance. Triploid D. pulex clones accumulated more EPA and had lower CTmin than diploid clones (better cold tolerance). Triploid clones retained more omega-6 polyunsaturated fatty acids at high temperature. CTmax was positively correlated with CTmin, suggesting the existence of important constraints in temperature tolerance caused by fatty acid composition.

Identification of salinization processes in coastal aquifers using a fuzzy logic and data mining based methodology: study case in a Mediterranian coastal aquifer (Spain)

In coastal aquifers, the seawater intrusion can mask the effects of salinity regional groundwater flows, connate waters mobilization or contaminant process. Therefore, to discriminate between all the processes that have taken place in the coastal aquifer, is a complex task. Normally, traditional hydrogeochemical methods (e.g., Piper and Durov) together with statistical multivariate techniques (e.g., cluster and factorial analysis) and other methods (e.g., ionic deltas and isotopic studies) have been used to understand the hydrogeochemistry of aquifers and to confirm previous hypothesis. This paper presents a characterization of the salinization process in coastal aquifers, by means a fuzzy logic and data mining based methodology, which has not been used before for this purpose in a coastal aquifer. The proposed fuzzy methodology is based on the use of the data mining computer tool Predictive Fuzzy Rules Generator (PreFuRGe). The results have been obtained by processing groundwater samples analyses with PreFuRGe. The parameters used for the experimentation have been: temperature, electric conductivity, redox potential, total dissolved solids, silicon dioxide, oxidability, major ions (chloride, sulphate, bicarbonate, nitrate, calcium, magnesium, sodium and potassium), and minor ions (arsenic, bromide, lithium, boron, strontium, chromium and fluoride). The application of this method has made it possible to differentiate several overlapping hydrogeochemical processes, such as seawater intrusion, the entry of high salinity regional groundwater flows with high concentrations of strontium, magnesium, lithium and sulphates, and the effect of contamination from agricultural activities, with the presence of nitrates. The qualitative obtained results in this paper have been compared to previous research carried out in the same coastal aquifer, and it is proved that the used fuzzy methodology is a powerful tool for discriminating between overlapping geogenic and anthropogenic processes in coastal aquifers.

Enhancing overall performances of large constructed wetlands using an integrated approach of numerical simulation and response surface method--Taking Bagong hybrid CWs as an example.

Constructed wetlands (CWs) with low carbon properties represented an effective approach for treating low-polluted water and improving water quality. Here, a research scheme was proposed to achieve maximum operation benefits of the large-scale CWs through parameter identification, operation simulation, evaluation, and analysis of the water quality process. Based on the two-dimensional water hydrodynamic model coupling with the Eco-Lab water quality module (with nutrients), simulation for Bagong hybrid CWs was successfully conducted. Variables were estimated using response surface design (RSD), and the values of model parameters were calibrated in accordance with central combination design (CCD) experiments. Ten key parameters in the water quality process affecting the overall pollutant removal of CWs were identified, and the water quality model of the CWs could be replicated well with the actual situation within the 95% confidence interval. The water purification effect of the CWs under five different influent flow conditions was simulated, and the maximum hydraulic loading (0.036m3·(m-2·h-1)) of the CWs was determined with the effluent water quality to meet the design requirements. The integrated method could effectively enhance the efficiency of parameter calibration and significantly reduce the simulation workload. The results of pollutants migration and transformation under different influent conditions could provide a universally applicable and valid method in engineering applications, and process a promising application prospect.

Abstract TP336: Neuropathological changes in the chronic phase of ischemic stroke are marked by persistent alteration of microglial function and neurodegenerative phenotypes

Improvements in acute stroke treatment, including EVT and critical care management, have increased survival rates post-stroke. However, many stroke survivors have significant post-stroke disability and cognitive impairment. Despite this, the chronic progression and long-term sequelae of ischemic stroke pathology remains understudied. Methods: We examined the chronicity of neurobehavioral recovery and progressive neuropathology in young male (3 month) and middle-aged male and female (14 month) C57Bl/6 mice at 3 and 6 months after a 60-minute transient middle cerebral artery occlusion (tMCAO) or sham surgery ( n = 10-20/group ). Behavioral testing included NORT, fear conditioning (FC), and tail suspension. Immunohistochemistry (IHC) was performed on mouse brains, as well as post-mortem human brain samples from patients with a chronic infarct ( n = 6/group ), to assess demyelination (MBP), neuronal apoptosis (TUNEL/NeuN), and amyloid burden (Aβ-42). Flow cytometry was performed on the ipsilateral mouse brain hemisphere. RNA isolated from the ipsilateral hemisphere was sent for gene profiling. Results: In both young and middle-aged mice, depressive-like behavior persisted for 6 months post-stroke (PS) (p= <0.0001), while cognitive function progressively worsened from 3 to 6 months, as seen by a reduction in the NORT (p=.0063) and FC (p=.0084). MRI imaging and IHC revealed cerebral atrophy, significant demyelination (mouse, p=.0361; human, p =.0001), and increased microglial density (p=.0059) in the hippocampus of both human and mice. TUNEL/NeuN staining showed ongoing neuronal degeneration in the human hippocampus, distal to the chronic infarct. Human chronic stroke hippocampus had increased amyloid burden compared to age-matched controls (p=.0677). Cytometric anyalses revealed disease-associated microglial (DAM) phenotypes were marked by increased senescence, cytokine production, and an altered redox state. Transcriptomic data showed significant chronic upregulation of DAM genes. Conclusion: Our findings demonstrate that ischemic stroke accelerates inflamm-aging and premature senescence within the chronic infarct microenvironment. Cellular senescence and chronic disease signatures may contribute to progressive cognitive impairment post-stroke. Identification of secondary injury processes, and late pathological events post-stroke, allude to a neurodegenerative etiology and may offer viable targets for delayed treatment to prevent further neurological decline.

FIND BEAR: An explainable artificial intelligence platform for stages automatic recognition in pool boiling

Pool boiling heat transfer plays a critical role as a high heat flux thermal control technology in a variety of scientific and application scenarios. However, automatic identifications of pool boiling stages with non-invasive technology achieving outstanding performance is barely reported. In this paper, we develop a novel artificial intelligence (AI) platform called Boiling-stages Explainable and Automatic Recognition (BEAR) and integrate it into our own FIND Multiphysics software. Inspired by the top-down and bottom-up attentional control mechanism of the human brain, BEAR aims to mimic the cognitive and discriminative processes of human experts while giving full play to the computational advantages of personal computers. This aim is decomposed into three goals and realized by the corresponding modules forming the architecture. BEAR is effective with an identification accuracy of up to 99.1 %. It is cost-efficient as the training time of the best-performing model embedded is 9.06 s with the model’s size of 65kB. It takes only about 0.048 ∼ 0.197 s per image to perform the entire automated identification process on a personal computer with an 8-core processor. Even a small dataset with low spatial and temporal resolution, comprising 33 images of 256 × 256 pixels, can yield results that are comparable to state-of-the-art literature in the field. Moreover, BEAR provides a platform with the scalability to carry a variety of algorithms for different application purposes, which can be beneficial in facilitating innovative modeling and further deployment in future application devices.

Deep Learning-Based Image Processing for Skin Disease Identification

Millions of people around the world suffer from skin diseases, and accurate and timely diagnosis plays a key role in their proper treatment. This paper introduces a skin disease detection system by applying the U-Net algorithm for image segmentation while comparing and combining two other machine learning algorithms for better diagnosis accuracy. The input images obtained through user engagement undergo preprocessing to eliminate noise and artifacts, enhancing further segmentation and classification. The system classifies the diseases, for example, melanoma, eczema, and psoriasis, by performing feature extraction using the segmented region of interest. The strength of generalization is guaranteed based on a rich image dataset used for training and evaluation, and the comparative analysis brings out the superiority of the U-Net in terms of segmentation accuracy, and insights into effectiveness of other alternatives. The output is a disease classification which is easy to interpret and that can lead to early diagnosis along with the proposal of treatment, and this work highlights the application of deep learning and image processing in dermatology to assist medical professionals and bring diagnostic tools within reach

Multi-Modal product recognition in retail environments: Enhancing accuracy through integrated vision and OCR approaches

This research presents a transformative artificial intelligence solution that addresses critical operational challenges in modern retail environments. Our integrated system combines advanced computer vision and text recognition technologies to automate product identification, inventory tracking, and checkout processes. In response to the retail industry's pressing needs for automation amid labor shortages and rising operational costs, we developed and implemented a comprehensive solution that demonstrates significant business value. The system achieved a 94.6% accuracy rate in product recognition while processing 50-60 items per second, enabling real-time inventory management and automated checkout capabilities. Field testing across multiple retail locations showed a 35% reduction in inventory management time, a 40% decrease in checkout wait times, and a 25% improvement in stock accuracy. The solution encompasses a robust dataset of 538 distinct products, including challenging categories such as liquor bottles and grocery items, and features sophisticated optimization techniques that ensure consistent performance in diverse retail environments. Implementation of this system can lead to substantial operational cost savings, enhanced customer experience, and improved inventory accuracy. Our research demonstrates how AI-driven automation can address the retail industry's current challenges while providing a scalable foundation for future innovations in retail operations management.