Volume 13 | Issue 4 |

 Abstract

Modern AI practices all strive towards the same goal: better results. In the context of deep learning, the term "results" often refers to the achieved accuracy on a competitive problem set. In this paper, we adopt an idea from the emerging field of Green AI to consider energy consumption as a metric of equal importance to accuracy and to reduce any irrelevant tasks or energy usage. We examine the training stage of the deep learning pipeline from a sustainability perspective, through the study of hyperparameter tuning strategies and the model complexity, two factors vastly impacting the overall pipeline's energy consumption. First, we investigate the effectiveness of grid search, random search and Bayesian optimisation during hyperparameter tuning, and we find that Bayesian optimisation significantly dominates the other strategies. Furthermore, we analyse the architecture of convolutional neural networks with the energy consumption of three prominent layer types: convolutional, linear and ReLU layers. The results show that convolutional layers are the most computationally expensive by a strong margin. Additionally, we observe diminishing returns in accuracy for more energy-hungry models. The overall energy consumption of training can be halved by reducing the network complexity. In conclusion, we highlight innovative and promising energy-efficient practices for training deep learning models. To expand the application of Green AI, we advocate for a shift in the design of deep learning models, by considering the trade-off between energy efficiency and accuracy.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine learning,Hardware Technologies

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Creative Commons Attribution 4.0 and The Open Definition

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The term achievement motivation may be defined by independently considering the words achievement and motivation. Achievement refers to competence (a condition or quality of effectiveness, ability, sufficiency, or success). Motivation refers to the energization (instigation) and direction (aim) of behavior. Thus, achievement motivation may be defined as the energization and direction of competence- relevant behavior or why and how people strive toward competence (success) and away from incompetence. Research on achievement motivation has a long and distinguished history. In fact, researchers have focused on achievement motivation concepts since the emergence of psychology as a scientific discipline (i.e., the late 1800s), when William James offered speculation regarding how competence strivings are linked to self-evaluation. Achievement motivation is currently a highly active area of research, particularly in the fields of educational psychology, sport and exercise psychology, industrial/organizational psychology, developmental psychology, and social-personality psychology. Achievement motivation research is conducted both in the experimental laboratory (where variables are typically manipulated) and in real-world achievement situations such as the classroom, the workplace, and the ball field (where variables are typically measured). The task of achievement motivation by researchers is to explain and predict any and all behavior that involves the concept of competence. Importantly, their task is not to explain and predict any and all behavior that takes place in achievement situations. Much behavior that takes place in achievement situations has little or nothing to do with competence; limiting the achievement motivation literature to behavior involving competence is necessary for the literature to have coherence and structure. That being said, competence concerns and strivings are ubiquitous in daily life and are present in many situations not typically considered achievement situations. Examples include the following: a recreational gardener striving to grow the perfect orchid, a teenager seeking to become a better conversationalist, a politician working to become the most powerful leader in her state, and an elderly person concerned about losing his or her skills and abilities. Thus, the study of achievement motivation is quite a broad endeavor. Emotional Intelligence is most often defined as the ability to perceive, use, understand, manage and handle emotions. Children with high emotional intelligence can recognize their own emotions and those of others, use emotional information to guide thinking and behavior, discern between different feelings and label them.Many different achievement motivation variables have been studied over the years. Prominent among these variables are the following: achievement aspirations (the performance level one desires to reach or avoid not reaching; see research by Kurt Lewin, Ferdinand Hoppe), achievement needs/motives (general, emotion-based dispositions toward success and failure; see research by David McClelland, John Atkinson), test anxiety (worry and nervousness about the possibility of poor performance; see research by Charles Spielberger, Martin Covington), achievement attributions (beliefs about the cause of success and failure; see research by Bernard Weiner, Heinz Heckhausen), achievement goals (representations of success or failure outcome In this context the purpose of the study was to investigate A Study on Achievement motivation and Emotional Intelligence among Secondary school students . The study also aimed to find out the co relation between the variables of the study. The study has been carried on students who were studying in 9th standard in the schools of city of Mysore. The sample of the study consisted of 100 male and female students. The data was collected by applying tools: Mangal Emotional Intelligence Inventory to measure the emotional intelligence of the secondary school students and V P Bhargava Achievement Motivation test to measure the Achievement Motivation of the secondary school students. The result shown that, There is a significant difference between the Achievement Motivation of male and female secondary school students, There is a significant difference between the Emotional Intelligence of male and female secondary school students and There is a positive insignificant relationship between the Achievement Motivation and Emotional Intelligence of secondary school students.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Descriptive survey method, t- test, Achievement Motivation and Emotional Intelligence.

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Creative Commons Attribution 4.0 and The Open Definition

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The exponential growth of Internet of Things (IoT) devices has introduced significant challenges in managing network resources efficiently while ensuring Quality of Service (QoS). The integration of Software-Defined Networking (SDN) with IoT environments has emerged as a promising solution due to its centralized control, programmability, and adaptability. However, dynamic traffic patterns, latency-sensitive applications, and heterogeneous network demands exacerbate the need for intelligent load-balancing mechanisms. This research identifies key limitations in existing QoS-aware load-balancing techniques in SDN-based IoT networks, such as poor scalability, high response time, and uneven resource utilization. To address these challenges, the study proposes a novel framework that focuses on improving load distribution across cloud, fog, and edge infrastructures while meeting QoS requirements. The methodology involves the classification of IoT traffic based on priority and implementing efficient routing and resource allocation strategies using SDN controllers. By leveraging centralized programmability, the proposed approach dynamically allocates resources and reroutes traffic to mitigate congestion and optimize network performance. The findings demonstrate that the proposed framework significantly reduces latency, enhances throughput, and ensures reliability for time-sensitive IoT applications. Comparisons with existing approaches reveal notable improvements in scalability and overall efficiency, making the framework suitable for real-world IoT deployments such as smart cities, healthcare systems, and industrial automation. This research contributes to the advancement of intelligent, QoS-aware load-balancing solutions in SDN-IoT networks and lays the groundwork for future optimizations in network resource management.

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Creative Commons Attribution 4.0 and The Open Definition

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Internet of Things, Load-balancing, Quality of service, SD-IoT, Software- defined networking

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract: The study provides a content analysis of the websites of ten Indian central institutions that are under the ministry provinces. The study identify an analysis of collection and sharing information for end users. In the new internet era, Websites are very common platform to interact with the users. The survey helps to identify an analysis of the available content on websites. The study's objective was to assess the library resources of ten central institutions in India that fall under the ministry province. A checklist has been created in order to gather information from the websites.. The main purpose of this article is very helpful for website developer and administrator. It is perfect to understand current collection of data available on websites, up-to-date information, and user friendly. A websites have service offering information about their collection, e resources, Useful links, hyperlinks, search options and FAQ. This study was limited to assessing 10 out of 56 central universities' performance grades and content. For this paper, observation method has been used for the study.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

World Wide Web (WWW), Library Websites, Central University. Content analysis, Province ministries

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A paper on AI based web development

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Future web

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Creative Commons Attribution 4.0 and The Open Definition

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In the age of artificial intelligence, the role of human creativity in literature is more vital than ever. While AI can generate text, mimic styles, and analyse language, it lacks the emotional depth, cultural context, and imaginative power that define human expression. Through qualitative analysis of selected English novels, the study highlights the emotional nuance, imaginative vision, and cultural resonance unique to human authorship. While AI can generate text, imitate literary styles, and analyse language, it lacks the emotional depth, cultural context, and imaginative power that define human creativity. AI can create artworks and fictions based on the keywords we provide, but they often lack the imaginative soul that humans inherently possess. This article explores the unique and irreplaceable role of human creativity in literature, emphasizing its importance in preserving the soul of literary expression amid technological advancement. The study examines how literature remains a profoundly human endeavour despite the growing influence of artificial intelligence.

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Creative Commons Attribution 4.0 and The Open Definition

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Creativity, Soul, Literature, AI Era, Human Imagination, Emotional Depth

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Creative Commons Attribution 4.0 and The Open Definition

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During the 21st century developing nations underwent major changes in their social and economic structures as digital technology transformed into fundamental systems for governance administration, educational establishments and financial programs. The digital arrival of India faces significant literacy gaps even though the government implements large-scale programs for digitization. A study will then track the digital literacy revolution in India through the evaluation of government plans keeping in mind infrastructure development along with corporate collaborations for universal digital empowerment. The paper uses digital inclusion and liberal-institutionalist frameworks to evaluate the digital ecosystem of India while providing an analytical investigation of its achievements and internal obstacles.

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Creative Commons Attribution 4.0 and The Open Definition

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: Digital Literacy, E-Governance, PMGDISHA, BharatNet, Digital Divide, Public-Private Partnership.

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Creative Commons Attribution 4.0 and The Open Definition

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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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Creative Commons Attribution 4.0 and The Open Definition

 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

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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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Aerobic Training, Flexibility and 't' ratio

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Creative Commons Attribution 4.0 and The Open Definition