Similar Color Research Articles

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.

Color-map recommendation for MR relaxometry maps.

To harmonize the use of color for MR relaxometry maps and therefore recommend the use of specific color-maps for representing , , and maps and their inverses. Perceptually linearized color-maps were chosen to have similar color settings as those proposed by Griswold et al. in 2018. A Delphi process, polling the opinion of a panel of 81 experts, was used to generate consensus on the suitability of these maps. Consensus was reached on the suitability of the logarithm-processed Lipari color-map for and the logarithm-processed Navia color-map for and . There was consensus on color bars being mandatory and on the use of a specific value indicating "invalidity." There was no consensus on whether the ranges should be fixed per anatomy. The authors recommend the use of the logarithm-processed Lipari color-map for displaying quantitative maps and maps; likewise, the authors recommend the logarithm-processed Navia color-map for displaying , , , and maps. This work originated with the Quantitative MR Study Group of the International Society of Magnetic Resonance in Medicine (ISMRM); it has the approval of the Publication Committee and of the Board of the ISMRM.

Frankfurters Manufactured with Valorized Grape Pomace as a Substitute of Nitrifying Salts

This study investigated the use of grape/wine pomace as a potential substitute for nitrifying salts in the production and preservation of frankfurters. Red wine pomace (RWP) from Tempranillo and white wine pomace (WWP) from Cayetana grapes were added to frankfurters made with Iberian pig backfat—an underutilized fat rich in oleic acid—at two levels (0.5% and 3% w/w). These new formulations were compared with a control (containing only meat, salt, and spices) and a commercial formulation containing nitrites and ascorbic acid. Analyses were conducted immediately after production and following 45 days of refrigerated storage to evaluate microbiological, color, physicochemical, and textural changes in the frankfurters. The addition of pomace slightly reduced the pH of the frankfurters but did not affect microbial counts during the manufacturing process. Frankfurters with pomace displayed a similar color to the control but showed lower redness compared to the commercial formulation with nitrites. Importantly, pomace reduced lipid and protein oxidation during production and storage. The reduction in lipid oxidation due to the pomace was comparable to the effect of nitrites and ascorbic acid. Furthermore, pomace effectively reduced protein oxidation, unlike nitrites and ascorbic acid, which primarily targeted lipid oxidation. Significant differences in texture were observed between commercial frankfurters and those containing pomace. Despite these variations in the appearance and the texture, the strong protective effect of pomace against oxidative reactions highlights its potential as a natural alternative to synthetic additives, offering a promising solution for the meat industry.

Apple Disease Detection and Classification using Random Forest (One-vs-All)

Fruit diseases detection and recognition are a common problem worldwide. Fruit Disease detection is a hot topic among researchers and is very complex due to structure and color similarity factors. In this research we proposed a new model to detect and classify the apple disease with the help of digital image processing and machine learning. First, image processing techniques were applied to enhance the image contrast and remove the noise, which helped to segment the region of interest accurately also help to extract feature without garbage data. Then K-means clustering technique with fuzzy C-mean method was implemented to segment the images. GLCM feature extraction was used after the segmentation. Real images of apple disease with multi-disease regions were used in the research method. These features were preprocessed with methods like LDA. K-Fold cross validation was used for training and testing, with combination of random forest machine learning method. The result showed high accuracy with comparison of existing techniques.

Authentication of Java Turmeric (Curcuma xanthorrhiza) from Turmeric (Curcuma longa) Using a Combination of UV-VIS-IR Spectrum and Chemometrics

Java turmeric (Curcuma xanthorriza) and turmeric (Curcuma longa) show similar colors, so they have the potential to be adulterated with each other, especially if they are presented in powder. This research aims to develop an analytical method for authenticating both types of samples with adulterant concentrations of 0.01% w/w and 0.005% w/w for the infrared range and 0.5 μg/g and 1 μg/g for the UV-Vis range. The pure sample was extracted for 40 minutes with 1:10 ethanol using ultrasonication. The extract was then concentrated using a rotary evaporator and freeze dryer. Adulterant samples were prepared by mixing both types of extracts. The absorption of the solution was measured at a wavelength of 200–800 nm and a wave number of 4000–400 cm-1. Multivariate analysis using partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA) was performed on the spectra. PLS-DA has not been able to authenticate adulterated samples. However, SIMCA analysis can detect differences between pure curcuma and adulterated samples in the infrared range until a concentration of 0.005% w/w, while it can only authenticate correctly in the UV-Vis range until a 1 μg/g concentration.

Two new species of Trigonidiidae (Orthoptera: Grylloidea) from southwest China

Two new species of Trigonidiinae (Orthoptera: Grylloidea; Trigonidiidae), Trigonidium albarum sp. nov. and Svistella prominens sp. nov., were discovered in Sichuan Province, China. T. albarum sp. nov. has a similar coloration to Trigonidium rubrumoculum He & Ma, 2022 and can be distinguished by different ectoparamere; while S. prominens sp. nov. and Svistella anhuiensis He, Li & Liu, 2009 have similar morphological characteristics and are distributed in the same area, but the new species is characterized by a dark yellow body and a semicircular protrusion on the lateral lobe of epiphallus. This article presents descriptions and illustrations of the new species.

SP-YOLO: A Real-Time and Efficient Multi-Scale Model for Pest Detection in Sugar Beet Fields.

Beet crops are highly vulnerable to pest infestations throughout their growth cycle, which significantly affects crop development and yield. Timely and accurate pest identification is crucial for implementing effective control measures. Current pest detection tasks face two primary challenges: first, pests frequently blend into their environment due to similar colors, making it difficult to capture distinguishing features in the field; second, pest images exhibit scale variations under different viewing angles, lighting conditions, and distances, which complicates the detection process. This study constructed the BeetPest dataset, a multi-scale pest dataset for beets in complex backgrounds, and proposed the SP-YOLO model, which is an improved real-time detection model based on YOLO11. The model integrates a CNN and transformer (CAT) into the backbone network to capture global features. The lightweight depthwise separable convolution block (DSCB) module is designed to extract multi-scale features and enlarge the receptive field. The neck utilizes the cross-layer path aggregation network (CLPAN) module, further merging low-level and high-level features. SP-YOLO effectively differentiates between the background and target, excelling in handling scale variations in pest images. In comparison with the original YOLO11 model, SP-YOLO shows a 4.9% improvement in mean average precision (mAP@50), a 9.9% increase in precision, and a 1.3% rise in average recall. Furthermore, SP-YOLO achieves a detection speed of 136 frames per second (FPS), meeting real-time pest detection requirements. The model demonstrates remarkable robustness on other pest datasets while maintaining a manageable parameter size and computational complexity suitable for edge devices.

Influence of pretreatment methods and addition of fragrant plants on <i>Gnetum gnemon</i> var <i>tenerum</i> powder tea

Background: Enhancing the utility of fresh vegetables, such as Liang leaves, is commonly achieved through drying and powdering as functional ingredients and food. In a preliminary study, Liang leaves displayed similar color, appearance, and texture attributes to Japanese and Chinese teas, but the flavor of the Liang drink significantly diminished. This research highlights the potential of the highly sought-after gastronomic vegetable, Gnetum gnemon var tenerum, as Liang, a Thai indigenous vegetable, but there is still a shortfall of scientific documentation. This work introduced the antioxidants of Liang leaves to create a new health-impact drinking product. Objective: The research aimed to study the improvement of antioxidant activity and flavor using pretreatments, including withering, steaming, rolling, and steamed-rolling, and following the addition of fragrant plants. Method: Liang leaves were pretreated by withering, steaming, rolling, and steaming rolling before drying. Then, the best condition was selected to add fragrant plants. Results: Withering gave the highest total polyphenol content at 26.95±0.47 mg GAE/g DW compared with the other treatments (p<0.05), while higher DPPH scavenging activity was noticed in the steaming-rolling treatment (p<0.05). However, sensory evaluation results were not significantly different (p>0.05) in each pretreated tea. Steaming-rolling was selected based on the high antioxidant activity for further fragrance improvement using the powder of Pandanus amaryllifolius Roxb leaves and Chrysanthemum indicum L flowers at various ratios. The highest sensory score was recorded in tea made from a mixture of Liang leaf powder, P. amaryllifolius leaf powder, and C. indicum flower powder at a ratio of 60:0:40. The TPC and DPPH scavenging activities of the mixed tea powders were tested, with results of 25.08±0.93 mg GAE/g DW and 11.70±0.26 mg GAE/g DW, respectively. In addition, this product could be a good source of TPC and antioxidant activity judged by ascorbic acid as 332.33±6.94 mg AE / g DW. Conclusion: The steaming-rolling treatment of Liang powder had the highest antioxidant activity, and then adding Chrysanthemum indicum L flowers powder at a ratio of 60:40 improved flavor, total polyphenol content, and antioxidant activity. The Thai indigenous vegetable, Liang leaves powder, is aimed for a novo alternative drink promoting health benefits. Keywords: Liang leaves, Tea, Pretreatment, Flavor, Antioxidant activity

A hyperspectral stealth material design method based on the composition and mixing spectral feature of desert soil

In this study, we used desert soil from Gansu, China, as a sample to propose a method for designing hyperspectral stealth coatings against desert soil backgrounds within the spectral range of 400–2500 nm, and the corresponding coating was prepared. Firstly, the correlation between the composition and typical spectral detected characteristics of the desert soil was systematically analyzed. It was found that the color and the spectrum of the desert soil in the range of 400–1000 nm were influenced by different types of iron oxides. The main spectral characteristic and reflection intensity at 1000–2500 nm were impacted by quartz and montmorillonite. Subsequently, the design method for hyperspectral stealth coatings was developed by analyzing the differences in spectral and structural characteristics between the coatings and the soil. The prepared coating exhibited similar color and spectral shape to the soil in the range of 400–1000 nm, with comparable spectral features around 1414 nm, 1915 nm, 2212 nm, 2250 nm, and 2346 nm. The correlation coefficient and the spectral cosine angle between the reflectance spectra of the coating and the soil within the 400–2500 nm wavelength were calculated to be 0.989 and only 0.05 radians, respectively. The effectiveness of the coating in achieving excellent camouflage against the desert soil background was confirmed through the analysis of multispectral images and thermal infrared temperature. This study holds significant importance for the application of hyperspectral stealth techniques in desert soil scenarios.

Flower Classification Using LIBS Combined with PCA Chemometrics

Classification of flowers is required to promote their appreciation, value, and application. In this study, laser-induced breakdown spectroscopy (LIBS) was applied in conjunction with principal component analysis (PCA) to identify and classify flower species. Three flowers of similar color or shape were selected as the experimental samples. Laser pulses were directed at the sample surface to form plasma, from which light was collected, and the spectra were analyzed to identify the surface material. The spectral data of 240 groups of experimental samples were obtained using LIBS. Spectral data were pretreated, and the 28 characteristic spectral intensity ratios of six characteristic elements (Na, Mg, Al, K, Si, Ca) of flower species after normalization were chosen to constitute a matrix (240×28) of spectral data. The data were compressed, after which their dimensionality was reduced to reduce the number of output variables of the model for PCA from 28 to 3. Finally, cluster analysis of the flowers was performed, and a standard sample database of flowers was established. Subsequently, a template matching model (TMM) was used as a model for training. The accuracy of the entire TMM model was 97%.

Acquisition of similar properties by filters in the same stream of a multistream convolutional neural network

Functional modular organization is observed in a variety of cortical areas in the brain. In the visual cortex of primates, adjacent neurons often respond to the same visual submodality, such as color or orientation, and have a similar preferred orientation or preferred color. However, it remains unclear why functional modular organization emerges in the cerebral cortex. In the present study, I constructed and trained a multistream convolutional neural network to examine whether filters in the same stream acquire similar properties. Although filters in the same stream were able to develop any structures, they acquired similar degrees of orientation and color selectivity and preferred similar orientations and colors. The deletion of filters in a single stream that had similar degrees of stimulus selectivity resulted in larger decreases in classification accuracy than the deletion of those that did not. By contrast, the deletion of filters in a single stream that shared a preferred stimulus resulted in similar decreases in classification accuracy to the deletion of those that did not. Together, these findings suggest that filters with similar degrees of stimulus selectivity in the same stream are required for optimal task performance of the multistream convolutional neural network, and probably of the brain.

Effect of whitening pens on hydrogen peroxide permeability in the pulp chamber, color change and surface morphology.

To evaluate the hydrogen peroxide (HP) permeability into the pulp chamber, color change, and surface morphology promoted by different whitening pens. Fifty premolars were divided into five groups (n = 10): untreated control; Colgate Optic White Express Whitening Pen (OE-C), Colgate Optic White Overnight Whitening Pen (OW-C), Equate Teeth Whitening Kit (TK-E) and Zimba Teeth Whitening Pen (TW-Z), applied daily for 15 minutes over ten days. HP permeability (μg/mL) was measured using UV-VIS spectroscopy, and color change (ΔEab, ΔE00, and WID) by a digital spectrophotometer at baseline (T0), after one (T1), five (T5) and ten days (T10). Initial HP concentration (%), pH and surface morphology were determined through titration, pH meter, and scanning electron microscopy, respectively. Statistical analysis included one-way and two-way ANOVA with Tukey's and Dunnett's tests (α = 0.05). OE-C and OW-C exhibited higher pH, lower HP concentrations, reduced HP penetration (p < 0.05), and similar surface morphology pattern compared to TK-E and TW-Z. Bleaching efficacy showed no significant differences among the whitening pens (p > 0.05). For ΔEab and ΔE00, OE-C and OW-C showed similar color changes across all time points, whereas the TK-E and TW-Z exhibited greater changes from T0 to T5 and T0 to T10 (p < 0.05). WID values significantly increased at T5 and T10 across all groups (p < 0.05). Whitening pens vary in pH, initial hydrogen peroxide concentration, and hydrogen peroxide penetration; however, these differences do not significantly impact their surface morphology and bleaching effect. The use of whitening pens appears promising for achieving effective teeth bleaching after five days of application. However, the detection of hydrogen peroxide within the pulp chamber may increase the risk of tooth sensitivity.

Analyzing Color Difference of Digital Cameras Under Different Color Temperatures

Purpose : Dental clinics had a variety of lighting conditions, which could affect digital photography. Digital Single-Lens Reflex Camera (DSLR) was usually used to take intraoral photos. Recently, there has been a growing trend to use smart-phones with the advancement of technology. The aim of this study was to analyze the differences of photographs taken with digital and smartphone cameras under different color temperature conditions.Materials and Methods : Three types of camera equipment were used: a DSLR (EOS 800D; Canon, Tokyo, Japan) with a macro lens (EF 100mm f/2.8 Macro USM, Tokyo, Japan) and diffuser (Lumilab, Gimpo, Korea) and two types of smart-phones; Galaxy (Samsung, Suwon, Korea) and iPhone (Apple, California, USA) with Smile line MDP (Smile line SA, St-lmier, Switzerland). All photographs were taken 30 times for every 500°K color temperature from 3500°K to 6000°K. Color information was extracted from the central part of the tooth using image editing software (Adobe Photoshop CS4, Adobe, California, USA). ΔE value was calculated by this formula based on CIE L*a*b* color systems. All statistical analyses were performed using SPSS (version 23, SPSS, Chicago, USA), with a significance level of 0.05.Results : In this study, DSLR showed the highest ΔE value followed by iPhone and Galaxy. DSLR showed minimum ΔE value in 4O00°K, iPhone and Galaxy showed in 4500°K. The lowest L* value was DSLR, followed by iPhone and Galaxy. The highest a* value was DSLR, followed by iPhone and Galaxy, except for 4000°K. The lowest b* value was DSLR, followed by Galaxy and iPhone. So DSLR was the darkest, reddest, and bluest of the three equipment.Conclusion : Although there was a significant difference from the actual shade, Galaxy showed the most similar color. Because each device showed different results, it was essential to consider the characteristics of each imaging device.

Sorghum Spike Detection Method Based on Gold Feature Pyramid Module and Improved YOLOv8s

In order to solve the problems of high planting density, similar color, and serious occlusion between spikes in sorghum fields, such as difficult identification and detection of sorghum spikes, low accuracy and high false detection, and missed detection rates, this study proposes an improved sorghum spike detection method based on YOLOv8s. The method involves augmenting the information fusion capability of the YOLOv8 model’s neck module by integrating the Gold feature pyramid module. Additionally, the SPPF module is refined with the LSKA attention mechanism to heighten focus on critical features. To tackle class imbalance in sorghum detection and expedite model convergence, a loss function incorporating Focal-EIOU is employed. Consequently, the YOLOv8s-Gold-LSKA model, based on the Gold module and LSKA attention mechanism, is developed. Experimental results demonstrate that this improved method significantly enhances sorghum spike detection accuracy in natural field settings. The improved model achieved a precision of 90.72%, recall of 76.81%, mean average precision (mAP) of 85.86%, and an F1-score of 81.19%. Comparing the improved model of this study with the three target detection models of YOLOv5s, SSD, and YOLOv8, respectively, the improved model of this study has better detection performance. This advancement provides technical support for the rapid and accurate recognition of multiple sorghum spike targets in natural field backgrounds, thereby improving sorghum yield estimation accuracy. It also contributes to increased sorghum production and harvest, as well as the enhancement of intelligent harvesting equipment for agricultural machinery.

Effects of Seed Colour and Regulated Temperature on the Germination of Boswellia pirottae Chiov.: An Endemic Gum- and Resin-Bearing Species.

(1) Background: According to the IUCN, Boswellia pirottae is classified as a vulnerable species. However, knowledge of its seed characteristics and germination behaviour is lacking. (2) Methods: The aim of this research was to characterise the seeds and evaluate the effects of seed colour and controlled temperatures on seed germination. The seeds were segregated into the following colour categories: light brown (LB), brown (B), and dark brown (DB). The seeds were evaluated under controlled constant temperatures (23 °C) and at room (fluctuating) temperature independently. One-way ANOVA, t-test, and germination indexes were used for analyses. (3) Results: The results showed significant differences in the mean seed masses of LB, B, and DB seeds. Similarly, the differently coloured seeds varied in their water imbibition rates. The result showed significant differences in the mean germination of the seeds in both the controlled temperature (23 °C) and room-temperature chambers among the LB, B, and DB seeds. However, the t-test revealed no significant differences in the mean germination of the seeds of similar colours between controlled temperature and room temperature conditions. (4) Conclusions: The seed's colour significantly influenced the seed mass, water imbibition capacity, and germination rate relative to the temperature treatment. Dark brown seeds are recommended for seed collection aimed at seedling propagation.

Leveraging Deep Transfer Learning for Precision in Similar Color and Texture-Based Fruit Classification

Worldwide, the most enormously produced fruits, including bananas, papayas, mangoes, and guavas, are found in India. Over the years, agricultural production in India has consistently increased. There is still a massive gap between per capita demand and supply due to losses, including post-harvest. With adequate processing facilities, a clear scope exists to reduce this post-harvest wastage. In recent years, research in cutting-edge technology like computer vision (CV), Artificial Intelligence, and image processing has played an important role in sorting as well as grading fruits. Fruits in similar colors and textures increase the difficulty of identification. Deep learning networks are used to adapt and recognize complex patterns, especially in visual tasks. Utilizing deep transfer learning facilitates achieving excellent results expeditiously. This paper uses the deep transfer learning approach to classify fruits with similar color and texture, namely guava, avocado, lime, apple, pear, mango, and pomelo sweetie. This study introduces a novel model derived from integrating DenseNet, MobileNet, and EfficientNet architectures. The model’s performance is systematically assessed using different optimizers, contributing to a comprehensive evaluation of its efficacy. Simulation findings indicate that MobileNetV1 when paired with the Adam optimizer, surpasses other models in terms of training time, accuracy, and testing time.

基于轻量化的 HCES-YOLO 的鸡蛋品质检测算法

The quality detection of eggs based on deep learning faced many problems, such as similar feature colors and low computational efficiency, which resulted in an increased probability of false detection or missed detection. To effectively solve these problems, this paper proposed an egg quality detection method based on YOLOv8n, which integrated the ContextGuideFusionModule, EfficientHead, and SIOU loss functions by improving the backbone network. The recognition rate from the field test was 88.4%, indicating that the algorithm could meet the real-time monitoring requirements, effectively identify the quality status of eggs, and provide support for intelligent poultry house management.

Simple Metric Measurement To Choose A keystone Flap Design On Extremities: A Cadaveric Study

Background: Closing defect on extremities can be challenging because of limited donor area in order to obtain similar quality, color, texture, and adequate size with the defect. The keystone flap has gained popularity as a tool for local reconstruction because of its simple design, short operative time, good aesthetic outcome, and cost-effective wound closure. The aim of this study is to introduce a method in choosing a keystone flap design based on simple metric measurement resulting in lower tension.. Methods: Four circular defects were created on 4 different regions of a fresh cadaver’s upper limbs. Diameters were 5 cm for upper arms and 3 cm for lower arms. Two options of keystone flaps designs were introduced. The keystone could be advanced in longitudinal manner or in transversal manner according to limb’s axis. We then calculated the percentage of the skin required to stretch, in order to close the defects. The less percentage of skin required to stretch between the two manners indicated the lower tension of the keystone flap. Results: Measurements in all of 4 regions of upper limbs showed that the percentage of skin stretch in closing the defects was lower in longitudinal advancement keystone flap compared to transversal advancement (19.88% versus 27.8% for upper arms and 15.71% versus 21.67% for lower arms) Conclusion: Simple metric measurements in choosing a keystone flap can be applied to defects on extremities. With less tension when raising the keystone flap, acceptable scar is expected and the occurrence of contracture and flap necrosis can be reduced.

Perigenital Defect Reconstruction Post Giant Condyloma Acuminata Resection: Experience With The Double Keystone Flap

Abstract: Due to its high recurrence, genital condyloma acuminata should be widely excised to include the full dermal thickness. Currettage is not advisable, as the rate of recurrence is high. The bigger the lession, the bigger donor area needed to close the defect. The reconstructive options do not merely depend on the defect area, but also on selecting a tissue donor of similar color, thickness, sensation and durability. The ease of donor transfer is equally important. Not many case of giant genital condyloma acuminata wide excision and reconstruction has been reported. For coverage of larger soft tissue defect, skin graft is the most common option; however durability is of concern and the perigenital area is a challenging area to graft. Two cases of perigenital reconstruction after giant genital condyloma excision is reported in this paper, using random perforator based flaps, the double keystone flap. Patient and Method: Both cases of giant genital condyloma underwent wide excision by the urologist and referred to our division for reconstruction. Both patients are male, and the defects were reconstructed using the double keystone flap. One patient needed a split thickness skin graft to cover remaining defect on the penile shaft. Result: In both cases, the double keystone flap successfully covered for the defects on the perigenital with tissue quality similar to its original characteristics. No complication ensued. All flaps were vital, no dehisence nor local infection occurred. Summary: The double keystone flap is a reliable option for the reconstruction of large defects on the perigenital region.

AI Dermatochroma Analytica (AIDA): Smart Technology for Robust Skin Color Classification and Segmentation

Traditional methods for skin color classification, such as visual assessments and conventional image classification, face limitations in accuracy and consistency under varying conditions. To address this, we developed AI Dermatochroma Analytica (AIDA), an unsupervised learning system designed to enhance dermatological diagnostics. AIDA applies clustering techniques to classify skin tones without relying on labeled data, evaluating over twelve models, including K-means, density-based, hierarchical, and fuzzy logic algorithms. The model’s key feature is its ability to mimic the process clinicians traditionally perform by visually matching the skin with the Fitzpatrick Skin Type (FST) palette scale but with enhanced precision and accuracy using Euclidean distance-based clustering techniques. AIDA demonstrated superior performance, achieving a 97% accuracy rate compared to 87% for a supervised convolutional neural network (CNN). The system also segments skin images into clusters based on color similarity, providing detailed spatial mapping aligned with dermatological standards. This segmentation reduces the uncertainty related to lighting conditions and other environmental factors, enhancing precision and consistency in skin color classification. This approach offers significant improvements in personalized dermatological care by reducing reliance on labeled data, improving diagnostic accuracy, and paving the way for future applications in diverse dermatological and cosmetic contexts.