EAI Endorsed Transactions on Intelligent Systems and Machine Learning Applications https://publications.eai.eu/index.php/ismla <p>EAI Endorsed Transactions on Intelligent<em> Systems</em> and Machine learning serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction. With its peer-reviewed format, the journal publishes original research and review articles written by today's experts in the field. Its coverage also includes papers on intelligent systems with machine learning applications in areas such as nanotechnology, renewable energy, medicine, engineering, Aeronautics and Astronautics, Mechatronics, industrial, manufacturing, bioengineering, agriculture, services, intelligence-based automation and appliances, medical application and robotic rehabilitations, space exploration, Medical Treatment and Health, Business and Finance, Internet of Things (IoT). Research addressing machine learning applications in other fields is also encouraged.</p> <p><strong>INDEXING</strong>: Journal recently launched (Pending)</p> European Alliance for Innovation (EAI) en-US EAI Endorsed Transactions on Intelligent Systems and Machine Learning Applications <p>This is an open access article distributed under the terms of the <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a>, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.</p> Enhancing Document Clustering with Hybrid Recurrent Neural Networks and Autoencoders: A Robust Approach for Effective Semantic Organization of Large Textual Datasets https://publications.eai.eu/index.php/ismla/article/view/4564 <p>This research presents an innovative document clustering method that uses recurrent neural networks (RNNs) and autoencoders. RNNs capture sequential dependencies while autoencoders improve feature representation. The hybrid model, tested on different datasets (20-Newsgroup, Reuters, BBC Sports), outperforms traditional clustering, revealing semantic relationships and robustness to noise. Preprocessing includes denoising techniques (stemming, lemmatization, tokenization, stopword removal) to ensure a refined data set. Evaluation metrics (adjusted randomness evaluation, normalized mutual information evaluation, completeness evaluation, homogeneity evaluation, V-measure, accuracy) validate the effectiveness of the model and provide a powerful solution for organizing and understanding large text datasets.</p> Ratnam Dodda Suresh Babu Alladi Copyright (c) 2024 Ratnam Dodda, Suresh Babu Alladi https://creativecommons.org/licenses/by-nc-sa/4.0 2024-03-18 2024-03-18 1 Influence of Promotion and Pricing on Purchase Incidence, Demand, and Sales Using Machine Learning https://publications.eai.eu/index.php/ismla/article/view/5082 <p class="ICST-abstracttext"><span lang="EN-GB">The consumer goods industry is a dynamic and fast-paced sector that faces significant challenges in meeting the consumer’s ever-evolving demands and preferences. Today’s retail businesses focus on optimizing their supply and retail execution to maintain a competitive edge in the market and remain profitable. The most impactful method is to offer promotional events that stimulate large-scale purchases and attract new customers. The patterns of normal sales days, promotion days, and non-promotion days are different and it is vital to capture the influence of promotions on demand and sales. Thus, it is vital to understand the effects of promotion and plan them. This paper aims to understand the influence of promotion and pricing strategies for FMCG retail businesses to maximize demand for each brand. Explore the use of Machine Learning (ML) and Deep Learning models such as Clustering and Neural Networks to identify and understand the various demand patterns to analyse the influence of promotion and pricing on demand, and enable businesses to respond more quickly to changes in the market by enabling them to make better-informed decisions that can mitigate risks associated with the impact of disruptions and to ensure the continuity of the business.</span></p> Rahul D Shanbhogue Anwesh Reddy Paduri Narayana Darapaneni Copyright (c) 2024 Rahul D Shanbhogue, Anwesh Reddy Paduri, Narayana Darapaneni https://creativecommons.org/licenses/by-nc-sa/4.0 2024-04-10 2024-04-10 1 Diagnosis of Glioma, Menigioma and Pituitary brain tumor using MRI images recognition by Deep learning in Python https://publications.eai.eu/index.php/ismla/article/view/5410 <p>Medical image processing is a very difficult and new field. One thing they do in this field is analyze pictures of people's brains to look for signs of tumors. They use a special computer program to help with this. This paper talks about a new way to use the program to find brain cancer early by looking at the texture of the tumor. This paper explains how we can find and understand brain tumors using special pictures called MRI scans. We use computer programs to help us do this. First, we find the tumor, then we separate it from the rest of the brain, and finally we measure how big it is. We can also figure out how serious the tumor is by looking at different kinds of tumors. To make it easier for people to use, we made a special program in a computer language called COLAB for python codes about using CNN network for deep learning. We tested this program on 8 patients and learned a lot about their tumors.</p> Seyed Masoud Ghoreishi Mokri Newsha Valadbeygi Vera Grigoryeva Copyright (c) 2024 Seyed Masoud Ghoreishi Mokri , Newsha Valadbeygi, Vera Grigoryeva https://creativecommons.org/licenses/by-nc-sa/4.0 2024-04-15 2024-04-15 1