Volume 13 | Issue 12 |

 Abstract

The existing system for screening and ranking resumes is done manually in which recruiters rank resumes based on their company policies. It is not only time-consuming but also prone to human error and often influenced by biased opinions. Automated-resume screening has grown in popularity as it is more efficient and accurate. The main goal of this system is to help recruiters and job-seekers to recruit and get recruited, respectively. Not only we develop our system that does what traditional AI-based resume rankers do but also gives valuable insights and detailed analysis of our users that could improve their resumes ultimately making them more deserving candidates for that job position.

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 Keywords

Intelligence, Machine Learning, Resume Parse, Weighting Filter, RegEx, NLP

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The objective of the study is to develop or construct a Scientific Knowledge and Aptitude Test. 200 items were initially selected with 60 items from Life Science, 80 items from Physical Science and 60 items from Mathematics. A draft test was administered on 150 boys and girls of Class IX and 50 boys and girls of class X English Medium Secondary Schools of Kolkata. The major purpose of the study was to find out the difficulty value and discriminating power of the items by the method of item analysis, Where N = 150, boys and girls of Class Ix, difficulty value has ranged between .12666 to .79333 and discriminating power between .0118824 and .902439. The difficulty values were determined in terms of the samples passing an item and discriminating power was determined as per difference of such proportion between the top and bottom 27% of the sample groups. 100 items were selected for the final item pool and was administered on 99 boys and 127 grills (N=226 of the standardization sample. The range of Difficulty value and discriminating power of the final 100 item pool was 0.1681415 to 0.7654867 and 0.0044642 to 0.7951388 respectively. From the range of difficulty value and discriminating power of the final 100 items, it can be revealed that the items are spreaded equally on both the sides of .50 the items included was from the easiest to the most difficult the sampling was purposive.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Aptitude, Difficulty value, Discriminating power, Scientific knowledge,

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

We present an analysis of the volume dependence of Grüneisen parameter and its higher order derivatives q and ƛ for five geophysical minerals viz. MgO, CaO, MgSiO3 , CaSiO3 and Pyrope garnet ( Mg3Al2Si3O12). The Burakovsky – Preston model has been used for representing gamma as a function of volume for the entire range of compressions. We have determined and reported values of γ, q and ƛ at different values of volume compressions. Values of these thermoelastic parameters ( γ, q and ƛ) have been transformed to the corresponding pressures using the Holzapfel equation of state for the minerals under study.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Grüneisen parameter ; Higher order derivatives; Equation of State ; Burakovsky – Preston model; Geophysical minerals.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Artificial Intelligence (AI) has changed something so that it becomes much better in various aspects of our lives, offering solutions to numerous problems and bridging gaps between reality and business. Within the domain of AI, emerging technologies such as machine learning and deep learning models have played an important role in transforming the way so that we can analyse data, make decisions, and can take action or give attention to difficult situations or problems, with an objective to understand them to find solutions. Liver disease encompasses a wide range of conditions that impair the liver's ability to function properly, leading to serious health issues. Risk factors for liver disease include viral infections (e.g., hepatitis B and C), excessive alcohol consumption, obesity, metabolic disorders, and genetic predisposition. Symptoms vary depending on the disease's severity but commonly include fatigue, jaundice, abdominal pain, and swelling. If the lever disease can be detected in early life, certain preventive measures can be taken so that the patient can recover from lever disease and can enjoy a real and happy life. Here machine learning models and ensemble methods of machine learning models have been applied for selecting a proper model for prediction of lever disease of the person. Under machine learning models Random forest, decision tree, gradient boosting, KNN(K nearest neighbour), logistic regression have been used. Under ensemble methods, voting classifier using maximum voting, average voting, Blending, Bagging and boosting, stacking of models have been used.

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 Keywords

machine learning models, Random forest, decision tree, gradient boosting, KNN(K nearest neighbor), Ensemble techniques, voting classifier using maximum voting, average voting, Blending, Bagging and boosting.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rise of multidrug-resistant (MDR) microorganisms is a major challenge for global public health in the 21st century. Traditional antimicrobial treatments are losing their effectiveness, making it necessary to look for alternative therapies. Bioactive phytocompounds from medants have shown prosing antimicrobial properties and could serve as valuable resources for developing new antimicrobial agents. This review looks at current knowledge of various phytocompounds, how they work against MDR pathogens, their synergistic effects with standard anttibiot, and thential clinical uses. We discuss major classes of phytocompounds, including alkaloids, flavonoierpenoids, phenolic compouuuunds, annnnnd esseemphasiffectiveness against resistant bacteria, fungi, and parasites. We also address the challenges of standardization, bioavailability, and to with future opportunities for turning these natural compounds into efffective treatments.

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 Keywords

Bioactive comds, multidistance, antimicrobial activity, medicinal plants, alternative therapy

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The main objective of this research is to develop a standardized evaluation for Scientific Knowledge and Aptitude. A total of 100 items were given to 226 students (99 boys and 127 girls, N=226) from classes X, XI, and XII. The study employed Purposive Sampling. To determine reliability, the KR-21 formula was used, resulting in a reliability of .93614 for Life Science, .81553 for Physical Science, .85806 for Mathematics, and .88750 for the overall test. Validity was assessed through inter-item consistency using the inter-correlation method across all subgroups and subtests, with correlations ranging from .488 to .729, and the inter-correlation between subtests and the full test ranged from .773 to .940. For External Validity, the scores from the Madhyamik Examination (class X school leaving certificate examination conducted by the West Bengal Board of Secondary Education) in Life Science, Physical Science, and Mathematics were correlated with the standardized test scores, with correlations ranging from .266 to .663. The study suggests that more randomization is needed in the sampling process.

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 Keywords

Scientific knowledge, Aptitude, Purposive sampling ,Life science ,Physical science, Mathematics, Reliability, validity ,correlation.

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract Artificial Intelligence (AI) and Machine Learning (ML) are increasingly transforming the landscape of personalized medicine by enabling patient-specific therapeutic strategies and optimizing drug formulation design. Personalized medicine aims to tailor treatments based on individual genetic, physiological, and lifestyle factors, improving therapeutic efficacy while minimizing adverse effects. Traditional drug formulation approaches often rely on trial-and-error methods, which are time-consuming and resource-intensive. AI and ML offer powerful tools to overcome these limitations by analyzing complex multidimensional datasets, predicting patient responses, and optimizing formulation parameters such as solubility, stability, and drug release profiles. Recent studies demonstrate that ML algorithms, including supervised, unsupervised, and reinforcement learning, can accurately predict drug behavior and guide the development of novel delivery systems such as 3D-printed tablets, nanoparticles, and controlled-release formulations. Despite their potential, challenges remain, including data quality, regulatory approval, and the interpretability of AI-driven models. Future directions include the integration of multi-omics data, real-world patient information, and explainable AI frameworks to enhance predictive accuracy and clinical applicability. This review summarizes current advancements in AI/ML applications for personalized drug formulation, highlighting trends, key findings, challenges, and prospects for the next generation of patient-tailored therapeutics.

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 Keywords

Keywords: 1. Artificial Intelligence (AI) 2. Machine Learning (ML) 3. Personalized Medicine 4. Drug Formulation Design 5. Predictive Modeling 6. Advanced Drug Delivery Systems 7. 3D Printing in Pharmaceuticals 8. Precision Medicine

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Creative Commons Attribution 4.0 and The Open Definition

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The current study examines how online communities are redefining accountability, and create an opportunity for individuals to feel free to exercise their expression. To estimate how social media platforms such as Facebook and Twitter frame international communication, the study relies on secondary data from scholarly research, court cases, and reputable web sources. While all of these digital environments carry the potential for misinformation, hate speech, or the censoring of content, they may also be an opportunity for democratic participation. This study highlights struggle with encouraging free expression confined within democratic values of establishing and regulating protections against harmful expression by codifying human rights protections and balancing regulation with the notion of proportionality. Essentially, it was assessed that the over-regulation of free expression or entirely unregulated free expression was not beneficial for democratic values. The study concludes with recommendations for increasing digital literacy, a more balanced approach to regulating free expression and formalizing accountability mechanisms about expressing one's speech that is open and transparent, in an equitable and safe online space to better encourage free expression

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Freedom of Expression, Digital Media Platforms, Social Media Regulation, Legal Accountability, Platform Liability

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Creative Commons Attribution 4.0 and The Open Definition

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Rapid urbanization, chronic traffic congestion, and the pervasive reliance on private two-wheelers necessitate the scaled adoption of shared, sustainable transport solutions in Indian cities. This empirical investigation addresses the limited academic focus on two-wheeler sharing dynamics by proposing and validating an integrated theoretical framework: the Extended Technology Acceptance Model (TAM) combined with the Theory of Planned Behavior (TPB), augmented by the context-specific variables of Cost-Effectiveness (CE) and Trust and Safety Perception (TS). Primary quantitative data were collected via a structured questionnaire employing a 5-point Likert scale from 120 respondents--predominantly young working professionals and students--across three socio-economically diverse cities (Pune, Vadodara, and Daman). Analysis utilized correlation and multiple regression techniques to test the proposed causal relationships. The empirical findings establish a clear hierarchy of drivers: economic feasibility (CE, Mean=4.32) and functional utility (Perceived Usefulness, Mean=4.21) are the strongest positive predictors of adoption intention. Conversely, Trust and Safety Perception, which registered the lowest mean score (3.78), was identified as the key psychological barrier, directly moderating the conversion of high user intention into consistent, habitual usage. The extended model demonstrated robust explanatory power, collectively accounting for 73% of the variability in user adoption behavior (R^2=0.73). The results confirm the superior utility of incorporating contextual factors in predicting adoption within price-sensitive emerging markets. Managerially, the study underscores the critical need for platforms to invest heavily in operational integrity, visible vehicle quality control, and robust safety protocols to solidify user trust and accelerate the sustainable urban mobility transition.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Shared Mobility, Two-Wheeler, Technology Acceptance Model, Theory of Planned Behavior, User Adoption, Emerging Market, Cost-Effectiveness, Trust.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The human gastestinal microbiome, made up of trillions of microorganisms, acts as a metabolically active organ that plays a crucial role in host physiology, immune development, and susceptibility to disease. Dysbiosis, which refers to an imbalance in microbial community composition and diversity, has been linked to various health issues, including inflammatory bowel diseases, metabolic disorders, neuropsychiatric conditions, and immune dysfunction. Probiotics, defined as live microorganisms that provide health benefits when taken in appropriate amounts, and prebiotics, which are non-digestible food components that stimulate the growth of beneficial microbiota, are effective methods for modifying the microbiome. This review summarizes the mechanisms and clinical evidence supporting the use of probiotics and prebiotics in maintaining gastrointestinal health and regulating the immune system. We explore major probiotic genera like Lactobacillus, Bifidobacterium, and Saccharomyces, their specific effects, categories of prebiotics such as inulin-type fructans and galacto-oligosaccharides, and synbiotic combinations. Strong clinical evidence shows these interventions can prevent antibiotic-associated diarrhea, manage symptoms of irritable bowel syndrome, maintain remission in ulcerative colitis, and shorten the duration of acute infectious diarrhea. Immunological mechanisms include strengthening the epithelial barrier, producing antimicrobial peptides, excluding pathogens, generating short-chain fatty acids, modulating dendritic cell maturation, and inducing regulatory T-cells. While generally considered safe, it's important to pay attention to strain-specific issues, the number of viable organisms, storage conditions, and possible adverse effects in immunocompromised individuals. Global regulations differ, with changing requirements for proving health claims. Future research will focus on next-generation probiotics like Akkermansia muciniphila and Faecalibacterium prausnitzii, postbiotic metabolites, personalized microbiome interventions based on individual profiles, and broader applications in metabolic health, mental wellness, and immune regulation.

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 Keywords

Keywords: Probiotics; Prebiotics; Gut Microbiome; Immunity; Dysbiosis; Short-Chain Fatty Acids; Lactobacillus; Bifidobacterium; Irritable Bowel Syndrome; Intestinal Barrier

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Creative Commons Attribution 4.0 and The Open Definition