Transactions on Federated Engineering and Systems https://milestoneresearch.in/JOURNALS/index.php/TFES <p><strong><em>Transactions on Federated Engineering &amp; Systems </em></strong><strong>[ISSN:</strong> Applied] is a prestigious and peer-reviewed multidisciplinary journal that serves as a prominent platform for cutting-edge research and advancements in the field of Federated Engineering and Systems. The journal aims to foster the exchange of innovative ideas, methodologies, and technologies within the realm of federated systems, providing a comprehensive understanding of this rapidly evolving domain. The journal caters to a diverse audience of researchers, academics, professionals, and industry experts in the fields of engineering, computer science, information technology, data science, artificial intelligence, and related domains. </p> Milestone Research Publications en-US Transactions on Federated Engineering and Systems From Leader to Laggard: An Analysis of Blackberry's UI/UX Missteps and the Decline of a Tech Giant https://milestoneresearch.in/JOURNALS/index.php/TFES/article/view/115 <div><span lang="EN-US">The failure of BlackBerry in the smartphone market is a cautionary tale about the importance of UI/UX design in the success of mobile devices. This research paper examines the impact of UI/UX design on BlackBerry's decline, including its failure to adapt to changes in the mobile landscape, poor design strategies, and a lack of innovation. By analyzing industry trends and BlackBerry's history, this paper argues that the company's focus on physical keyboards, its failure to recognize the importance of touch screen devices, and its slow pace of introducing new features and applications resulted in a subpar user experience that led to declining sales and a loss of market share. Despite attempts to rebrand and reposition the company, BlackBerry was unable to recover its once dominant position in the smartphone market. This paper highlights the importance of UI/UX design in creating a positive user experience that resonates with consumers and emphasizes the need for mobile companies to continuously innovate and adapt to changing user needs and behaviors.</span></div> Bharath P Damodhar D B M Venkatesh Prasanna Kumar Shetty Syed Thouheed Ahmed Copyright (c) 2023 Bharath P, Damodhar D B, M Venkatesh, Prasanna Kumar Shetty, Syed Thouheed Ahmed https://creativecommons.org/licenses/by-nc-nd/4.0 2023-08-18 2023-08-18 1 1 1 12 10.5281/zenodo.8262610 Cloud-Based Essentail Home Services Aggrregator: Maintainance services made easy and affordable https://milestoneresearch.in/JOURNALS/index.php/TFES/article/view/116 <div><span lang="EN-US">Cloud-based Home services aggregator aims to provide the much needed and essential everyday services to the consumers in an easy and affordable manner. This proposed approach basically brings technology to the doorsteps of both the employee and the employer and helps them connect in a never before manner.&nbsp; The importance and impact of this paper is clearly visible in the present and pressing times of the Covid epidemic. This proposed approach demonstrates a workflow model that can function irrespective of the epidemic conditions and at the same time minimize the risk of virus infection by reducing people to people contact. Despite the reduction in contact the work quality is not affected. Tough times such as an epidemic call on for these services as essential and important much more than ever. Through this proposed approach we bring a common platform to those who provide the services and those who need it. The scope of the provided services ranges from basic home cleaning, car wash, laundry to much more. The proposed approach also aims to bring to platform the large - feature phone using - Indian workforce. The limited capabilities of the existing infrastructure hinder this section of workforce from connecting to better work opportunities. A potentially large number of footsteps on the side may cause it to render slowly. </span></div> Sowmya Sundari L K Anitha K Konjengbam Dollar S Copyright (c) 2023 Sowmya Sundari L K, Anitha K, Konjengbam Dollar S https://creativecommons.org/licenses/by-nc-nd/4.0 2023-08-18 2023-08-18 1 1 13 17 10.5281/zenodo.8262683 From Industry Leader to Near Extinction: Unpacking the Tragic Tale of Nokia's Downfall https://milestoneresearch.in/JOURNALS/index.php/TFES/article/view/117 <div><span lang="EN-US">In-depth assessments and analyses of Nokia Corporation's strategic technology management are provided in this study. We examined and studied the historical evolution of Nokia's core business, leadership strategies, business architecture, R&amp;D policy, innovation strategy, various&nbsp;product launches, and recognition and demonstration of smartphones using traditional narrative literature studies and secondary sources. We discovered a number of strategic gaps that earlier analytical investigations appeared to have failed to recognize and generalize. In order to add to the body of knowledge, we present a collection of the lessons discovered that chronologically explain why Nokia was unable to establish and maintain a competitive edge, particularly in the smartphone market. We concluded that Nokia's issues were not caused by a lack of innovation but rather by a lack of precise technology forecasting and a failure to recognize that the demands of the smartphone market went beyond simply showcasing a mobile phone that can make calls, send texts, and connect to the internet, but also the platform that runs all these functions simultaneously. We further debate how likely it is that the new Nokia handsets would compete and credibly succeed in a very well-established market since the brand name Nokia has lately returned to the market through a newly licensed company (HMD Global).</span></div> Supreetha S Gokul kumaran S Monisha Copyright (c) 2023 Supreetha S, Gokul kumaran S, Monisha https://creativecommons.org/licenses/by-nc-nd/4.0 2023-08-18 2023-08-18 1 1 18 26 10.5281/zenodo.8262716 Soft Tissue Tumour Diagnosis Using Machine Learning: A Comparison of Hybrid Algorithm https://milestoneresearch.in/JOURNALS/index.php/TFES/article/view/127 <div><span lang="EN-GB">The Soft tissue tumour detection based on machine learning involves using algorithms and models to identify and classify&nbsp;&nbsp; treatment tumours that arise in the Muscles, fat, nerves, and blood arteries are examples of soft tissues. In order to categorize soft tumours based on their histopathological features. We trained and tested both algorithms on a large dataset of histologically confirmed soft tissue tumours and achieved high accuracy, precision, and recall. Our results demonstrate the capacity of ML algorithms to enhance the precision and effectiveness of soft tissue tumour diagnosis and support clinical judgement. The performance of the hybrid algorithms in the classification of soft tissue tumours based on their histopathological features. The study found that both algorithms achieved high accuracy, precision, and recall rates, demonstrating the potential of ML algorithms to improve the accuracy and efficiency of soft tissue tumour diagnosis. The article provides valuable insights for pathologists and oncologists in the use of ML algorithms in soft tissue tumour diagnosis and clinical decision-making</span></div> K Amuthabala Laxmi Raja Copyright (c) 2023 K Amuthabala, Laxmi Raja https://creativecommons.org/licenses/by-nc-nd/4.0 2023-12-07 2023-12-07 1 1 27 36 10.5281/zenodo.10279622 Phishing Websites Classification Placed on URL Features and Extreme Machine Learning https://milestoneresearch.in/JOURNALS/index.php/TFES/article/view/128 <div><span lang="EN-GB">Phishing attacks have become an increasingly common threat to individuals and organizations alike. The traditional methods used to detect phishing attacks, such as blacklisting known phishing URLs or using heuristics to identify suspicious websites have proven to be limited in their effectiveness. Phishing attackers continuously evolve their tactics, making it difficult for traditional detection methods to keep up. To address this challenge, this study explores the use of machine learning classifiers to uncover illegitimate websites. Specifically, this research utilizes the Multilayer Perceptron and Bernoulli Naive Bayes (NB) classifiers. The feature selection process is performed using a decision tree classifier, which helps to identify the most relevant features for the classification task. To train and test the classifiers, the study collected a dataset of blacklisted and whitelisted websites. Accuracy, precision, recall, and the ROC curve were only few of the measures used to assess the classifier's effectiveness. The results demonstrate the effectiveness of the Multilayer Perceptron and Bernoulli NB classifiers in detecting phishing websites. The feed forward neural network classifier achieved an accuracy of over 82% on the dataset. These results showcase the potential of machine learning techniques in improving the discovering of phishing attacks and reducing further risks of phishing attacks.</span></div> Ranjitha R G Meenakshi Sundaram A Copyright (c) 2023 Ranjitha R G, Meenakshi Sundaram A https://creativecommons.org/licenses/by-nc-nd/4.0 2023-12-07 2023-12-07 1 1 37 43 10.5281/zenodo.10279674 Music Classification Using Convolutional Neural Systems https://milestoneresearch.in/JOURNALS/index.php/TFES/article/view/129 <div><span lang="EN-GB">In this work we used two CNN models, to determine the genre of a particular musical composition, Lenet-5 and CNN-64 are trained on a dataset of audio samples. We evaluated the models based on accuracy and loss. We found that Lenet-5 achieved higher accuracy and lower loss than CNN-64, indicating its effectiveness. The outcome highlights potential for CNNs in retrieval of music data also demonstrate the utility of Lenet-5 for achieving high accuracy and low loss in genre classification. Thus we see that CNNs can be used in music-related applications, such as music recommendation systems and music transcription. </span></div> Arpitha K M Nimrita Koul Copyright (c) 2023 Arpitha K M, Nimrita Koul https://creativecommons.org/licenses/by-nc-nd/4.0 2023-12-07 2023-12-07 1 1 44 50 10.5281/zenodo.10279702