Volume 13 | Issue 4

 Abstract

Artificial Intelligence (AI) has emerged as a powerful tool in various domains, and the field of drug discovery and drug delivery is no exception. This review article aims to provide an overview of the applications of AI in drug discovery and delivery and explore its future prospects. The article begins by introducing the fundamental concepts of AI. Furthermore, the article discusses the AI-powered software or tools including for drug discovery, in nanotechnology and in drug formulation and delivery. Finally, the article presents the future prospects of AI in drug discovery and drug delivery systems.

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AI in drug discovery, AI in drug delivery systems, and AI-powered software or tools.

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To making herbal powder shampoo

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2

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The Digital Gram Panchayat streamlines rural governance by providing an accessible online platform for administrative services. It enables villagers to apply for certificates, pay taxes, and stay informed about government schemes, fostering transparency and efficiency. The system enhances citizen engagement, reduces bureaucratic delays, and ensures real-time tracking of applications, ultimately empowering rural communities with seamless digital governance.

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Digital Governance, Gram Panchayat, E-Governance, Rural Development, Online Public Services, Citizen Engagement, Tax Payment, Digital Administration

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This paper explores the intersection of cybersecurity and international relations, specifically through the lens of the 2007 cyber attack on Estonia. The study investigates how the attack marked a critical point in understanding the role of threats in global politics and security.By examining the attack's causes, impacts, and responses , this paper illustrates the emerging significance of cybersecurity as a component of statecraft and international diplomacy.

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o Cybersecurity o Cyber Warfare o Cyber Threats o Estonia Cyberattack 2007 o International Relations o Cyber Defense o Distributed Denial of Service (DDoS) o NATO Cyber Policy o Tallinn Manual o Sovereignty in Cyberspace

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This paper is basically consists of study of gait pattern characteristics of varying height in male and female

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Gait pattern characteristics step length step width

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Plagiarism is becoming a greater issue in academics and digital publications because it is so simple to get and reproduce content online. Traditional methods of detecting plagiarism, like manual comparison and keyword matching, are usually time-consuming and unreliable. An effective machine learning-based method for identifying plagiarism in text data is presented in this research. The system use Natural Language Processing techniques to extract features and eliminate noise from text using Term Frequency-Inverse Document Frequency vectorisation. A Logistic Regression classifier is then trained to distinguish between content that has been plagiarised and that has not using tagged text samples. The model achieves high accuracy, demonstrating effective categorization performance. A web-based interface created with Flask is also part of the project, which makes it simple for users to upload and check content for plagiarism. With potential uses in publishing, content management systems, and educational institutions, this automated technology provides a scalable, accurate, and user-friendly approach to plagiarism detection.

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Plagiarism Detection, Machine Learning, TF-IDF, Support Vector Classifier, Flask Application

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In today's fast-paced world, accessing timely medical care can be challenging due to long wait times, scheduling difficulties, and a lack of proper guidance. The Doctor Appointment System addresses these issues by integrating AI and machine learning to create a more efficient and accessible healthcare experience. It ensures secure access with OTP-based authentication, preventing unauthorized logins. A machine learning-powered disease prediction model helps patients analyze symptoms and gain preliminary insights before consulting a doctor, while an AI-driven chatbot provides instant assistance, answering queries and guiding users through the appointment process. To enhance convenience, the system includes a Nearby Hospital Locator, allowing users to search for hospitals based on their city. An admin-controlled scheduling system manages appointments efficiently, enabling administrators to approve or reject requests based on doctor availability while maintaining a specialist patient limit to avoid overbooking. Additionally, a feedback system allows users to report issues and provide input for continuous improvements. By integrating secure authentication, AI-driven assistance, predictive analytics, and location-based hospital search, this system enhances accessibility, reduces waiting times, and ensures a smooth and organized appointment booking process for both patients and healthcare providers.

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The Doctor Appointment System, Patient Module, Doctor Module, Administrator Module, Disease Classification Algorithm, Machine Learning, RASA Chatbot, Appointment Management, Doctor Recommendation.

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This research undertakes a comprehensive investigation into the social security experiences of retired employees, encompassing a detailed examination of their demographics, social security usage, and challenges encountered. Additionally, it conducts an assessment of government-provided schemes aimed at promoting elderly welfare, alongside a comparative analysis of social security systems in less developed countries. Employing a mixed-methods approach, this study seeks to identify key areas for improvement and inform policy recommendations designed to enhance the overall well-being of retired employees.

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retired employees, social security, elderly welfare, government initiatives, comparative analysis.

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The Census 2011, being a nationwide enumeration of households and their characteristics, provides a comprehensive baseline understanding of the housing situation in rural India. It captures crucial data points such as the type of dwelling (pucca, semi-pucca, kutcha), the number of rooms, and the availability of basic amenities. This macro-level data highlights the extent of housing inadequacy across different regions and socio-economic groups. PMAY-G strategically leverages the detailed household-level information within the SECC 2011, a subset of the Census focused on socio-economic indicators, to pinpoint the most deprived families. The deprivation criteria outlined under PMAY-G, such as households with no shelter, living in kutcha houses with specific room limitations, households headed by single women, or those with disabled members, are directly derived from the data collected in SECC 2011. This targeted approach ensures that the benefits of the housing scheme reach those in the greatest need, promoting social equity and inclusive development. The Gram Sabha's involvement in validating the beneficiary lists generated from the SECC 2011 adds a layer of grassroots verification and ensures that local contexts and genuine needs are considered. While the Census 2011 provides the initial framework for identification, the ongoing implementation of PMAY-G and subsequent surveys may reveal evolving housing needs and demographic shifts that future census exercises will further illuminate, creating a continuous feedback loop for refining housing policies.

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pradhan mantri awas yojna,gramin, rural development, housing scheme

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The Pradhan Mantri Gramin Awas Yojana (PMGAY), launched in 2016 by the Government of India, is a flagship rural housing scheme aimed at providing "Housing for All" by 2028. The target under the PMAY-G was construction of 2.95 crores pakka houses with basic amenities by march 2024, out of which almost all houses have been sanctioned and 2.69 crores houses have been completed till end of the financial year 23-24. The program is implemented under the Ministry of Rural Development and provides financial assistance of INR1.2 lakh for plains and INR1.3 lakh for difficult terrains. PMGAY focuses on social inclusion by giving preference to marginalized groups including Scheduled Castes, Scheduled Tribes, minorities, and women. Beneficiaries are selected based on the Socio-Economic and Caste Census (SECC) 2011, ensuring transparency and targeting those most in need. One of the key features of the scheme is convergence with other government schemes like Swachh Bharat Abhiyan (toilets), Ujjwala Yojana (LPG connection), Saubhagya Yojana (electricity), and the National Rural Drinking Water Programme, enabling a holistic approach to rural development. By empowering rural communities with secure housing and basic amenities, PMGAY plays a critical role in reducing poverty, enhancing dignity, and promoting socio-economic upliftment. The scheme also boosts local employment and construction activities, contributing to rural economic growth. As PMGAY progresses toward its goal of "Housing for All," it continues to represent a major stride in India's mission to ensure sustainable and inclusive rural development.

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Housing scheme, pradhan mantri, yojna, pmay, Indra Awas Yojana, pradhan mantri awas yojana

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Social media marketing has emerged as a transformative force in the digital age, reshaping the way businesses engage with consumers. This study explores the impact of social media marketing on consumer behavior, brand awareness, and business performance. Through a review of recent literature and case studies, the research highlights how platforms such as Facebook, Instagram, Twitter, and TikTok provide cost-effective and interactive channels for targeted advertising, customer engagement, and brand loyalty. The findings suggest that businesses leveraging data-driven strategies and user-generated content tend to achieve higher customer retention and improved market reach. However, the study also discusses challenges such as algorithm dependency, privacy concerns, and managing negative feedback. Overall, social media marketing proves to be a vital tool for contemporary marketing, offering both opportunities and risks in an increasingly connected marketplace.

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challenges and barriers , business outcomes

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The objective of this study is to determine the elements that play an important role on consumer's buying behavior. The purpose of this research is to find out the main important factors related with the packaging effect, which are driving the success of a brand. Companies in order to create the right packaging for their products, they must understand the consumer buying process and understanding the role and the impact of packaging as a variable that can influence the purchase decision. So, by understanding what factors influence the buying behavior and what packaging elements are most important help companies making the right decisions about packaging their products. This research will identify the relationship between consumer buying process as the main variable of the study and some independent variables like packaging colour, printed information, packaging material, design of wrapper, printed information, brand image, and innovation and practicality that help consumers in their decision buying process.

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Packaging, Consumer buying behavior, Marketing, Branding, Product design

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The pharmaceutical industry generates a significant amount of waste, ranging from chemical solvents to packaging materials. Effective recycling methods are essential for reducing the environmental impact and achieving sustainable production. This descriptive study aims to explore and evaluate the recycling methods widely used in the pharmaceutical industry. Data was gathered through literature reviews, case studies, and industry reports to identify the most prevalent practices and innovations. The study focuses on key areas such as solvent recovery, waste-to-energy conversion, plastic and glass recycling, and management of hazardous materials. Results indicate that solvent recovery through distillation is one of the most commonly employed methods, with high efficiency in cost and waste reduction. Plastic and glass recycling initiatives are also widely adopted, particularly in packaging lines, driven by stricter environmental regulations and corporate sustainability goals. Advanced techniques, such as chemical recycling and bioremediation, are gaining traction for managing complex and hazardous pharmaceutical waste. While these methods contribute significantly to minimizing waste, challenges remain, including high initial costs, regulatory compliance, and the need for specialized infrastructure. The study concludes by recommending integrated waste management systems, cross-industry collaborations, and investment in cutting-edge recycling technologies to further enhance sustainability in the pharmaceutical sector. This study provides valuable insights for stakeholders in the pharmaceutical industry, policymakers, and researchers, paving the way for more eco-friendly practices and innovation in waste recycling.

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Pharmaceutical industry, recycling methods,waste management, hazardous waste, packaging recycling, environmental impact.

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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.

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

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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.

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

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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.

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ECC

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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.

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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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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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