Dominant Attributes Research Articles

Designing A Household Waste ShredderMachine Using The Quality Function Deployment Method

Household waste, especially inorganic waste, is becoming an increasingly pressing challenge in waste management in the community, which can have a negative impact on the environment if not handled properly. This research aims to design a portable, effective, and efficient household waste shredding machine by developing essential attributes such as automatic, ergonomic, easy to operate, durable, safe to use, low cost, easy to disassemble, not easily damaged, and easy to move. The method used is Quality Function Deployment (QFD), which enables the identification and fulfilment of user expectations through in-depth analysis of the voice of the customer. The results showed that of the attributes developed, the most dominant attributes were safety in use and ease of operation, followed by portability and durability. Meanwhile, users considered attributes such as ease of disassembly and low cost less critical. The resulting machine design is able to fulfil all the criteria set by users. It provides an innovative solution to improve the efficiency of inorganic waste management at the household level to provide convenience, safety, and portability in its use. From the weighting of attribute values, the most dominant order to the last is the attribute of easy to move with a value of 10,542, easy to disassemble 9,404, safe when used 8,821, ergonomic 8,151, easy to operate 6,630, durable 6,248, low cost 5,439, the machine is not easily damaged 5,283, automatic 5,110.

Effect of Pre-Processing Treatment and Concentration of Alaria esculenta, Saccharina latissima, and Laminaria digitata Varieties on Texture and Consumer Attribute Preference of Crackers.

This study investigates the effects of three brown seaweed species (Alaria esculenta, Saccharina latissima, and Laminaria digitata), their pre-processing treatments, and incorporation percentages on the physical and sensory properties of crackers. Significant (p ≤ 0.001) two-way and three-way interactions were observed for moisture content, with seaweed addition generally resulting in drier crackers. Shrinkage was primarily influenced by sample thermal treatment, while hardness was significantly affected by seaweed species, treatment, and their interactions. The freeze-thaw treatment produced harder crackers compared to other treatments. Sensory analysis using temporal dominance of sensations (TDS) revealed variations in dominant attributes across different seaweed species and treatments, with retorted and freeze-thaw treatments enhancing crunchiness and reducing fishy flavors. The study demonstrates the potential of seaweed as a functional ingredient in cracker formulations, affecting both textural properties and sensory experiences. It also highlights the importance of pre-processing treatments in modulating these effects, providing valuable insights for the development of seaweed-fortified food products with enhanced nutritional value and consumer acceptability.

Enhanced creaminess of plant-based milk via enrichment of papain hydrolyzed oleosomes

There is an increased consumer demand for plant-based milk in substituting dairy milk due to the ethical, health concerns and environmentally-friendly choice. However, perceived creaminess as dominant attributes present a big challenge in consumer acceptance for those milk alternatives. In this study, we developed a novel and easily scalable strategy to enhance the creaminess of soy milk via enrichment of oleosomes. The soybean oleosome creams were extracted and hydrolyzed with papain, resulting in formation of oil droplets with more phospholipid and less protein at the surface, which significantly reduce friction coefficient in the presence of saliva (from 0.15 to 0.03 at a speed around 50 mm/s). Moreover, blending papain-hydrolyzed oleosome creams with raw soy milk enables the creation of a plant-based milk that matches the nutritional profile, lubrication properties, and creaminess of full-fat dairy milk. QCM-D and passive microrheology were employed to characterize hydration of oleosomes into the mucin layer and relevant viscosity change, suggesting that papain hydrolyzed oleosome might decrease friction coefficient via hydration lubrication mechanism. This approach could be applied to enhance the creaminess mouthfeel and nutritional profile of plant-based milk.

Effect of frozen storage duration on temporal dominance of sensations (TDS), chemical investigations and moisture distribution of roasted chicken wings

In order to guide manufacturers or consumers to choose frozen chicken wings (CWS) reasonably for roasting, the changes of sensory quality and physicochemical properties of CWS with different freezing storage time (0–12 months) after roasting were studied. The results showed that juiciness, fat flavor, meat flavor, and saltiness were the most important dominant attributes of roasted CWS, and their dominance rate gradually decreased from the ninth month. Increased (P < 0.05) TBARS value and water loss result in poor juiciness. According to the results of GC-IMS, the decrease of the dominant rate of meaty may be related to the decrease of the contents of 2-ethyl-5-methylpyrazine, benzene and thiazole. In addition, the accumulation of 1-octen-3-ol, n-hexanol, and hexanal caused by long-term freezing (> 9 months) give chicken wings a fishy and metallic taste. Therefore, CWS that have been frozen for more than 9 months are not ideal ingredients for roasting.

ANALISIS PENGARUH SIKAP PETERNAK TERHADAP KEMAMPUAN BERWIRAUSAHA PENGGEMUKAN DAN PEMBIBITAN SAPI DI KECAMATAN PADEMAWU KABUPATEN PAMEKASAN

Pamekasan Regency has the lowest cattle population in Madura, 194,283 heads. The attitude of farmers who have an important role in the cattle business. This study aims to determine 1) the entrepreneurial characteristics of farmers, 2) the attributes of the dominant attitudes of farmers, and 3) the relationship between the attitudes of farmers and their entrepreneurial abilities in Pademawu District, Pamekasan Regency. The method used is descriptive quantitative, using secondary and primary data from 35 cattle breeders. This study uses a descriptive analysis and multi-attribute Fishbein. The results of this study indicate that 1) the entrepreneurial characteristics of breeders are still common, and their entrepreneurial skills are still low; 2) the dominant attitude of farmers in entrepreneurship is the selling price of farmers, while the attitude that has the lowest rank is maintenance; 3) the relationship between attitudes and abilities in entrepreneurship of breeders is said to be quite close, entrepreneurial attitudes of breeders such as motivation, yield orientation, risk-taking, opportunity utilization and perceptions of livestock business are not dominant attributes, but have a large enough effect. Cattle breeders in Pademawu District need counseling about maintenance risks, financial management, opportunity utilization, yield orientation, and increased motivation.

An Adaptive Migration Collaborative Network for Multimodal Image Classification.

The multispectral (MS) and the panchromatic (PAN) images belong to different modalities with specific advantageous properties. Therefore, there is a large representation gap between them. Moreover, the features extracted independently by the two branches belong to different feature spaces, which is not conducive to the subsequent collaborative classification. At the same time, different layers also have different representation capabilities for objects with large size differences. In order to dynamically and adaptively transfer the dominant attributes, reduce the gap between them, find the best shared layer representation, and fuse the features of different representation capabilities, this article proposes an adaptive migration collaborative network (AMC-Net) for multimodal remote-sensing (RS) images classification. First, for the input of the network, we combine principal component analysis (PCA) and nonsubsampled contourlet transformation (NSCT) to migrate the advantageous attributes of the PAN and the MS images to each other. This not only improves the quality of images themselves, but also increases the similarity between the two images, thereby reducing the representational gap between them and the pressure on the subsequent classification network. Second, for the interaction on the feature migrate branch, we design a feature progressive migration fusion unit (FPMF-Unit) based on the adaptive cross-stitch unit of correlation coefficient analysis (CCA), which can make the network automatically learn the features that need to be shared and migrated, aiming to find the best shared-layer representation for multifeature learning. And we design an adaptive layer fusion mechanism module (ALFM-Module), which can adaptively fuse features of different layers, aiming to clearly model the dependencies among multiple layers for different sized objects. Finally, for the output of the network, we add the calculation of the correlation coefficient to the loss function, which can make the network converge to the global optimum as much as possible. The experimental results indicate that AMC-Net can achieve competitive performance. And the code for the network framework is available at: https://github.com/ru-willow/A-AFM-ResNet.

Assessment of Sensory Attributes in White Tea Utilizing the High Identity Traits (HITS) Methodology Employing an Immersive Approach

White tea is a plant-derived product from Camellia sinensis L, which is processed without enzymatic oxidation and fermentation. The high identity traits (HITS) is a sensory evaluation method that identifies dominant attributes using five attributes to describe each sample. The sensory evaluation research on white tea uses the HITS method with an environmental approach aimed at identifying dominant attributes of white tea using both trained and untrained panelists, as well as at determining the intensity of these dominant attributes using an environmental approach with both trained and untrained panelists. The materials used were five samples of Gamboeng white tea, Heizl, Dandang, Pucuk, and Ennie1. The HITS method with trained panelists successfully identified dominant attributes as follows: yellow-brown brew color, rose aroma, rose flavor, astringent aftertaste, and light mouthfeel. Meanwhile, the experiment using untrained panelists showed dominant attributes as follows: yellow-brown brew color, jasmine aroma, jasmine flavor, astringent aftertaste, and light mouthfeel. The HITS method with an immersive approach for both trained and untrained panelists had an impact on increasing the intensity of aroma, flavor, and mouthfeel while decreasing the intensity of aftertaste.

Redefining dominance calculation: Increased competition flattens the dominance hierarchy in dairy cows

Dominance hierarchies are known for mitigating conflicts and guiding priority of access to limited resources in gregarious animals. The dominance hierarchy of dairy cows is typically investigated using agonistic interactions, often monitored at the feed bunk right after fresh feed delivery when competition is high resulting in frequent interactions. Yet, the outcome of agonistic interactions during times of high competition may be more influenced by cows' high valuation of fresh feed than their intrinsic attributes, such that the dominance hierarchy constructed using agonistic interactions under high versus low competition times might differ. We tested how the structure of the dominance hierarchy changes in relation to different levels of competition in a dynamic group of 48 lactating dairy cows over 10 mo, with 6 cows exchanged every 16 d for a total of 159 cows. Using a validated algorithm we continuously detected the actor and reactor of replacement behaviors in 30 feed bins as cows competed for feed. We also calculated the percentage of occupied feed bins to characterize competition at the moment of each replacement. These data were combined to create hierarchies using Elo ratings, separately for 25 occupancy levels ranging from 13% to 100%. For each 1% rise in feeder occupancy, hierarchy steepness fell by 2.41 × 10-3 ± 9.71 × 10-5 (SE), and the percentage of dyads where both cows replaced each other rose by 0.13% ± 0.01%. At the highest feeder occupancy level in comparison to the lowest one, we observed 7.57% more dyads in which the dominant individual (those won more interactions at the lowest feeder occupancy) started to lose proportionally more. The magnitude of decrease in the winning rate of the dominant individual in those dyads also got amplified by 1.06 × 10-3% ± 1.37 × 10-4% (SE) for each 1% increase in feeder occupancy. These findings illustrate how inferred hierarchies vary with competition, with high competition flattening the hierarchy due to increased success of subordinate animals. We suggest that during heightened competition, increased valuation of resources can affect competitive success more than the individual's intrinsic dominance attributes. We recommend against calculating dominance hierarchies based on agonistic interactions during periods of high competition alone, and more generally urge researchers to differentiate agonistic interactions based on context when constructing dominance hierarchies.

Influence of soil type and ripening degree on the intensity and sensory dominance of Mexican habanero chili (Capsicum chinense Jacq.)

AbstractThe objective of this research was to determine the intensity and sensory dominance of habanero peppers grown in different types of soils and with different degrees of maturation. Chili samples grown in red soil (Mayan name: K'ankab lu'um) and black soil (Mayan name: Box lu'um) were evaluated in an immature state (green coloration) and intermediate ripening (green‐orange coloration) using descriptive analysis (DA) and temporal dominance of sensations (TDS). Sensory representation and classification percentage of chili peppers were evaluated using principal component analysis and discriminant factor analysis. The sensory results demonstrated that intermediate ripening chili peppers presented higher color intensities associated with pungency. Immature chili peppers were characterized by having high odor intensities. The TDS results showed that the dominant attributes were burn feeling, chili flavor, heat, and numbness. Immature chili peppers grown in black soil tend to be burn feeling dominant and chili peppers grown in red soil were characterized as chili flavor. The ripe habanero peppers were chili flavor burn feeling and heat. The correct classification percentage was 89.81%.Practical ApplicationsThis research exposes the sensory differences of habanero peppers with different degrees of maturation and grown in different types of soils. The findings can serve as a reference for future research on the sensory characterization of pungent and for the industry dedicated to the production of habanero chili and for the gastronomy area in charge of the preparation of different dishes that use habanero chili as an ingredient.

Analysis of sensory and cognitive performance of generational consumers using artisan tortillas

AbstractThe objective of this research was to compare the sensory and cognitive performance of generations of consumers using artisanal tortillas. Four groups of generational consumers were evaluated (baby boomers, generation X, Y, and Z; n = 100 consumers per generational group). Three experiments were conducted: (1) use of the intensity scale, (2) determination of dominant attributes via TDS and (3) elicited emotions and memories. The results of the first experiment showed that baby boomers, generation X and Y had the best performance in terms of discrimination and consensus. The second experiment demonstrated that generation Z consumers identified dominant attributes of texture in mouth and the other generations identified corn flavor, nixtamal flavor, and sour taste attributes as dominant. Regarding the evaluation of emotions and memories, it was observed that as generations progress, more emotions, and memories were used. In this sense, generation Z was the one that used the most negative emotions and memories.Practical ApplicationsAnalyzing the generational effect can help researchers and tortilla manufacturers to the selection of generational consumer for the evaluation of artisanal, cross‐cultural, or industrialized foods.

Changing perceptions of people wearing masks: Two years of living in a pandemic

AbstractDespite the widespread use of face masks to combat COVID‐19, little is known about their immediate and delayed social consequences. To understand short‐ and long‐term effects of face masks on interpersonal perception, we measured the evaluation of faces with and without masks at four time points—June 2020, January 2021, September 2021 and June 2022—from the early months of the pandemic in North America to the more recent, and from the implementation of mask mandates to the end of these requirements. Surprisingly, we found that, in general, faces with masks were perceived as more competent, warm, trustworthy, considerate and attractive, but less dominant and anxious than faces without masks. Moreover, differences in attributions of dominance, trustworthiness and warmth between faces with and without masks increased in a linear trend from June 2020 to June 2022. Notably, the impact of masks on perceptions of competence, considerateness, attractiveness and anxiousness did not change over time. We discuss how mask mandates can alter people's social perceptions of others who wear masks compared to those who do not wear masks and how these mandates may influence attributions of some traits more than others through mere exposure and/or social norms.

Cancer Risk Identification using Machine Learning

In this comprehensive study, we explore the SEER (Surveillance, Epidemiology, and End Result) dataset to meticulously craft a predictive model aimed at understanding patient survivability, focusing keenly on outcomes like patient survival diagnosis and treatments. Employing a good approach involving rigorous data cleaning, intricate feature extraction, and detailed correlation analysis, we identify the dominant attributes that significantly influence patient outcomes. Our methodology integrates classical machine learning models, including decision trees and random forests, with modern techniques such as neural networks. This mix of methodologies allows us to accurately predict patient survivability. Furthermore, our study delves into the intricate relationships between attributes, models, and algorithms, aiming to identify the dominant factors that influence the outcomes. Our groundbreaking findings underscore the enormous potential of integrating these dominant attributes, paving the way for the creation of exceptionally robust predictive models. These models will substantially enhance medical decision-making processes and, in turn, elevate the overall quality of patient care. Keywords - SEER dataset, patient survivability, machine learning, decision trees, random forests, neural networks, data cleaning, feature extraction, clinical data, medical decision-making.

Volatile Compounds Content and Sensory Profile of Katuk (Sauropus androgynus) Leaves After Household Scale Heating

Katuk (Sauropus androgynus) is an indigenous Indonesian plant, and the leaves are often consumed as fresh or processed vegetables. Despite its potential, there are no reports on the volatile compounds content and sensory profile of Katuk leaves after being processed using household scale heating. Therefore, this study aimed to determine the volatile compounds content and sensory profiles of Katuk leaves after household scale heating (steaming and boiling). The extraction of constituents was carried out using the headspace-solid phase microextraction method, followed by identification with gas chromatography-mass spectroscopy. The sensory profile analysis was performed using the free choice profiling method with untrained panelists. The analysis results showed the presence of 16 volatile compounds derived from 7 groups, including aldehydes (5 compounds), alcohol (3 compounds), other components (3 compounds), ketones (2 compounds), as well as benzene, terpenoids, and esters (1 compound). The dominant compounds found in fresh Katuk leaves were alcohol [(Z)-3-hexene-1-ol]. Meanwhile, aldehydes (benzaldehyde, nonanal, benzeneacetaldehyde, and (E)-2-pentenal) and alcohol (1-Heptanol and Nerolidol) were dominant in the steamed samples, with benzene (naphthalene) being predominantly found in the boiled samples. The sensory profile analysis results showed that fresh Katuk leaves had a grassy and earthy aroma with a grassy flavor, while the boiled samples had a smooth and juicy texture. The dominant attributes found in steamed Katuk leaves included moist, tender, and tasteless. Based on these findings, household scale heating could modify the composition of volatile compounds, thereby affecting the sensory profile. The results obtained were expected to serve as a foundation for the processing of Katuk leaves at both industry and household levels.

Intrusion Detection Using Bat Optimization Algorithm and DenseNet for IoT and Cloud Based Systems

In Internet of Things (IoT) and cloud systems, Intrusion Detection (ID) is very vital for protecting the security infrastructures. ID techniques are extensively used to detect and track malicious threats in cloud and IoT systems. In the IoT based ID, the conventional techniques work based on the manual traffic feature values that increase the complexity of the networks and achieve a limited detection rate on the larger IoT databases. For addressing the above-stated issues and achieving high classification results, an effective deep learning based ID-System (IDS) is implemented in this article. Initially, the IoT data is acquired from the NSW-NB15 and NSL-KDD databases, and then, the standard scaling normalization technique, known as Min-Max normalization, is applied to select the dominant attributes and to eliminate outliers from the acquired databases. Additionally, the optimal features are selected from the rescaled normalized data by implementing the Bat optimization algorithm. The selection of optimal features decreases the computational complexity and training time of the IDS. The chosen optimal features are passed into the DenseNet model for carrying out intrusion attack detection. Particularly, in the binary-class classification, the Bat-based DenseNet model obtained 98.89% and 98.40% of accuracy on the UNSW-NB15 and NSL-KDD databases, correspondingly. The obtained simulation results prove the higher effectiveness of the current study when it is related to the state-of-the-art classifiers.

Attributes, Challenges, and Performance of Women Leaders in Private Higher Education Institutions in Cavite: Basis for Strengthening Management Development Program

This study was conducted to investigate the attributes, challenges, and performance of women leaders in private higher education institutions in Cavite as a basis for strengthening management development program. Specifically, the study aimed to describe the profile of the participants; the most dominant attributes of women leaders; the most difficult challenges being encountered by women leaders; and their level of performance. This was also conducted to assess the relationship between the dominant attributes and profile and the most difficult challenges and profile, and the level of performance and profile of the participants; relationships between the dominant attribute and challenges and level of performance and most challenges encountered and performance of women leaders as well as propose a management development program to be used by private HEIs. This research considered total enumeration of 103 women leaders of 15 selected private HEIs in the province of Cavite. The conduct of surveys, interviews and focus group discussions were done to gather data. The study employed the descriptive and correlational research designs with mean, frequency, standard deviation, Mann-Whitney U Test, Kruskal Wallis H Test, and Spearman’s correlation as statistical tools. Likewise, this applied qualitative research method to solicit deeper insights from the participants by posting questions focused on their experience, explicit expectation, challenges, and further aspirations as a woman leader in an HEIs in Cavite. The results showed that majority of women leaders were married, with an average age of 41 to 50. Many were either with units in master’s or master’s graduates and with units in doctorate and with more than 16 years as a women leader. The most dominant attributes of women leaders are integrity and the ability to build teams, while maintaining the balance between the work and life remained to be most challenging experience. People management got the highest mean. Significant differences were noted in the dominant attributes, most difficult challenges, and level of performance across the profile. Significant relationships were also identified between the dominant attribute and most difficult challenges encountered; dominant attributes and level of performance; and most challenges encountered and performance of women leaders. In terms of performance, women leaders were found to be very good in people management. A proposed management development program was recommended which maybe integrated to the plans of the respective private HEIs.

Audio Feature Space Analysis for Emotion Recognition from Spoken Sentences

An analysis of low-level feature space for emotion recognition from the speech is presented. The main goal was to determine how the statistical properties computed from contours of low-level features influence the emotion recognition from speech signals. We have conducted several experiments to reduce and tune our initial feature set and to configure the classification stage. In the process of analysis of the audio feature space, we have employed the univariate feature selection using the chi-squared test. Then, in the first stage of classification, a default set of parameters was selected for every classifier. For the classifier that obtained the best results with the default settings, the hyperparameter tuning using cross-validation was exploited. In the result, we compared the classification results for two different languages to find out the difference between emotional states expressed in spoken sentences. The results show that from an initial feature set containing 3198 attributes we have obtained the dimensionality reduction about 80% using feature selection algorithm. The most dominant attributes selected at this stage based on the mel and bark frequency scales filterbanks with its variability described mainly by variance, median absolute deviation and standard and average deviations. Finally, the classification accuracy using tuned SVM classifier was equal to 72.5% and 88.27% for emotional spoken sentences in Polish and German languages, respectively.

An Artificial Intelligence based automated case-based reasoning (CBR) system for severity investigation and root-cause analysis of road accidents – Comparative analysis with the predictions of ChatGPT

Road accidents have been progressively causing havoc in our society and certain preventive measures must be taken to reduce or possibly eliminate road accidents. The derivative of a road accident ranges from a mild injury to casualty. This research work mainly focusses on developing a novel case-based reasoning system to investigate and troubleshoot the cause of road accidents on a war-foot basis. First, the dominant attributes contributing to the cause of road accidents are identified and finalized as 28. A unique road accident dataset is developed which comprises of 1028 data collected from web resources, popular news magazines and extended further to large scale database of one-million cases by biased random number simulation. Each attribute is given a severity weightage of 1,2 and 3 for computing the net weighted score for a case in the database. Also, non-weighted scores are computed by introduction of a primary number dataset to maintain the uniqueness of the score which is further used for similarity analytics. Now, an accident news is randomly selected, and Rapid automatic keyword extraction (RAKE) schema is used as Natural language processor (NLP) for extracting the dominant keywords from the news articles. The extracted keywords are compared and further mapped into a factor-matrix comprising 28 attributes causing road accidents. Further, similarity analytics is performed to evaluate the severity scores and comparison of new cases. The system demonstrated high retrieval accuracy with all road accident cases collected from real world scenarios. This research has great prospects on troubleshooting road accident cases effectively and provides instant promising troubleshooting measures to prevent such accidents in the future. Also, the proposed framework might be useful for intelligent decision-making systems and automated driving systems. Based on the final outlook, a comprehensive framework for national road safety could be developed and passed as a valid law for implementation. Finally, the forecasted results of the proposed algorithm are compared with the predictions of Chat GPT program.

Analisis komponen bioaktif dan atribut sensori seduhan kopi Arabika Kalosi Enrekang dengan pengolahan pascapanen berbeda [Analysis of bioactive compounds and sensory attributes of Kalosi-Enrekang Arabica coffee prepared different postharvest processing

Postharvest processing affects the chemical components and sensory attributes of coffee beans. This study aimed to evaluate the effect of postharvest processing on caffeoylquinic acids and alkaloids (bioactive compounds) and sensory attributes (aroma and taste) of brewed coffee. Coffee beans of Kalosi-Enrekang were processed using three postharvest processing methods (honey, natural, and full-washed), roasted at a medium degree, and brewed with tubruk brewing techniques. Bioactive compounds were analyzed using UPLC-PDA and sensory attributes were analyzed using the Quantitative Descriptive Analysis method. The data were analyzed using single-factor ANOVA and Principal Component Analysis. The research showed that postharvest processing did not affect the bioactive compound of brewed coffee. Postharvest processing affected the aroma and taste attributes of brewed coffee. Natural processing had dominant attributes of fruity, spicy, sweet, and caramelized. Honey processing had dominant attributes of nutty and roasted, while full-washed processing did not have dominant attributes. The result also showed that caffeoylquinic acids and alkaloids did not affect coffee brewed's aroma and taste attributes.

Dominant Requirements for Student Graduation in the Faculty of Informatics using the C4.5 Algorithm

Graduating on time is one of the indicators in the achievement and ranking of educational institutions. The achievement of graduating on time in educational institutions is essential to balance incoming and graduating students. The problem that occurs, the attributes for graduating on time have varying weightings, so the determinants of the attributes for passing on time need to be known so that the anticipation of achieving graduation on time can be met. The purpose of this study is to find out the dominant attributes in the prediction of graduating on time for students. The attributes used are credit scores (Semester Credit Units), GPA scores (Grade Point Average), and English scores (TOEFL). The method used is the C4.5 Algorithm which is one of the classification methods in data mining. The data used was 262 data, split randomly with a composition of training and testing data of 80:20. Data is processed using the data mining process by creating decision trees. The decision tree results using the C4.5 Algorithm show that the GPA value is the most influential attribute in predicting a student's graduation time. In addition, predictions based on the decision tree of the C4.5 Algorithm with criterion = 'gini' and max_depth = 5 showed an accuracy result of 77%.

Congruity and Incongruity between Projected (DMO) and Perceived (UGC) Destination image – A Comparative Content Analysis

User-generated Content (UGC) in the digital world has gained credibility as a kind of word-of-mouth in recent years with the advent of new Information and communication technologies (ICT). Travelers rely on UGC information than DMO content while the travel decision-making process. No previous research has been conducted to correlate both UGC and DMO destination images of Kerala. The study analyzes the dominant attributes of the destination image of Kerala presented through DMO and UGC Visual Content. Also, find out whether there is any commonality in destination attributes between pictures posted by tourists (UGC) and DMO or vice versa. Images of Kerala were gathered for this study from DMO’s website (www.keralatourism.org), Instagram, and Facebook, two of the most popular social media platforms worldwide. By applying content analysis methodology to the collected DMO and UGC images of Kerala Tourism, the research objectives are attained. Data from the last five years were considered for the study to understand the latest trends and changing patterns. The results aid tourism stakeholders in planning effective social media marketing strategies to capture the imagination of tourists, hence creating a better destination image online.