Volume 14 | Issue 6 |

 Abstract

The fast development of generative artificial intelligence has made synthetic media production to be democratized, allowing creating deep fakes that could be genuinely realistic and endanger the quality of digital evidence. In this paper, the author evaluates the problems facing digital forensics and the entire judicial framework, whereby the integrity of multimedia data is core to the ruling. Based on an extensive overview of existing types of detection methods, such as convolution neural nets, transformer architectures, and novel forensic paradigms, this study examines their relevance to the process of evidentiary authentication. The phenomenon of anti-forensics, whereby the adversarial methods are used to circumvent detection systems, is also explored in the study leading to an increase in technological arms race. By relying on current expert surveys and significant research projects, including the DETECTOR project conducted by the EU, the presented paper has suggested a multi-layered framework applicable to deep fake evidence authentication, combining technical detection methods with chain-of-custody and forensic examiner training and legal admissibility guidelines. These findings clarify the fact that despite the development of the detection algorithm, to discover a long-term solution, the systems level interventions, including the provenance infrastructure, regulatory models, and institutional adoptions are required to sustain the trust in digital evidence.

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 Keywords

Generative AI,Synthetic media ,Deepfakes, Digital forensics, Judicial framework ,Multimedia integrity, Detection methods, Convolutional neural networks , Transformer architectures

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Tobacco use among adolescents remains a serious public health concern in India, particularly in North Eastern states such as Mizoram. Despite awareness campaigns, early initiation of tobacco use continues due to peer pressure, cultural normalization, and limited health education.To assess the effectiveness of a Structured Teaching Programme (STP) on knowledge regarding tobacco use and its harmful effects among adolescents in selected schools of Aizawl, Mizoram.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Evaluate, Effectiveness, Structured Teaching Programme, Knowledge and tobacco use and its effects.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid growth of Amazon e-commerce has increased the need for intelligent product optimization techniques that can improve product visibility, search ranking, and customer reach. Manual keyword research is often time-consuming, inconsistent, and unable to effectively capture competitor trends and semantic relevance. To address this issue, this project proposes an AI-based keyword recommendation framework for Amazon e-commerce sellers using Deep Learning, Natural Language Processing, and Machine Learning techniques. The system is developed using the Django framework and analyzes product titles, bullet points, descriptions, categories, seed keywords, and competitor product data to generate relevant and competitor-aware keyword suggestions. The proposed framework applies advanced NLP preprocessing methods such as lemmatization, part-of-speech tagging, named entity recognition, and noun phrase extraction to clean and structure product text. Candidate keywords are extracted using a hybrid approach that combines KeyBERT, RAKE, YAKE, n-gram generation, and noun phrase extraction. Semantic similarity is computed using Sentence Transformer embeddings, while machine learning models such as XGBoost and LightGBM are used to improve keyword ranking based on relevance, phrase quality, and competitor evidence. The system also supports duplicate keyword removal, keyword placement suggestions, competitor evidence tracking, and export of results in CSV and PDF formats. Experimental evaluation using multiple product categories shows promising performance in terms of accuracy, precision, recall, F1-score, Mean Reciprocal Rank, and NDCG. The proposed system provides a scalable and practical solution for Amazon sellers to improve product discoverability, optimize listing content, and make data-driven keyword decisions using AI-powered keyword intelligence

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Deep Learning, Natural Language Processing, Keyword Recommendation, Amazon E-Commerce, Semantic Search, Machine Learning, KeyBERT, XGBoost, Django, Information Retrieval

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 Abstract

This study assesses the land suitability for sandalwood cultivation in Chamarajanagar District, Karnataka, using Geographic Information System (GIS) and Multi-Criteria Decision Analysis (MCDA) techniques. Ten environmental and geographical parameters, rainfall, elevation, slope, soil moisture, soil organic carbon, soil pH, NDVI, land use/land cover, Topographic Wetness Index (TWI), and Topographic Ruggedness Index (TRI), were integrated through a weighted overlay analysis in ArcGIS. The Analytical Hierarchy Process (AHP) was employed to assign weights based on the ecological requirements of sandalwood. The resulting suitability map classified the district into five categories: Very Highly Suitable, Highly Suitable, Moderately Suitable, Not Suitable, and Highly Not Suitable. The study demonstrates the effectiveness of GIS-based spatial analysis in identifying optimal areas for sustainable sandalwood cultivation and resource management.

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 Keywords

Sandalwood Land Suitability, Chamarajanagar, GIS, Multi-Criteria Analysis. Analytical Hierarchy Process (AHP).

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 Abstract

Urban land lease policies play an important role in shaping access to urban land, wealth accumulation, and socioeconomic opportunities in rapidly urbanizing cities. This study examined the distributional impacts of urban land lease policies across socioeconomic groups in Addis Ababa, Ethiopia. Specifically, it assessed trends in income distribution and inequality, identified factors influencing urban land lease outcomes, and evaluated public perceptions regarding the effects of land lease policies on inequality and land ownership concentration. A cross-sectional mixed-methods research design was employed. Data were collected from 384 households selected through stratified random sampling across five sub-cities of Addis Ababa, complemented by key informant interviews, focus group discussions, and document review. Descriptive statistics, Gini coefficient analysis, Pearson's Chi-square tests, trend analysis, and Ordinary Least Squares (OLS) regression were used for data analysis. The findings revealed that income inequality increased between 2008 and 2024, with the Gini coefficient rising from 0.39 to 0.47. The survey data produced an adjusted Gini coefficient of 0.631, indicating substantial inequality among respondents. Regression results showed that advance payment, bidder experience, economic growth, government subsidy, zoning classification, land use regulations, and proximity to infrastructure positively influenced land lease outcomes. Age was the only demographic variable significantly associated with perceptions of inequality and land ownership concentration. The study concludes that urban land lease policies contribute to unequal access to urban land and recommends more equitable and transparent land allocation mechanisms.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Urban land lease policy, income inequality, land ownership concentration, socioeconomic groups, Addis Ababa.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study investigates the socio-economic and relational impacts of digital colonisation and platform capitalism on Myanmar-owned hospitality micro, small, and medium enterprises (MSMEs) operating within Thailand. While public narratives routinely position third-party digital infrastructures as neutral mechanisms of economic empowerment, this research exposes them as highly asymmetrical mediating architectures. Using an interpretivist qualitative approach, the study conducts an enterprise-level analysis through semi-structured interviews with 20 purposively sampled Myanmar entrepreneurs across restaurants, cafes, guesthouses, small hotels, and online retail spaces in major urban centers such as Bangkok. The empirical findings demonstrate that integration into digital ecosystems does not automatically equate to digital justice. Instead, platforms function as extractive digital gatekeepers that centralize core business dynamics--including discoverability, algorithmic visibility, reputational metrics, and consumer transactional data. This infrastructural dependency subjects resource-constrained migrant business owners to steep commission arrangements and opaque, externally governed rule-making frameworks. This structural vulnerability is further compounded by localized, migrant-specific contextual factors, including linguistic barriers, restricted capital access, regulatory limits, and legal precarity in the host country. Consequently, the research reframes digital transformation from a purely managerial efficiency paradigm into a critical conflict of data power and corporate hegemony. Finally, the paper outlines practical coping mechanisms deployed by migrant entrepreneurs to mitigate platform reliance and foster direct consumer loyalty, while providing strategic recommendations for policy-makers and non-governmental organizations to cultivate more equitable and resilient environments for inclusive digital development.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Digital Colonisation; Platform Capitalism; Hospitality SMEs; Migrant Entrepreneurship; Platform Dependence; Data Colonialism; Digital Inclusion; Asymmetrical Power Dynamics; Thailand

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Healthcare delivery systems across the world largely depend upon the efficiency, competency, and quality of nursing services. Nurses constitute the largest group of healthcare professionals and remain in continuous contact with patients throughout hospitalization. They play a central role in assessment, planning, implementation of care, monitoring of patient conditions, medication administration, infection prevention, emotional support, health education, and coordination with multidisciplinary healthcare teams. Because of this continuous involvement in patient care, the quality of nursing services directly influences patient outcomes, patient satisfaction, hospital reputation, and overall healthcare standards.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Healthcare delivery systems across the world largely depend upon the efficiency, competency, and quality of nursing services.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Healthcare has evolved from a purely service-oriented sector into a highly competitive and patient-driven industry. Multispecialty hospitals, which offer a wide range of medical services under one roof, have become an essential part of modern healthcare systems. These hospitals provide comprehensive care across various specialties such as cardiology, orthopedics, neurology, oncology, and general medicine, making them a preferred choice for patients seeking quality and convenience. In recent years, the rapid growth of private healthcare institutions and increasing patient awareness have intensified competition among hospitals. As a result, traditional methods of attracting patients are no longer sufficient. Hospitals are now adopting structured and strategic marketing approaches to differentiate themselves, enhance their reputation, and ensure patient satisfaction. Marketing in healthcare is not limited to advertising but includes building trust, maintaining long-term relationships, and delivering value-based care. Marketing strategies in multispecialty hospitals encompass a wide range of activities, including branding, digital marketing, patient relationship management, service quality improvement, and community outreach programs. The integration of digital platforms such as websites, social media, and online consultation services has further transformed how hospitals interact with patients. These tools help hospitals reach a broader audience, improve accessibility, and provide timely information. Moreover, patient expectations have significantly increased, with individuals seeking not only effective treatment but also a positive overall experience. Factors such as hospital infrastructure, staff behavior, transparency in pricing, and post-treatment care play a crucial role in influencing patient decisions. Therefore, hospitals must adopt a patient-centric approach in their marketing strategies to ensure satisfaction and loyalty. This study focuses on analyzing the various marketing strategies adopted by multispecialty hospitals and evaluating their effectiveness in attracting and retaining patients. It aims to provide insights into how healthcare institutions can utilize modern marketing techniques to achieve sustainable growth while maintaining high standards of patient care.

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MARKETING STRATEGIES OF MULTISPECIALITY HOSPITALS

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 Abstract

Diesel engines remain essential power units in transportation, marine systems, construction equipment, industrial generators, agricultural machinery, and remote energy applications. Despite their robustness, diesel engines are exposed to operational failures caused by abnormal temperature rise, oil pressure loss, excessive vibration, overspeed, overcurrent, lubrication degradation, sensor malfunction, and delayed human intervention. Conventional engine protection systems are predominantly reactive because they activate shutdown only after a critical threshold has already been exceeded. Such abrupt shutdown strategies may prevent catastrophic failure but can also increase mechanical stress, interrupt operational continuity, and provide limited diagnostic intelligence for condition-based maintenance. To address these limitations, this paper proposes a Smart Predictive Shutdown System for Diesel Engines as an IoT-enabled framework that integrates real-time sensing, intelligent fault prediction, multi-level decision logic, gradual shutdown control, and condition-based protection. The proposed framework continuously monitors critical engine parameters, including temperature, oil pressure, vibration, current, and rotational speed, and processes these signals through threshold-based rules, moving-average trend analysis, anomaly detection, and lightweight predictive logic suitable for implementation on microcontrollers or programmable logic controllers. Unlike traditional emergency shutdown systems, the proposed architecture introduces a graded protection strategy consisting of warning, alarm, soft shutdown, and immediate shutdown states. The soft shutdown mechanism is designed to reduce load progressively before complete engine stop, thereby minimizing thermal and mechanical shock under non-critical but deteriorating conditions. Furthermore, the framework supports local data logging, cloud-based monitoring, smart alerts, self-diagnostics, and backup protection when sensor failure is detected. The scientific contribution of this study lies in transforming diesel engine protection from a reactive safety mechanism into an intelligent predictive protection system that combines industrial IoT, condition monitoring, fault prognostics, and adaptive safety control. The proposed system provides a practical and scalable pathway for improving diesel engine reliability, reducing unplanned downtime, supporting condition-based maintenance, and enhancing operational safety in industrial environments.

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 Keywords

Diesel engine protection, predictive shutdown, Internet of Things, condition-based maintenance, fault prediction, gradual shutdown, intelligent monitoring, industrial IoT, engine safety, predictive maintenance.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: New media, social media, wildlife conservation, human-wildlife conflict,

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Creative Commons Attribution 4.0 and The Open Definition