Volume 13 | Issue 4 |

 Abstract

The adoption of e-banking services has transformed the banking sector, offering convenience and efficiency to customers. However, employees in private sector banks face several challenges in adapting to this technological shift. This paper investigates the key obstacles faced by bank employees in adopting e-banking services in the private sector banks of Tirupur. By exploring various factors, including technological barriers, lack of training, resistance to change, and the impact of organizational culture, the study sheds light on the difficulties encountered by employees in integrating e-banking solutions into their daily operations. Additionally, the paper examines how these challenges affect employee productivity, customer service, and overall job satisfaction. The findings underscore the importance of providing comprehensive training programs, fostering a culture of innovation, and addressing technical challenges to ensure smooth adoption of e-banking services and to enhance overall organizational performance.

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 Keywords

E-banking, Employee Challenges, Technology Adoption, Training, Resistance to Change.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Agriculture is a backbone of the global economy, and empowering farmers with advanced technology and tools can enhance productivity and sustainability. Traditional agricultural methods were based on manual tools, natural fertilizers, practices like crop rotation and rainwater harvesting to sustain productivity and soil health. Farmers used their predictive, observational knowledge for weather, crop prediction and recommendation natural pest control remedies, though these methods lacked scalability and resilience against environmental changes. Machine learning offers a promising solution by providing the prediction and recommendation capabilities by analyzing vast datasets, including soil, weather, and crop patterns, to forecast yields, recommend and predict suitable crops, and optimize fertilizer usage. This paper proposes a machine learning-based crop, fertilizer recommendation and prediction, weather and rainfall prediction. The system uses 4 features for crop prediction, 8 features for crop recommendation, 9 features for fertilizer recommendation and 7 features for yield prediction. Along with tools for farmer's assistance like news feed, chatbot support. We evaluate four models--Decision Tree Classifier, Random Forest Classifier, Random Forest Regressor and Statistical Analysis --showing that the models achieve high accuracy by leveraging data-driven patterns, feature importance analysis to optimize predictions and recommendations for agricultural needs.

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 Keywords

Machine learning, Agriculture, prediction, Recommendation, Decision Tree, Random Forest classifier, Random Forest Regressor, Statistical Analysis, Recall, F1 score.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A global challenge that has become a menace is the example of cancers such as lung and colon cancer (LC AND CC) Colon cancer(CC). The path of this research, therefore, in this very important area, is early detection through the use of AI in histological image analysis. The study introduces a novel hybrid feature set aiming to improve classification accuracy by integrating DenseNet201 with color histogram techniques. The validation of this feature set works with eight major ML algorithms-KNN, SVM, Light GBM, CatBoost, XGBoost, decision trees, random forests, and multinomial naive Bayes. This comprehensive study, therefore, highlights an exotic model that achieved an accuracy of 99.683% on the LC AND CC25000 dataset. Taking the same concept to breast cancer detection using the Break His dataset shows a great accuracy of 94.808%. These results highlight the revolutionary potential of AI in the challenging area of histopathological analysis and therefore stand to become a transformative player in accelerating diagnostic accuracy. A thorough comparative analysis showcases the strengths and weaknesses of current AI practices in medical imaging, outlining a pathway for improvement and future clinical application.

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 Keywords

Densenet201, histopathological images, image processing, lung and colon cancer, machine learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The construction industry in India faces increasing pressure to meet international quality standards and customer expectations. This study explores the application of Total Quality Management (TQM) and Six Sigma methodologies within the sector to enhance construction quality and project performance. It emphasizes TQM as a comprehensive philosophy involving continuous improvement, employee participation, and customer satisfaction across all project stages. Through a detailed case study of a construction project. The paper evaluates the quality of concrete work using Six Sigma performance metrics. The findings reveal disparities in quality perceptions among stakeholders and identify barriers such as inadequate training, communication gaps, and poor quality control practices. By integrating TQM principles with Six Sigma tools, the research advocates for a structured quality management framework, including stakeholder training, establishment of quality circles, and development of national bodies to promote quality awareness. The proposed model offers a practical approach for enhancing competitiveness and achieving defect-free construction outcomes.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

TQM, SIX Sigma

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Image steganography has recently received much attention as a form of secure communication whereby one or more hidden data are embedded into an image. Among the most widely used techniques for data hiding is the Least Significant Bit (LSB) method, where the least significant bits of pixel values are replaced with secret data. However, LSB based steganography often results in visible distortions that can be detected easily. This paper proposes an improved method that utilizes image balancing methods prior to data embedding to reduce such distortions. The method consists of preprocessing the image to balance its brightness, contrast, and colour balance in such a manner that the hidden data will not significantly alter the visual look of the image. The secret data is then embedded into the least significant bits of the balanced image. Experimental results have shown The proposed method successfully conceals data and keeps the quality of the cover image intact. Besides, extraction of the hidden information is carried out with less error rate, indicating the success of the proposed method in both security and visual integrity. This approach is tested through the usage of image quality metrics and accuracy of data extraction that makes it a good approach for practical use in secure communication.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Image Steganography, Least Significant Bit (LSB), Image Balancing, Data Hiding, Image Processing.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Keywords: Drotaverine HCl, Aceclofenac, UV spectrophotometry, Simultaneous equation method, Method validation

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Creative Commons Attribution 4.0 and The Open Definition

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A decentralized social media application using Ethereum redefines the nature of interaction on the internet with an emphasis on privacy, ownership, and transparency. There is no one point of control such as for central sites, making censorship and abuse of data unthinkable. User activity like posting and sharing is controlled by automatically acting smart contracts that bring in trust and finality. Web3 wallets like MetaMask eliminate the traditional logins with added security and reduced data breaches. Direct users own their contents, encouraging the existence of an uncensored society. A rewarding system compensates users for user participation in blockchain tokens, encouraging user-led and fair social networking.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Ethereum, Metamask, Smart Contact, IPFS, Block chain

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the new evolving world, traffic rule violations have become a central issue for majority of the developing countries. The numbers of vehicles are increasing rapidly as well as the numbers of traffic rule violations are increasing exponentially. Managing traffic rule violations has always been a tedious and compromising task. Even though the process of traffic management has become automated, it's a very challenging problem, due to the diversity of plate formats, different scales, rotations and non-uniform illumination conditions during image acquisition. The principal objective of this project is to control the traffic rule violations accurately and cost effectively. The proposed model includes an automated system which uses camera to capture video. The project presents Automatic Number Plate Recognition (ANPR) techniques and other image manipulation techniques for plate localization and character recognition which makes it faster and easier to identify the number plates. After recognizing the vehicle number from number plate the SMS based module is used to notify the vehicle owners about their traffic rule violation. An additional SMS is sent to Regional Transport Office (RTO) for tracking the report status

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Motivation In all the countries there are driving rules available for people to drive carefully by being honest and respecting. When these rules are broken it is defined as a road violation. There road violations that happens in day to day traffic. For an example most common violations such as red light violation, over-speeding, overtaking other vehicles through double white lines and single white lines. These road protocols are applied thoroughly for the places where most of the accidents happe

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This study analyzes the methods through which Rupi Kaur writes and spreads poetry on Instagram by examining the impact digital media including Instagram has had on modern literary practice. Social media has influenced the development of Insta poetry into its hybrid format which unites brief texts with basic images for delivering interactive reader experiences. Kaur employs online platforms to reshape poetic expression through her poetry which deals with subjects such as love healing and feminism along with identity exploration. The Instagram visual format enables poets to develop designs and spacing arrangements that generate more impactful emotional responses. The analysis of Kaur's work pertains to digital humanities because this discipline focuses on understanding technological intersections between literature and art and humanistic knowledge. Social media enables her poetry to achieve worldwide distribution which increases both accessibility and literary inclusivity for all readers. Insta poetry allows users to engage right away with their interaction through various social functions that transform it into a dynamic literary encounter beyond traditional academic spheres. Digital poetry receives opposite reactions between those who view it as a democratic art form reaching diverse audiences and individuals who consider it cheapens the art while transforming it into a commercial product. Digital media has not replaced poetry but transformed it according to Kaur's success story which created new opportunities for poets to engage with their audience. The research demonstrates that technological advancements have turned poetry into a more vibrant form that both readers and viewers can experience easily along with its refined visual presentation. Kaur's Insta poetry creates a major transformation in contemporary literature which generates essential dialogues concerning the digital modifications of poetry.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Digital Poetry, Rupi Kaur, Instapoetry, Social Media, Visual Identity, Digital Humanities

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Distributed Denial-of-Service (DDoS) attacks remain a persistent and evolving threat in the contemporary digital landscape, capable of inflicting significant financial losses, reputational damage, and operational disruptions. While rudimentary DDoS attacks emerged in the late 1990s, the threat landscape has dramatically transformed. Today's DDoS attacks leverage vast botnets, often comprising compromised Internet of Things (IoT) devices, and employ sophisticated techniques such as amplification and application-layer attacks to overwhelm targets. These attacks can cripple critical infrastructure, disrupt online services, and extort businesses. This survey paper provides a comprehensive overview of DDoS attacks, including their historical evolution, contemporary attack strategies, and the diverse array of tools used by threat actors. It further categorizes and analyzes the expanding arsenal of attack and defense mechanisms. Finally, it identifies critical research gaps and proposes directions for future work, emphasizing the need for proactive, adaptive, and collaborative defense strategies to effectively mitigate the growing threat of DDoS attacks in an increasingly interconnected world.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Adaptive,proactive

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Creative Commons Attribution 4.0 and The Open Definition