IJO -International Journal Of Computer Science and Engineering (E:ISSN: 2814-1881) (P.ISSN: 1595-935X)

DOCUMENT-DRIVEN AI SOLUTIONS FOR TALENT OPTIMIZATION

2025-04-25T12:45:19+00:00

The recruitment process is a critical function in modern organizations, and manual screening of resumes is often time-consuming and error-prone. This paper proposes a structured and semi-automated framework for extracting key resume attributes using AI and ranking candidates through a configurable scoring model. By integrating natural language processing, automation tools, and visualization platforms, the system enhances decision-making efficiency for recruiters. The solution uses modular Python scripts and APIs to parse resumes, score candidates, and present the results through dashboards, with an optional feedback loop for further evaluation. In particular, the proposed system leverages the capabilities of GPT-3.5 through LangChain to semantically understand resume content, enabling more accurate parsing and ranking. The system architecture supports end-to-end automation, starting from resume intake via email, intelligent parsing, rule-based scoring, and finally, interactive result visualization.

Indian cuisine analysis

2025-04-30T07:59:37+00:00

Indian cuisine is diverse and rich, with regional variations in ingredients, preparation styles, and taste preferences. This project aims to analyze Indian recipes using data science and machine learning techniques to uncover patterns related to regional cuisines, nutritional information, ingredient popularity, and recipe clustering. By utilizing various visualization and classification models, this study helps understand how different factors influence Indian food. The results can be useful for culinary businesses, health-conscious individuals, and cultural researchers.

Stress Sentiments via Emotion Detection

2025-05-02T11:24:44+00:00

Suicide prevention is a critical task that requires early intervention and support. This project aims to develop an emotion detection system that can identify suicidal sentiments in text data, enabling timely interventions. Using NLP and ML techniques, our system will analyze text inputs and detect emotions associated with suicidal ideation, such as hopelessness, despair, and distress. Our goal is to create a tool that can accurately identify individuals at risk and provide resources and support to help them cope with their emotions and overcome suicidal thoughts.

Effect of Artificial Intelligence on Performance of Public Sector Service Organizations with Employee Competency and ICT Strategy mediated by Employee Performance

2025-05-21T12:04:16+00:00

This research study is mainly focused on improving performance of public sector organizations. The public sector plays important role in countries. It has the responsibility of making policies towards betterment of the citizens. There are various public sector institutions established to deliver services to the citizens in the countries under the control of governments.Some of the services are issuing permits, generating passports, providing health related services, preparation of birth and dead certificates. Most of these services are essential services requested by citizens and people seek effective and efficient service from the public institutions. It has been identified that the citizens are unsatisfied about the services delivered by public sector institutions in countries. The main reason for the unsatisfaction is weak performance shownby the public sector.According to one of the research studies, it has been identified that the performance is low because of the negative impact coming from the employees who are serving in public institutions. The absenteeism, lateness, laziness, and less commitment are some of the factors which are leadingfor negative impact of the employees. Hence this research study is designed to find the effect of artificial intelligence for public sector performance with the support of employee competency and ICT strategy while mediating employee performance. The dependent variable is defined as performance of public sector service organizations (PPSO). There are three independent variables are named as employee competency, ICT strategy and artificial intelligence. Employee performance is the mediating variable in the study. It has been formed eight hypotheses based on the conceptual framework. The quantitative methods are applied to conduct analysis of the research. it is developed a questionnaire consisting of questions which are based on variables mentioned in the framework.The Likert scale is considered in this questionnaire. In Sri Lanka, divisional secretariats are established under public sector to serve citizens by delivering essential services.It has been selected employees who are working in western province divisional secretaries to represent population of the research.The random sampling technique is used to filter respondents required to answer thequestionnaire. Once responses are collected through self-administrated questionnaire, data cleaning techniques areused to clean data avoiding missing data, out of range data and outliers. The data cleaning process isconducted using SPSS software application. The frequency analysis, cross tabulation and Mahalanobis functionalities are important among these techniques.The Histogram, Q-Q plot, and box plot techniques are used to find normality of data collected.The correlations among variables are identified by employing variance inflation factor (VIF) supported by SPSS while heteroscedasticity is tested through scatter plot technique. Once data cleaning process is over,descriptive statists are generated to understand data. Exploratory factor analysis (EFA) is executed to find relationships between items in the questionnaire. Here it is used cronbach's alpha for reliability testing purpose. After conducting exploratory factor analysis, it is performed confirmatory factor analysis (CFA) using AMOS software application to receive structural equation model (SEM). It has been presented key findings by this study based on final SEM. According to the findings,there are some relationships exists between artificial intelligence and performance of public sector service organizations,between employee performance and performance of public sector service organizations,between employee competency and employee performance. In addition, it found that employee performance mediates the relationship between employee competency and performance of public sector service organizations.