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Evaluating social media content's effect on consumer engagement in the context of digital marketing
The advancement of social media platforms in promoting consumer participation in brand development and sustainable consumption has been substantial. Social media's popularity has increased significantly in the twenty-first century. To enhance sales performance, enterprises consistently seek novel strategies to integrate these platforms into their promotional initiatives. Social media functions as a platform for networking and communication; consequently, organizations must imbue their brands with personality to connect with consumers. Despite extensive academic research on corporate social media marketing techniques, the influence of these activities on consumer purchase choices remains largely unexplored. Organizations have recently embraced influencer marketing as a tactic to promote and publicize their content by leveraging the support of influential individuals. The growing frequency of product endorsements on social media highlights the importance of understanding the impact that these influencers have on customers. This research aims to analyze the influence of social media content and its characteristics on consumer engagement in the digital domain. Additionally, this study will serve as a foundation for future investigations in this area. The insights regarding the content elements of social media marketing that foster consumer engagement were contributed by seventy-five unique social media users. 2025 by the authors; licensee Learning Gate. -
FORTIFICATION OF LAUNDRY WATER WITH BACTERIA CAPABLE OF SODIUM DODECYL SULFATE (SDS) REMEDIATION AND PLANT GROWTH PROMOTION. A SUSTAINABLE WAY TO REUSE WATER FOR IRRIGATION
Anionic surfactant sodium dodecyl sulfate (SDS) is used in cosmetics and cleaning goods. It discharges into the environment and waterways due to its extensive use. Basal media with 0.05% SDS as the sole carbon source was used to isolate bacteria that can utilize SDS. The isolates survived nitrogen-free medium and solubilized potassium and phosphate. Using 16S rRNA sequencing, Enterobacter cloacae strain MSK86 (OR136425) was identified. Stains-all dye was used to test the bacteria's SDS-utilizing capability. A 49% drop in SDS levels in the broth was observed after 7 days of 24-hour analysis. The bacteria exhibited tolerance to heavy metals like Cd (II), Ar (III), and Zn (II) at concentrations up to 2000 ppm, whereas they were susceptible to Cu (II), Cr (II), and Pb (II) at minimum concentrations of 200, 600, and 1000 ppm, respectively. The bacteria effectively reduced SDS levels in the laundry wash water. The treated water was reused for the irrigation of Capsicum annum L. and Solanum lycopersicum L. until the 45th day of growth. The plants' morphological and phytochemical properties were also analyzed. The potential of bacteria for SDS degradation and plant growth enhancement has been extensively explored independently; however, these traits have not been studied together in a single bacterial strain. In the present study, multifaceted Enterobacter cloacae MSK86 was isolated with these capabilities together, which may help in SDS remediation, making the water reusable for irrigation. (2025), (Slovak University of Agriculture). All Rights Reserved. -
Developing a psychometric scale to measure motherhood stress among newly working mothers in Indias IT sector
While becoming a mother is an incredible blessing, it can also be a very stressful time for women. India has a unique cultural and economic diversity, with women facing distinct challenges in their role as new mothers, including family obligations and a tendency toward a patriarchal mindset. This research aims to identify the unique stress factors related to motherhood stress for a new working mother in the IT sector and to create a scale to measure these factors. The study employed a mix of qualitative methods, such as focus group discussions, and quantitative research methods, including a pilot survey among 115 mothers, to develop a new scale for motherhood stress. Four main factors were identified through exploratory factor analysis: career-related stress, adequacy of support systems, maternal guilt, and self-efficacy. A new 31-item scale is proposed. Internal consistency, reliability, and validity were established for all items on the scale, with a KMO value of 0.826 and a Cronbach's alpha of 0.840. This new scale will be a useful tool for organizations to understand motherhood stress among newly working mothers and to adopt practices that reduce stress and address prejudices through interventions such as anti-discrimination policies, managerial sensitization, flexible work options, career counseling, and peer support. For policymakers, this study highlights the need for an industrial policy that recognizes the cultural setting and the unique challenges faced by Indian working mothers, as well as the importance of rigorous enforcement of maternity policies to ensure equitable treatment for them. 2026 AESS Publications. All Rights Reserved. -
The role of audiobooks in developing English listening proficiency: A study of undergraduate learner in Tamil Nadu
Listening is one of the inevitable yet often overlooked skills in the process of learning a second language, especially for Indian undergraduate students learning English as a Second Language (ESL). Even after years of studying English in school, many students still struggle to understand spoken English. This occurs because students do not get sufficient exposure to real English as spoken in daily life, and they often feel nervous or uncomfortable in classroom settings. Various apps and tools are used to facilitate language learning, but there remains limited understanding of how audiobooks specifically contribute to enhancing listening skills. This study examines the effectiveness of audiobooks in developing listening skills through a twelve-week program involving 66 undergraduate students from Tamil Nadu. Students participated in a pre-test before the intervention and a post-test after completing the twelve-week audiobook listening program, which involved daily sessions of one hour. They listened to audiobooks with teacher support initially and then independently. The results indicated a significant improvement in listening scores, with an average increase of 25%. Additionally, students reported feeling less anxious about listening, better retention of new vocabulary, and improved ability to follow spoken English. The findings suggest that audiobooks are a vital component of language learning, providing autonomous, low-pressure, and long-term benefits in listening development. Consequently, audiobooks enhance confidence and foster consistent listening skills among ESL learners. The study recommends integrating audiobooks into regular classroom activities to support language acquisition. Contribution/Originality: This study contributes to the existing literature by validating audiobooks as effective ESL tools. It uses a new estimation methodology through SPSS analysis, originates a formula linking Krashens Affective Filter with audiobook learning, and is one of the few studies on Tamil Nadu undergraduates. The paper contributes quantitative and emotional factors, documents progress, and finds that audiobooks enhance proficiency. 2025 AESS Publications. All Rights Reserved. -
FINANCIAL DECISION-MAKING POWER AND RISK-TAKING BEHAVIOUR IN INDIAN HOUSEHOLDS
This study aims to examine the impact of decision-making power on risk-taking behaviour in household economies in India. It further explores the relationship between decision-making power, perceived risk-taking behaviour, and actual risk-taking behaviour. Further, the study employs the primary data collected through a structured questionnaire. The snowball sampling method was adopted to gather data from 312 retail investors in the study area. The response rate for the sample size is 91.50%. An OLS regression model was constructed to measure the frequency of trading habits as a proxy for the respondents' risk-taking behaviour. The results indicate that decision-making power significantly impacts investors' risk-taking behaviour in Indian household economies. Additionally, decision-making power has a significant impact on perceived risktaking behaviour. The findings of this study show how decision-making power influences the risk-taking behaviour of retail investors. This study adds value to the literature on behavioural finance and household economies. The results will pertinently support retail investors' decision-making skills in unbiased investment decision-making. 2025 by the author(s). -
Unfolding the aggression and locus of control paradigm in sportspersons and non-sportspersons
The present study investigated Aggression and Locus of Control on Combat Sports Persons, Non-Combat Sports Persons, and Non-Sports Persons. In this study, a sample of 240 individuals (80 Combat sports, 80 Non-Combat Sports & 80 Non-Sportspersons) was used through purposive sampling. The tools administered were the Buss and Perry Aggression Questionnaire by Arnold H. Buss and Mark Perry and Rotters Locus of Control Scale by Julian Rotter respectively. The objective of the study was to investigate Aggression and Locus of Control in males and females from Combat, Non-Combat, and Non-Sports persons. This research also aims to explore the relationship between Aggression and Locus of Control. Mean, t-test, F-value (ANOVA), and correlation have been computed over SPSS-16. Results suggest that males from Combat have higher Aggression than people from non-sports and non-combat sports. There is also a significant difference between non-sports persons and sports people over the Locus of Control, sports persons showed internal locus of control compared to non-sports persons who were higher on external locus of control. The result also indicates a significant relationship between the anger dimension of the Aggression and Locus of Control. 2025 ARD Asociaci Espala. -
Resilience Amidst Neglect: Analyzing Government Failures and Community Strengths in the Wayanad Crisis
Disaster-prone areas are geographically, environmentally, or climatically vulnerable to natural disasters and require strong preparedness and adaptation. Collaboration, indigenous knowledge, and resource mobilization help communities recover and rebuild after crises, reducing vulnerability. This study investigates the catastrophic landslide in Wayanad, Kerala, in July 2024, to analyze governmental inadequacies in disaster preparedness and the notable resilience strategies exhibited by the local community. The landslide, intensified by unregulated development in ecologically vulnerable areas, underscores the failure to execute essential environmental recommendations, including those specified in the Madhav Gadgil and Kasturirangan reports. Notwithstanding these challenges, the community's swift response via local organization, resource mobilization, and local leadership was instrumental in alleviating additional losses and facilitating recovery. The research highlights the relationship among crisis management, community empowerment, and environmental sustainability in areas susceptible to disasters. This research analyzes the Wayanad disaster and proposes a model that integrates proactive policy measures with community-driven strategies to bolster resilience and diminish vulnerability in ecologically sensitive areas. The RESTORE Model Framework is a comprehensive methodology for disaster management, highlighting Resilience, Early Warning, Sustainability, and Strategic Collaboration as fundamental components. The results underscore the imperative for collaborative frameworks that incorporate government entities, local stakeholders, and environmental specialists to enhance disaster management systems. 2026, IDRiM Society. All rights reserved. -
Effect of quick lime addition for improving compressive strength of fly ash based (cement free) geopolymer concrete
In this current investigation, a comprehensive exploration has been undertaken to advance the development of Class F fly ash-based Geopolymeric mortar and concrete, utilizing fly ash that has been sourced from the Satpura Thermal Power Station in Sarni, District Betul, Madhya Pradesh, India. The study has systematically investigated the influence of Quick Lime addition in the formulation of fly ash-based Geopolymeric mortar and concrete, with a particular focus on enhancing compressive strength within the constraints of ambient atmosphere conditions. The Geopolymeric binder has been meticulously crafted using sodium hydroxide and sodium silicate as alkaline activators. Systematic variations of Quick Lime dosages (ranging from 0% to 10% by weight of fly ash) have been introduced, and the ensuing specimens have undergone scrutiny for standard consistency and setting time under ambient temperature curing. The outcomes have underscored a discernible trend wherein the judicious addition of 7 to 9 wt.% calcium oxide (Quick Lime) to the fly ash matrix has precipitated a noteworthy reduction in setting time at room temperature, concurrently manifesting a substantial enhancement in compressive strength for Geopolymeric mortar and concrete formulations. The elucidation of the binder's microstructural phases and their chemical characteristics has been pursued through rigorous analytical methodologies, encompassing X-ray fluorescence (XRF), X-ray diffraction (XRD), scanning electron microscopy (SEM), and field emission scanning electron microscopy (FE-SEM). Cost analysis has also been conducted for 1m of concrete, comparing conventional concrete (M25) and fly ash-based Geopolymer concrete. 2025, National Institute of Science Communication and Policy Research. All rights reserved. -
Interface between Legal and Moral Implications on Patenting Biotechnological Inventions: A Comparative Analysis of the Patents Law of India, the US and the EU
The biotechnology industry has seen one of the most significant expansions in the Patent Laws. It is challenging for a law to link with protecting the intangible property vested in biotechnology patents, as the biotechnology resources come from living organisms. Patenting biotechnological inventions raises legal, ethical and moral concerns. In the light of the current context, this paper examines patents law of Section 3(b) of the Indian Patents Act, 35 U.S.C Section 101 of the U.S. Patent Law, Article 53(a) of the European Patent Convention and Article 6 of the EU Biotech Directive. The paper compares the legal structure of these three countries, illuminating the similarities and differences in interpreting morality provisions by referring to various case precedents, statutes, and legal references within the patent laws of the United States and the European Union as a benchmark for evaluating Indias patent system. The paper proposes and suggests clarifying morality clauses of the Indian Patents Act to keep up with the advancements in Biotechnology inventions by providing a clear definition of morality and contrary to public order instead of patent controllers exercising their discretionary power unguided. The paper highlights that it is vital to consider moral and ethical implications as biotechnology evolves, ensuring that law and morality should foster a holistic and informed approach to shaping the future of biotechnological inventions in a global context. 2025, National Institute of Science Communication and Policy Research. All rights reserved. -
Liability of Artificial Intelligence System: A Bibliometric Study of Current and Emerging Trends (20112024)
The Integration of Artificial intelligence across the various sector such as Transportation as Autonomous vehicle, Business, education and healthcare has introduced the remarkable efficiencies such as data interpretation, data analysis, predictive analysis and Advance decision making, however it also purposed the unprecedented Legal issues. The Artificial intelligence system has become autonomous and obtained the capability of self decision making from the data. These advances of the AI system challenged the various aspect of Legal framework such as Insurance policy, intellectual property in AI and the Liability in case fault. The question of liability has become pressing concern because the Black box nature of AI and the involvement of various stakeholder complicated the assignment of legal responsibility in case of Failure. The present study aimed to investigate the research landscape including the knowledge, emerging area and the trends available in the literature on the Artificial intelligence liability. This research adopted the Bibliometric analysis methodology using the R software Biblioshiny Package, the analysis conducted on Liability focused studies related to artificial intelligence from timespan of 2011-2024. A total 154 document were obtained from the scientific databased SCOPUS and Web of Science after rigorous manual review of keywords Liability and Artificial intelligence in Title and abstract. This study employed the several analyses on the data including growth of research area, leading document, distribution of studies by the author, leading county, collaboration network, trend topic and factorial analysis. The finding indicates a notable increase in the number of publication form 2011-2024 focusing the healthcare sector. The emerging research area includes the area such as insurance, product liability, civil liability, strict liability of artificial intelligence. The study underscored the AI rule, regulation framework underdeveloped which require the further study in relation of legal liability. Finally, the findings suggest that the increasing focus on liability framework will foster the trustworthy AI and better regulating policies. 2025, National Institute of Science Communication and Policy Research. All rights reserved. -
Farmers Rights: A Euro-Indian Comparison through the lens of Intellectual Property Rights
Intellectual Property is profoundly contributing to the field of agriculture. It is used in agriculture to reward innovative breeding procedures that produce new varieties. In emerging nations, farmers have a significant role in the political, social, and economic facets of society. Agriculture provides a substantial amount of work and means of subsistence for people. Plant variety protection regulations are distinct from patent law in both India and European countries. Plant variety protection regulations are distinct from patent law in both India and European countries. This study aims to give a comparative perspective between the European and Indian regime's protection of farmers' rights and plant varieties, as well as the effect of international protocols and conventions. The Protection of Plant Varieties and Farmers Rights Act of 2001 is examined in this study along with its key components. More significantly, the measures that benefit farmers are emphasised, along with the importance of the awards and recognitions that the Indian government has instituted. This paper therefore aims to explore the state of farmers' rights under intellectual property law in two diverse regions: Europe and India. 2026, National Institute of Science Communication and Policy Research. All rights reserved. -
A Scientometric Analysis of Research Studies on 43 Years of Leadership in Online Education
Leadership in online education involves strategically managing digital learning environments to ensure effective instruction and engagement. This research aims to identify the publication trends and highlight trending research topics and scientific conversations in this field of leadership in online education. Using 947 records from the Scopus database, the evolution of leadership discourse in online education was examined using scientometric analysis to find the trends, most influential authors, institutions, publishing platforms, and countries. The extracted data spanned publications from 1981 to 2023. The trending topics evolved from knowledge management and school administration to e-learning and higher learning, and, after 2020, to challenges faced in imparting education due to Covid-19. The United States, China, and the United Kingdom emerged as leading contributors to this field. Co-authorship analysis highlighted international collaborations, which emphasised the growing global interest in leadership within virtual learning environments. These research findings could be helpful for researchers and managers in the field of education for adapting to the digital age. 2025, Commonwealth of Learning. All rights reserved. -
A STUDY ON CONJUGACY GRAPHS
In this paper, we introduce the notion of an equivalence graph based on equivalence relation defined on a group. Furthermore, restricting ourselves to conjugacy relation, a special type of equivalence graph called a conjugacy graph is also defined. In addition, a graph theoretical expression for the class equation is established followed by related results. 2025, RAMANUJAN SOCIETY OF MATHEMATICS AND MATHEMATICAL SCIENCES. All rights reserved. -
Brand Loyalty Drivers among Generation Z Fashion Consumers: A Comparative Analysis
Brand loyalty is crucial in the competitive fashion market, particularly among Generation Z. Although previous studies have investigated what drives loyalty, there is still limited evidence from India, particularly about gender differences. This study adopts a context-specific and exploratory approach to examine brand loyalty and its drivers among Generation Z fashion consumers in Bangalore. The study adopts a quantitative research design with a structured questionnaire using a 5-point Likert scale. A sample of 100 Generation Z students in Bangalore was selected using convenience sampling to collect the data. Further descriptive and inferential statistical analyses were conducted using SPSS. The findings show positive associations among brand loyalty, brand awareness, perceived quality, emotional connection, and social influence. Independent-samples t-tests reveal no significant difference in overall brand loyalty between male and female respondents. However, regression analyses indicate that perceived quality and brand awareness are relatively stronger predictors of brand loyalty among male respondents. In contrast, emotional connection is a stronger predictor among female respondents. These findings suggest differences in motivational pathways rather than loyalty intensity. The study suggests that while overall brand loyalty levels are similar across genders, the motivational drivers underlying loyalty differ. These findings are context-specific and exploratory, and their generalizability is limited by convenience sampling and a restricted geographic scope. 2026 Journal of Computers, Mechanical and Management. -
Combined Antimicrobial Activity of Chromolaena odorata, Azadirachta indica Leaf Extract, against Streptomyces scabiei (Plant pathogen) and Paenibacillus polymyxa, and their interaction
Streptomyces scabiei is a gram-positive soil dwelling actinobacteria, that causes the scab disease in many plants, especially in potatoes. This causes black blotches on the tuber that negatively impacts the economic and market worth of potatoes. Plant species like Chromolaena odorata and neem are known for their antibacterial potential against common human microflora and agricultural pathogens. Aiming to find natural solutions to plant pathogens, the current study analyzes, the antimicrobial effect of these plant extracts at different concentrations using well diffusion, MIC and MBC. Combination study was also performed to test the synergistic antimicrobial effect against Streptomyces scabiei. In addition, another soil dwelling bacteria, Paenibacillus polymyxa, known for its plant growth promoting potential was also tested against the plant pathogen to understand microbe-microbe interactions through cross streak assay. Both plant extracts showed promising antimicrobial effect against Streptomyces scabiei, however, they showed very less antimicrobial potential against the plant growth promoting bacterium. Moreover, cross streak assay showed that both the bacterium coexisted together. Hence, further studies can be conducted to formulate a biofertilizer containing the two-plant extract in optimum concentrations and Paenibacillus polymyxa to prevent scab disease and also enhance plant growth. 2025, Crop Protection Research Centre. All rights reserved. -
Ecological and Socioeconomic Triggers of Forest Fires in Uttara Kannada, India, and Their Impact on Biodiversity Conservation
This study studies the complex cause and consequence aspects of forest fire in the western ghats district of Uttara Kannada, Karnataka, India-a region particularly known for biodiversity. It singles out key factors influencing fire dynamics: natural or anthropogenic elements, such as lightning, droughts, and anthropogenic changes induced by socioeconomic change. Through field information and satellite image analysis, research shows how such climate change and increased human activities continue to fuel rising fire frequency and intensity, posing a threat to the ecological and biodiversity balance within the region. The study conclusion calls for collaborative forest management mechanisms that integrate grassroots practices with wider global conservation visions. This study puts forth actionable recommendations that improve the technique of fire management and prevention. These efforts seek to minimize ecological and economic damage brought about by forest fires. These efforts lead to better comprehension of the concept of ecosystem integrity and bring forward the relevance of preserving biodiversity against climatic and human challenges. 2025 The Author(s). -
Bayesian and Frequentist Estimation of Stress-Strength Reliability from a New Extended Burr XII Distribution
In this article, we propose and study a new three-parameter heavy-tailed distribution that uni-fies the Burr type XII and power inverted Topp-Leone distributions in an original manner. This unification is made through the use of a simple shift parameter. Among its interesting function-alities, it exhibits possibly decreasing and unimodal probability density and hazard rate functions. We examine its quantile function, stochastic dominance, ordinary moments, weighted moments, incomplete moments, and stress-strength reliability coefficient. Then, the classical and Bayesian approaches are developed to estimate the model and stress-strength reliability parameters. Bayes estimates are obtained under the squared error and entropy loss functions. Simulated data are considered to point out the performance of the derived estimates based on the mean squared error. In the final part, the potential of the new model is exemplified by the analysis of two engineering data sets, showing that it is preferable to other reputable and comparable models. 2025, National Statistical Institute. All rights reserved. -
Application of Large Language Models for Data-Driven Analytics in Oncology: Insights and Evidence Generation from Real-World Imaging Data
Breast cancer is one of the most common and serious types of cancer. It can affect people of all ages and genders around the world. The increasing incidence of breast cancer, coupled with its complexity, has placed a significant burden on healthcare systems and patients alike. Traditional diagnostic methods, while effective, often face limitations in early detection and accurate prognosis, which are critical for improving patient outcomes. In recent years, artificial intelligence (AI) and machine learning (ML) are changing the way we solve problems and make decisions in the field of medical diagnostics, enhancing the ability to detect, diagnose and predict breast cancer. However, there are still challenges, such as the need for large and diverse datasets to train these models, making AI tools work smoothly in hospitals, and addressing ethical concerns in healthcare. This paper looks at how AI and ML are used in breast cancer care, especially in analyzing real-world medical data like images, histopathology, and other datasets such as doctor notes & discharge summaries, to identify patterns that may be unnoticeable to medical experts. Large Language Models (LLMs) using embeddings, are highlighted for their capacity to improve the accuracy of image related interpretations, potentially detect early-stage tumours, and predict disease progression and treatment responses. Real-world medical datasets have been collected and analysed using different models. A publicly available Convolutional Neural Network (CNN) and a custom-built Large Language Model (LLM) with embeddings were tested. The Generative AI model achieved 98.44% accuracy, significantly higher than the traditional AI model's 61.72%. Future research can explore how Generative AI can help classify patients based on risk levels. This could lead to personalized treatment plans, reducing unnecessary treatments and improving patients' quality of life. Given the research is primarily focussed on breast cancer, there is an attempt to showcase that by harnessing the power of AI and ML, there is potential to significantly reduce the global burden of breast cancer, offering new avenues for early detection, accurate diagnosis, and tailored therapeutic strategies. Continued research and collaboration among oncologists, data scientists, and policymakers are essential to fully realize the benefits of AI in the fight against breast cancer, ultimately leading to better patient outcomes and a decrease in breast cancer-related mortality. 2025, Modern Education and Computer Science Press. All rights reserved. -
Classification of Medicinal Plant Leaves using Deep Learning Algorithms
This research explores the automated leaf-based identification of medicinal plants, utilizing machine learning and deep learning techniques to address the crucial need for efficient plant classification. Driven by the vast potential of medicinal plants in pharmaceutical development and healthcare, the study aims to surpass the limitations of existing methodologies through thorough experimentation and comparative analysis. The primary goal is to develop a robust and automated solution for classifying medicinal plants based on leaf morphology. The methodology encompasses acquiring diverse datasets. Specifically, set 1 data is processed by applying resizing, rescaling, saturation adjustment, and noise removal, while Set 2 data is processed by applying resizing, rescaling, saturation adjustment, noise removal, and PCA (Principal Component Analysis). The proposed algorithms include Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), YOLOv8, Vision Transformer (ViT), ResNet, and Artificial Neural Networks (ANN). The study evaluates the efficacy and effectiveness of each algorithm in plant classification using metrics such as accuracy, recall, precision, and F1 score. Notably, the ResNet model achieved 93.8% and 94.8% accuracy in Set 1 and Set 2, respectively. The SVM model demonstrated 56.5% and 56.6% accuracy in Set 1 and Set 2, while the Vision Transformer (ViT) model achieved 84.9% and 74.4% accuracy in Set 1 and Set 2, respectively. The CNN model showcased high accuracy at 96.7% and 94.8% in Set 1 and Set 2, followed closely by the ANN model with 96.7% and 96.6% accuracy. Lastly, the YOLOv8 model achieved 96.0% and 95.1% accuracy in Set 1 and Set 2, respectively. The comparative analysis identifies CNN and ANN as the top-performing algorithms. This research significantly contributes to the advancement of medicinal plant identification, pharmaceutical research, and environmental conservation efforts, emphasizing the potential of deep learning techniques in addressing complex classification tasks. 2026, Modern Education and Computer Science Press. All rights reserved. -
Underwater Image Dehazing: A Comprehensive Approach
Underwater imaging in recent times has advanced by trying to correct color distortion, increase contrast, and increase image clarity if the light need is less. The use of deep learning has been effective in enhancing image quality, but challenges persist in the decompression process due to data inconsistencies. In order to do this a new scheme is proposed in this study. Unlike other methods which depend only on the single images captured, here an attempt is made to use images taken in other conditions to overcome this limitation, by using the model to try and improve such underwater images in general irrespective of the water conditions. A key innovation is the disassembly and synthesis of multi-channel illuminance data. Specifically, we decompose the input image into its red, green, and blue frequencies, and then approximate the illuminance component within each channel. By independently manipulating and reconstructing these channel-specific illuminance maps, we can effectively address the non-uniform light scattering and absorption that are characteristic of underwater environments. This allows us to correct for the inherent color casts and haze that degrade image quality. To further refine the enhancement, we incorporate, advanced color correction methods such as image saliency exploration and white balance adjustment to compensate for color attenuation caused by light absorption at different depths. These techniques effectively restore lost colors and enhance contrast, thereby improving image clarity and sharpness. This is helpful in the field of engineering and also forms the foundation for further exploring methods of improving images captured underwater. Investigational outcomes exhibit that the intended method ominously augments image eminence, making it highly effective for underwater detection and exploration tasks, offering an innovative solution for hazy images in various conditions and advancing underwater monitoring and exploration technologies. 2026, Modern Education and Computer Science Press. All rights reserved.