Volume 13 | Issue 6 |

 Abstract

The integration of Artificial Intelligence (AI) into recruitment processes has changed traditional hiring methodologies, enabling more subtle assessments of candidates' competencies. This paper presents an AI-driven mock interview system that evaluates both technical proficiencies and soft skills through multimodal analysis. The system employs computer vision techniques to analyse facial expressions and gestures, utilizing datasets like FER+ for emotion recognition. Natural Language Processing (NLP) models, such as BERT, are finetuned to assess the semantic relevance and clarity of candidates' responses. Additionally, computational paralinguistics extract vocal features--intonation, pitch, and speaking rate--to infer affective states and communication effectiveness. By integrating these modalities, the system provides comprehensive feedback, translating complex model outputs into actionable insights, thereby facilitating targeted improvements in interviewee performance. This approach not only enhances the objectivity and efficiency of candidate evaluations but also addresses ethical considerations by ensuring interpretability and transparency in AI-driven assessments.

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 Keywords

Artificial Intelligence, Mock Interview System, Multimodal Analysis, Facial Expression Recognition, Natural Language Processing, BERT, Computational Paralinguistics, Soft Skills Assessment.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Oils contamination has been a global issue, regionally, pollution from oils spills. Oils contain several compounds which are harmful to the environment, several ways have been implemented to curb the effects of such compounds in the environment. Oil pollution from spills, industrial discharge, and urban runoff poses severe environmental and health risks to human, aquatic life, as well as plants. Microbial degradation has proved to be a more effective and reliable way of remediating polluted environments. Certain Oils like Petrol Gasoline contains Petroleum Hydrocarbons. The study focus on the degradation of Petroleum hydrocarbons by bacteria, the bacteria was extracted from soil from a petrol gasoline pump. The study was to show factors which influence the degradation process, ph. ,oxygen levels, temperature and nutrient availability. The bacteria was able to simplify the harmful compounds into non toxic substances, since it uses Petroleum hydrocarbons as source of carbon. With the presence of oxygen the aerobic mechanism became possible and more effective. The study also indicates factors that could also enhance the efficiency of the biodegrading bacteria in degrading of Petroleum hydrocarbon. Genetic engineering has proved to be an essential tool in modifying and amplifying bacteria on its capability to degrade Oils. Highlighted in the study as well is the issues encountered in the process of bioremediation of environments affected by oil spills, or the existence of Petroleum Hydrocarbon, it is expensive to carry out such bioremediation processes. The paper also discusses future directions in place to enhance biodegradation by microbials, such as synthetic biology on bacteria to make it more effective towards conversion of Petroleum Hydrocarbons. Since this topic has been a sensitive issue ,more resources have been put in place for more research on how to reduce pollution by oil spills.

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 Keywords

Microbes degrading oils

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In current era, Artificial Intelligence (AI) is an emerging, innovative and speedy development digital technology. The application of AI has become more and more obvious technology in education, especially teaching and learning activities. The uses of AI in education can assist teachers to enhancing their teaching efficacy for effective teaching. AI is promoting the teaching efficacy among teachers for meaningful instruction, efficient classroom activities and quality of teaching. Also it helps to improve the students' adaptive learning and online learning activities. The aim of the study is to ascertain the level of use of uses of AI in teaching efficacy of prospective teachers. A survey method was used for data collection through questionnaire (Utilisation of artificial intelligence in teaching efficacy). The samples of 300 prospective teachers were selected by stratified random sampling technique. The samples were studied in B.Ed., colleges of Education of Namakkal district, Tamil Nadu. The questionnaire was consists of 40 statements based on the Likert scale. The responses were 'always' (3), 'often' (2) and 'sometimes' (1). The findings of the results revealed that the prospective teachers have high level of utilisation of AI in teaching efficacy. Also, the study found the results showed that there is no significant difference in utilisation of AI in teaching efficacy among prospective teachers with respect to their categorical variables such as gender, locality of prospective teachers, medium of instruction, stream of subject, age, computer knowledge and AI used in teaching. The suggestion of the study is the prospective teachers are using the AI in teaching it enhance to effective teaching efficacy in their classroom activities.

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 Keywords

Prospective Teacher, Teaching Efficacy, Artificial Intelligence (AI), Utilization.

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India has '140 million hectares' of net cropped area, next only to that of the 'USA'. Similarly, India's irrigated area is also the second largest in the world, next only to 'China'. The country is well-endowed with natural resources and varied climatic circumstances, and much of the land in India can be double cropped. Traditionally, crop production has accounted for over four-fifths of the agricultural output, but over the past two decades or so the condition has changed dramatically. The share of livestock in the agricultural production has risen sharply and now accounts for close to 30 per cent of the total agricultural output. Overall, the composition of agricultural output has gradually been shifting towards high-value crops and animal products, especially milk. The performance of agricultural sector has been quite inspiring, making the country self-reliant in food. The country has even started exporting some food products. This performance is due largely to 'green revolution'. The Present review paper emphasis on trends, challenges, evolution and policy frame work on Agriculture sector in India.

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 Keywords

Agriculture, Animal Products, Climate, Crop, Food, Green revolution and Policy

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Creative Commons Attribution 4.0 and The Open Definition

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Women who underwent cesarean delivery, physical wellbeing, psychological wellbeing, associated factors.

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Creative Commons Attribution 4.0 and The Open Definition

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The swift increase of digital information in personal, educational, and business areas has generated an urgent demand for trustworthy, scalable, and easily accessible storage options. Conventional local and physical storage systems frequently fail to meet contemporary challenges like remote access, scalability, real-time collaboration, and data protection. This study details the creation and implementation of a Virtual Storage System (VSS) that provides a secure platform for file storage, management, and retrieval accessible via the cloud over the internet The system incorporates vital functions including secure user verification, role-based access management, file version control, and backup systems. It offers an intuitive web interface enabling users to carry out essential file tasks such as uploading, downloading, sharing, and deleting. Attention is focused on maintaining data integrity and confidentiality by utilizing encryption methods and resilient architecture. The proposed system underwent assessment for performance, usability, and security, showcasing its ability to act as a cost-efficient and effective substitute for conventional storage systems in both academic and industrial environments.

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 Keywords

Virtual Storage Framework; Cloud-Based Storage; Data Protection; File Administration; User Verification; Data Encryption; Role-Specific Access Control; Online Application; Data Archiving; File Revisioning.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Tomato cultivation plays a crucial role in global agriculture and economy; however, it is often challenged by various diseases affecting plant health and yield. Timely detection and management of these diseases are vital for ensuring crop productivity and food security. In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown promising results in automating disease detection tasks in plants. This study proposes a novel approach for detecting tomato leaf diseases utilizing CNNs. The proposed model leverages the power of deep learning to automatically learn discriminative features from input images, enabling accurate classification of healthy and diseased tomato leaves. The dataset used for training and evaluation comprises a diverse range of tomato leaf images representing different disease classes. Experimental results demonstrate the effectiveness of the proposed method in accurately identifying various tomato leaf diseases, outperforming traditional machine learning approaches and achieving competitive performance compared to existing deep learning-based methods. The developed model offers a valuable tool for early and accurate diagnosis of tomato leaf diseases, facilitating prompt intervention and effective management strategies for ensuring sustainable tomato production.

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 Keywords

Tomato cultivation, Convolutional Neural Networks (CNN), Deep Learning, leaf diseases, plant health

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Creative Commons Attribution 4.0 and The Open Definition

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Linear Programming was conceived in the period of the second world war to address defense needs. Dantzig, who developed LP modelling and simplex algorithms, tried to use LP for his personal diet plan (meeting his nutritional needs at minimum cost). He could not sustain the approach because of various factors. In the later history of LP modelling, diet problems have been addressed in various settings like hospitals, industries and with additional objectives like environmental impacts, cultural acceptance. One of UN Sustainability goals is meeting nutrition needs of children. In India programs like Akshaya Patra provide midday meals for school going children. Everyday millions of school kids are fed by such programs. Can we do what Dantzig did for himself (diet planning) for every school going kid? Such goals are now attainable and sustainable with modern developments. Quantum Computing and Artificial Intelligence will help us to realize this. Quantum computers using features of the quantum world (superposition and entanglement) allow us to solve problems which are not practically tractable for classical computers and provide exponential speedup in time. Quantum algorithms (like Quantum Approximation Quantum Algorithm) have been conceived for linear programming and tested. Artificial Intelligence can now provide user-friendly support for data extraction, model formulation, solution links and interpretation of results for action for various kinds of user settings. The paper develops a road map for Quantum AI Linear Programming decision support for personalized diet plans for members of massive organizations like national midday meals for school kids.

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 Keywords

Linear Programming, Diet Plan, Artificial Intelligence, Quantum Computing,Quantum Algorithms, National Midday Meals Scheme.D:Waves System.

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Creative Commons Attribution 4.0 and The Open Definition

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Solanum lycopersicum, commonly known as tomato, belongs to the Solanaceae family. It is a herbaceous, dicotyledonous plant found in tropical and temperate climates, including India, China, and the United States. This plant contains of carotenoids ,vitamins,phenolic compound and other bioactive compound like desmethyl-sterols and ?- and ?-tocopherols. Along with the tocopherols, delta-5-avenasterol, the desmethyl sterols, and citrostadienol that contribute to multiple biological activities, such as anti-inflammatory, antimicrobial, neuroprotective, anti-diabetic, antioxidant, cardioprotective, anti-cancer, and benefits for gut and skin health. This review aims to evaluate the hormonal potential of Solanum lycopersicum leaf extracts by examining their phytochemical composition.

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Solanum lycopersicum, bioactive compounds, tocopherols, antioxidant, anti-inflammatory, hormonal potential, phytochemical composition.

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 Abstract

Orally fast dissolving strip is a novel drug delivery dosage form design and develop as an alternative to orally conventional drug delivery system such as tablet, capsule , syrup . Recent trends are shifting towards developing an innovative drug delivery system to improve safety , efficacy and patient compliance by fast dissolving technologies , which dissolves within a minute in oral cavity .

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Fast dissolving flim , mouth ulcer , strip, pain relief

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Creative Commons Attribution 4.0 and The Open Definition