Volume 13 | Issue 4 |

 Abstract

The surge in online job portals has greatly expanded access to employment opportunities worldwide. However, this increased convenience has also led to a rise in fraudulent job postings, putting job seekers at risk of identity theft, financial scams, and other threats. This paper introduces an integrated Fake Job Detection and Sentiment Analysis System aimed at improving the credibility of job listings through the application of machine learning and natural language processing techniques. The system utilizes a Random Forest Classifier, trained on the Fake Job Post dataset, achieving a detection accuracy of 98%. To capture user sentiment, a Bidirectional Long Short-Term Memory (Bi-LSTM) model is trained on the Glassdoor Review dataset, reaching a sentiment classification accuracy of 63%. The proposed dual-layered architecture supports real-time authenticity validation and sentiment-based feedback analysis, enhanced by an intuitive feedback interface and an administrative dashboard for manual review and trend tracking. Unlike traditional approaches that treat detection and sentiment analysis as separate components, our system unifies both into a cohesive, scalable platform. It is adaptable to diverse job markets and offers potential applications across job portals, recruitment sites, and employer branding initiatives, fostering greater trust and minimizing users' exposure to fraudulent employment opportunities.

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 Keywords

Fake job detection, Random Forest, Sentiment analysis, Bi-LSTM, Machine learning, Recruitment security, Natural language processing.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Malicious websites or uniform resource locator (URLs) are a huge concern in the field of cyber-security. It has always proven to be a threat to society to access such malicious websites that result in comprising the system. Many cases have occurred where malicious websites have penetrated user's computer and their privacy was compromised. These websites are a host to various cyber tools that are used to remotely control or corrupt a device. The cyber tools used are spams, malwares, trojan horses and many more. This has resulted in various losses throughout the world not only financial but also emotional. This has made these malicious urls a global threat. Traditional classification methods include blacklists, periodic reporting and signature comparisons based on data volume, changes, processes over time and relationships between features. In our project we have tried to use the deep learning approach to detect and block these websites. Also, as mentioned visiting such malicious websites result compromises the device's security, downloading malware containing files is also a huge concern. Many websites that are pirated are available on the internet which might contain downloadable malware files. Therefore, there is a need to block such downloads before they are downloaded and attack the system. The Malwares are responsible for corruption of file and this results in compromising the user's privacy as well as his financial loss. To eliminate this problem, we are trying to develop a extension that is trained as a deep learning model that blocks the download before completion and helps the user to stay away from malwares.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Malicious URL detection, Deep learning, LSTM, Malware detection, Chrome extension, Cybersecurity, Static feature analysis, Threat prevention.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The purpose of the present study was to investigate the Effect of aerobic training on flexibility among college women hockey players. To achieve the purpose of the study thirty women hockey players were selected from Sri Sarada College of Education. The subject's age ranges from 18 to 24 years. The selected players were divided into two equal groups consists of 15 women players each namely experimental group and control group. The experimental group underwent an aerobic training programme for six weeks. The control group was not taking part in any training during the course of the study. Flexibility were taken as criterion variable in this study. The selected subjects were tested on flexibility by sit and reach test. Pre-test was taken before the training period and post- test was measured immediately after the six week training period. Statistical technique 't' ratio was used to analyse the means of the pre-test and post test data of experimental group and control group. The results revealed that there was a significant difference found on the criterion variable. The difference is found due to aerobic training given to the experimental group on Flexibility when compared to control group.

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 Keywords

Aerobic Training, Flexibility and 't' ratio

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Afghanistan Mein Bharat Ki 'Soft Power' Ranniti Aur Pakistan Ki Vifalta

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Afghanistan Mein Bharat Ki 'Soft Power' Ranniti Aur Pakistan Ki Vifalta

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

BACKGROUND: Tuberculosis is specific infectious disease caused by Mycobacterium tuberculosis. The disease primarily affects the lungs and causes pulmonary tuberculosis. It can also affect other body parts such as intestine,meninges,bones and joints, lymph gland,skin and other body parts.DOTS remain central to public health approach to tuberculosis control, which is now presented as Stop Tuberculosis strategy to ensure cure by providing the most effective medicine and confirming that it is taken. It is only strategy, which has been documented to be effective worldwide on a programme basis. ASHA workers are designated as DOTS providers, they go house to provide the DOTS medicines to the community peoples. OBJECTIVES: The study aims to assess the effectiveness of Planned teaching Programme on level of the knowledge regarding DOTS therapy among ASHA workers. STUDY DESIGN AND METHODOLOGY: For this study a Pre -experimental design was selected with non prabability purposive sampling tecnique. Data was collected from 200 ASHA workers of Primary Health Centre,Misrod Bhopal, Madhyapradesh .Tool consists of part -I sociodemographic Variables and Part -II consists of knowledge questionnaire collecetd data was analyzed by descriptive and inferential statistics. The difference between the Pre-test and Post -test mean knowledge score of ASHA workers regardig DOTS therapy was statistically significant. CONCLUSION: It concluded that Planned Teaching Programme (PTP) was effective in enhancing the knowledge of ASHA workers regarding DOTS therapy.

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 Keywords

tuberculosis, mycobacterium tuberculosis, pulmonary tuberculosis, dots, public health, asha workers, planned teaching programme, knowledge assessment, primary health centre, bhopal, madhyapradesh, pre-experimental design, non-probability sampling, knowledge questionnaire, statistical significance

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Road maintenance is a critical aspect of infrastructure management to ensure safety and efficiency in transportation networks. Traditional manual methods of detecting road cracks are labor-intensive, time-consuming, and prone to human error. To address these challenges, this study proposes an automated road crack detection system leveraging advanced image processing and deep learning techniques, specifically Convolutional Neural Networks (CNNs). The workflow includes collecting road surface images, preprocessing through resizing, normalization, and data augmentation, and training a CNN-based model for crack detection. The model is fine-tuned using hyperparameter optimization and validated for generalization to unseen data. Once deployed, the system performs real-time crack detection, evaluates crack severity, and generates comprehensive reports. The detected cracks are stored in a database to facilitate maintenance planning, prioritization, and repair scheduling. This automated approach significantly improves detection accuracy, reduces processing time, and enables scalable road infrastructure management. The proposed system demonstrates the potential to enhance road safety and optimize maintenance strategies through intelligent and efficient crack detection mechanisms.

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 Keywords

Road Crack Detection, Image Processing, Deep Learning, Convolutional Neural Networks (CNNs), Machine Learning, Surface Defects, Edge Detection

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Technology is seen as an advancement to reduce the burden. Digitalization is the trend of the era. Day to day work in every area of work is not only enabling the working crowd to perform their work easier but also making results more accurate and reliable unlike the traditional process. This process has been taken over by the present digitalization where many software applications are in use to make the process faster and more efficient in contrast to traditional decision making which involve long process and many panels of discussion to come up with the best decision. Digitization leverages technologies and digital tools to enhance or redefine business processes and models. As technology continues to shape the workplace, employers are adopting strategic approaches to ensure the effective integration of new tools to make decisions more effective without much affecting the finance of the business. Through an analysis of current trends this article provides insight into how various digital tools are contributing to strategic decision-making process of the management. how it can contribute to more inclusive and sustainable job creation. The aim of this article is to figure out how digital tools are playing a pivotal role in decision making process of the management in recent years and has brought a tremendous change in the Business scenarios.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Digitalization, Decision making, Digital tools, Management

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Creative Commons Attribution 4.0 and The Open Definition

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Adolescence is a time of increased vulnerability to mental health problems, including depression, anxiety, and peer pressure. Many adolescents have difficulty expressing their emotions effectively, which leads to low self-esteem, relationship problems, and low self-esteem. This systematic review aimed to examine the effectiveness of art therapy in improving adolescent mental health. The PICOTS (Population, Intervention, Comparison, Outcome, Time, and Setting) framework guided the identification of relevant research studies. Results from the American Art Therapy Association show that art therapy has a positive effect on overall mental health; regardless of previous art experience. Art therapy has shown promising results in treating a variety of mental health issues. Future research should examine the long-term effectiveness of art therapy for specific mental health diagnoses, examine its integration into treatment plans and educational settings, and evaluate its cost-effectiveness. Art therapy offers a valuable non-verbal therapeutic approach for adolescents. It provides a safe and supportive space for emotional expression, self-disclosure, and the development of coping mechanisms.

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 Keywords

Art Therapy, Anxiety, Depression, Self-Expression, Colour, Emotion, Intervention, Mental Health, Therapy, Adolescents

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Leukemia, also known as blood cancer is a dangerous disease that arises in the bone marrow and overproduces malformed white blood cells. These cells make blood unhealthy and weaken body's ability to fight with infection. Leukemia is categorized based on speed of progression [Acute and Chronic] and the types of white blood cell affected [lymphocytic or myeloid]. Due to high risk factor, it requires accurate and timely diagnosis for effective treatment. Current advancements in Machine learning (ML) have revealed promising diagnostic results. This study aims to provides a complete overview of different Machine learning and Deep learning Techniques applied for the diagnosis of leukemia, highlighting the parameters like accuracy and number of features extracted, using different segmentation techniques.The paper explores the application of various Machine Learning (ML) algorithms, with Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Image Processing techniques, demonstrating outstanding results in a specific area. These algorithms have shown outstanding success in tasks like image classification, object detection, and medical image analysis. Our review highlights the capability of ML in enhancing leukemia diagnosis accuracy, reducing diagnosis time, and improving patient outcomes

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 Keywords

Leukemia, image processing, Machine learning, SVM.

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Creative Commons Attribution 4.0 and The Open Definition

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In today's global society, skilled cross-cultural communication is crucial, as language connects individuals, businesses, and nations, necessitating efficient translation systems. This review analyzes AI-driven translation, discussing advancements, challenges, and ethical considerations while exploring key debates, methodologies, and unresolved questions in the field. It highlights innovations and limitations in AI translation, emphasizing its impact on multilingual communication and cultural understanding. Ensuring accuracy, adaptability, and inclusivity in machine translation is essential for ethical AI development. The study underscores the importance of responsible AI practices, advocating for transparency and fairness in translation technologies. Future research aims to enhance cross-lingual adaptability and improve AI-driven translation systems. Inclusion and cultural awareness remain central priorities in the evolution of AI translation, shaping its role in global communication.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial intelligence, Language translation, Machine translation, Neural Machine Translation, Statistical Machine Translation, Natural Language Processing

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Creative Commons Attribution 4.0 and The Open Definition