Volume 13 | Issue 4 |

 Abstract

The development of intelligent healthcare solutions has transformed disease diagnosis and treatment recommendation processes, addressing critical needs in personalized and accessible healthcare. This research presents an AI-driven, web-based disease prediction and recommendation system built using the Flask framework, which integrates machine learning models with symptom-based user inputs to offer personalized healthcare recommendations. Using a Support Vector Classifier (SVC) trained on symptom-disease datasets, the model predicts likely diseases based on user-inputted symptoms, then offers targeted recommendations encompassing medication, dietary suggestions, exercise routines, and preventive precautions. The system holds both technical and clinical significance, combining data-driven methods with healthcare-specific recommendations to streamline diagnosis and treatment. Technical contributions include efficient data integration from diverse sources--symptom records, medication lists, and disease information--to create a robust recommendation engine capable of supporting healthcare providers and patients alike. Clinically, the model addresses common diagnostic challenges, such as symptom ambiguity, by utilizing a probabilistic approach to enhance diagnostic accuracy. Moreover, the system prioritizes user accessibility through an intuitive web interface, making it suitable for both clinical and individual use. This AI-powered recommendation system has substantial societal implications, especially for remote or underserved populations with limited access to medical care. By providing preliminary diagnostic insights and personalized recommendations, the model can bridge gaps in healthcare access and potentially reduce the burden on healthcare facilities. In summary, this research contributes to the ongoing integration of AI in healthcare, demonstrating a scalable, user-friendly model that can enhance early diagnosis and support better health outcomes, ultimately advancing the accessibility and personalization of medical services.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Disease Prediction, Machine Learning, Python Programming, Healthcare Analytics, Predictive Modeling, Medical Data Analysis, Health Informatics

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Children are among the most vulnerable members of society, and the growing incidence of sexual offences against them underscores the urgent need for comprehensive legal safeguards. In India, the Protection of Children from Sexual Offences (POCSO) Act, 2012, and the Juvenile Justice (Care and Protection of Children) Act, 2015, form the legislative bedrock for addressing such crimes--both from the perspective of victim protection and juvenile justice. This research undertakes a critical examination of these two pivotal laws, assessing their effectiveness in preventing abuse, delivering justice, and ensuring the welfare of affected children. The study explores the practical challenges involved in their implementation, including institutional shortcomings, enforcement bottlenecks, and judicial interpretations. It further highlights how legal provisions interact with child psychology and procedural sensitivities. By analyzing relevant case law, policy frameworks, and ground realities, the paper aims to evaluate the extent to which these laws have succeeded in their objectives. Finally, it offers constructive recommendations to strengthen the protective mechanisms for children, contributing to the broader goal of building a safer, more just society for the nation's youth. Furthermore, the paper discusses persistent issues such as victim stigmatization, delayed justice, lack of child-friendly legal processes, and the need for specialized training among stakeholders. By identifying systemic loopholes and offering evidence-based recommendations, this research seeks to contribute to ongoing policy reforms and advocate for a more sensitive, child-centric approach to justice. Ultimately, it emphasizes the importance of collaborative efforts in creating a safe and supportive environment for every child, thereby steering society towards a more secure and compassionate future.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

POCSO Act 2012, Juvenile Justice Act, Child protection laws India, Legal framework for child safety, Juvenile justice system

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

On behalf of technology scaling, on-chip memories in a die undergoes bit errors because of single events or multiple cell upsets by the ecological factors such as cosmic radiation, alpha, neutron particles or due to maximum temperature in space, leads to data corruption. Error detection and correction techniques (ECC) recognize and rectify the corrupted data over communication channel. In this paper, an advanced error correction 2-dimensional code based on divide-symbol is proposed to weaken radiationinduced MCUs in memory for space applications. For encoding data bits, diagonal bits, parity bits and check bits were analyzed by XOR operation. To recover the data, again XOR operation was performed between the encoded bits and the recalculated encoded bits. After analyzing, verification, selection and correction process takes place. The proposed scheme was simulated and synthesized using Xilinx Vivado implemented in Verilog HDL. Compared with the well known existing methods, this encoding-decoding process consumes low power and occupies minimum area and delay.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

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ECC

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This research investigates variations in decision-making approaches between individuals from North India and South India, focusing on cultural, psychological, and organizational influences. The study evaluates how regional traditions, historical contexts, and societal values impact five decision-making categories: analytical, emotion-driven, group-oriented, delay-prone, and impulsive strategies. A blended methodology was adopted, incorporating a 20-item survey calibrated for cultural relevance and distributed to 209 participants (balanced regional representation), covering diverse age groups (19-55), genders, and professions. Advanced statistical techniques, including factor validation and multivariate testing, were used to isolate cultural factors, while frameworks such as individualism-collectivism theory contextualized behavioral outcomes. Core insights reveal that North Indian respondents prioritized methodical decision-making, marked by structured evaluation, risk mitigation, and adherence to established norms--traits consistent with societal emphasis on hierarchy and individual achievement. South Indian participants, however, leaned toward emotionally informed choices, reflecting adaptive, context-sensitive reasoning, potentially shaped by communal values and indirect communication practices. Collaborative decision-making was more frequent in South Indian groups, aligning with collective social structures, whereas North Indians often deferred decisions in high-pressure scenarios, possibly due to fear of individual accountability. Impulsive choices showed minimal regional disparity, though younger demographics universally favored rapid, instinctive actions. The analysis emphasizes cultural self-awareness--recognizing inherent cognitive biases--as vital for reducing misinterpretations in cross-cultural collaborations, such as multinational team projects. Practical applications include adapting leadership models (e.g., task-focused strategies for North Indians vs. relationship-driven approaches for South Indians) and developing region-specific evaluation metrics for workplace assessments. Policymakers could apply these insights to improve intercultural training programs, while integrating cultural flexibility indices may enhance global personality assessments. Theoretical advancements explore potential neurobiological foundations for decision-making styles, suggesting brain network variations (e.g., analytical vs. emotional processing pathways) as areas for future inquiry. Limitations include urban participant overrepresentation and snapshot data collection, which may exclude rural or less-educated perspectives. Subsequent studies should employ longitudinal tracking to observe cultural adaptation over time and neuroscientific methods (e.g., brain imaging) to map decision-making neural pathways. Comparative subregional analyses (e.g., Gujarat vs. Kerala) and qualitative fieldwork could further elucidate localized cultural nuances. By merging behavioral science and cultural anthropology, this work advances understanding of how societal norms and cognitive patterns jointly shape decision architectures in evolving global landscapes.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cultural decision-making, Analytical reasoning, Emotion-driven choices, Collective social dynamics, Cognitive bias mitigation, Individualism-collectivism theory, Leadership adaptation, Neurobehavioral correlates, Intercultural training.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Influencer Marketing, Consumer Trust,,Skincare Industry, Digital Marketing, Social Media Influencers, Brand Engagement, Market Segmentation, Consumer Behavior, Authenticity in Marketing, Micro-Influencers, Macro-Influencers, Skincare Product Recommendations, Brand Loyalty, Purchase Intentions, Social Media Advertising, Online Consumer Perception, Content Marketing, Transparency in Influencer Marketing, Digital Advertising Trends, Influencer Endorsements, Consumer Psychology, Product Reviews, Tru

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Creative Commons Attribution 4.0 and The Open Definition