ISSN: 3008-0940
Submit Article
Submission Instructions
Open Access Information
Ethics and Malpractice Statement
Most Recent Issue
Editor(s)-in-Chief: Oscar Castillo and Sachi Nandan Mohanty

EAI Endorsed Transactions on Intelligent Systems 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.

INDEXING: Journal recently launched (Pending)

  • Editors-in-Chief
  • Oscar Castillo, Tijuana Institute of Technology, Tijuana, Mexico
  • Sachi Nandan Mohanty, VIT-AP University, Andhra Pradesh, India
  • Area Editors
  • Xiaochun Cheng, Swansea University, Swansea, UK
  • Sven Groppe, University of Luebeck, Germany
  • Lal Hussain, University of Azad Jammu and Kashmir, Pakistan
  • Rathore Rajkumar Singh, Cardiff Metropolitan University, UK
  • Mohammed Altaf Ahmed, Prince Sattam Bin Abdulaziz University, Saudi Arabia
  • Izzat Al-Darraji, University of Baghdad, Iraq
  • Miloš Stojmenović, Singhidunum University, Serbia
  • Md Kafiul Islam, Independent University, Bangladesh
  • Patrick Siarry, University Paris-Est Créteil Val de Marne, France
  • Abdulsattar Abdullah Hamad, Samarra University, Iraq
  • Gourab Sen Gupta, Massey University, New Zealand
  • Jiaxiang Zhang, Swansea University, Swansea, UK
  • Antonio Zuorro, Sapienza University, Rome, Italy
  • Georgios Tsaramirsis, Higher Colleges of Technology Women Campus, Abu Dhabi, UAE
  • Sanjay Misra, Covenant University, Nigeria
  • Edlira Kalemi Vakaj, Birmingham City University, UK
  • Sanju Tiwari, Universidad Autonoma de Tamaulipas, Mexico
  • Ming Yang, Kennesaw State University, GA, USA

  • Aspects-based representative significance of Machine Learning algorithms & natural language processing applications in nanotechnology.

    Appears in:
    ismla 24(1):

    Authors:
    Pascal Muam Mah

    Published:
    4th Dec 2024

    Abstract:
    Introduction: The rapid changes in computational power of machine learning algorithms and natural language processing applications have led to multi-scale and many core designs in nanotechnology. Machine learning algorithms and natural language processing applications are easing the burden engineers have to go through to understand nanoparticles. Problem: There is still a challenge to predict and control particles of nanomaterials at nanoscale. Aspect-based climatic conditions are negatively impacting the world with huge modification on nanoparticles, nanomaterials and nanostructures. Objective: Study examines aspects of machine learning algorithms and natural language processing applications that can be used to predict and control particles, and structure of nanomaterials at nanoscale. Method and materials. The study examines significance of machine learning algorithms & applications in nanotechnology, examines aspects of machine learning algorithms & natural language processing applications applied in nanotechnology, and discusses current-future trends of nanotechnology based on learning algorithms & natural language processing applications. Results and conclusions. The findings result in the conclusion that machine learning & natural language processing application in nanotechnology is implementing an advanced microscopic revolution with the potential to metamorphose the world's industrialization and scale human existence. Machine learning algorithms have the potential to predict and classify nanomaterials and natural language processing has the potential to retrieve relevant data hidden within the classified nanomaterials which results has a huge significance in the pharmaceutical industry

  • Exploring The Efficiency of Metaheuristics in Optimal Hyperparameter Tuning for Ensemble Models on Varied Data Modalities

    Appears in:
    ismla 24(1):

    Authors:
    Vivek BC

    Published:
    6th Aug 2024

    Abstract:
    Effective disease detection systems play an important role in healthcare by supporting diagnosis and treatment. This study provides a comparison of hyperparameter tuning methods for disease detection systems using four health datasets; kidney disease, diabetes detection, heart disease and breast cancer detection. The main objective of this research is to prepare datasets by normalizing the input and testing machine learning models such as Naive Bayes Support Vector Machine (SVM), Logistic Regression and k Nearest Neighbor (kNN). to identify effective models for each data set. After implementing the models, we apply three hyperparameter tuning techniques: Grid search, random search, and particle ensemble optimization (PSO). These methods are used to tune the model parameters. Improve overall performance metrics. The evaluation focuses on accuracy measurements to compare model performance before and after hyperparameter tuning. The results of this study illustrate how different tuning techniques can improve the performance of disease detection systems across a range of healthcare datasets. By conducting testing and analysis, we determine the appropriate tuning method for each data set, yielding valuable insights, to develop an accurate and effective disease detection system .These discoveries serve to advance the field of healthcare analytics and machine learning  to deliver outcomes for patients and healthcare services.

  • Detecting Alzheimer’s Patients using Features in Differential Waveforms of Pupil Light Reflex for Chromatic Stimuli

    Appears in:
    ismla 24(1):

    Authors:
    Wioletta Nowak, Tomasz Krecicki, Minoru Nakayama

    Published:
    18th Jul 2024

    Abstract:
    A procedure to detect irregular signal responses to pupil light reflex (PLR) was developed to detect Alzheimer’s Disease (AD) using a functional data analysis (FDA) technique and classification with an Elastic Net. In considering the differences in features of PLRs between AD and normal control (NC) participants, signals of summations and differentials between experimental conditions were analysed. The coefficient vectors for B-spline basis functions were introduced, and the number of basis was controlled for an optimised model. Model trained data was created using a data extension technique in order to enhance the number of participant observations. In the results, the required number of basis functions for differential signals is larger than the number for the their summation signals, and the features of differential signals contribute to classification performance.

  • Detection, Localization of Cardiomegaly and TB Disease of CXR Images using Deep Learning

    Appears in:
    ismla 24(1):

    Authors:
    Anwesh Reddy Paduri, Dinesh R, Ganesh Pradeep P V, Narayana Darapaneni

    Published:
    8th Jul 2024

    Abstract:
    INTRODUCTION: Tuberculosis (TB) continues to pose a significant worldwide public health concern, as it stands as the primary contributor to mortality stemming from infectious illnesses. Cardiomegaly, characterized by an enlarged heart, poses medical concern as well. OBJECTIVES: Timely identification of Cardiomegaly is vital for effective management. Chest X-ray diagnosis is an easily available method with less radiation exposure to detect several lung infections and heart enlargement. Utilizing computer-aided diagnostic systems can aid in the early detection of lung conditions and the enlargement of the heart. METHODS: We worked on different state-of-the-art CNN architectures such as VGG, DenseNet and EfficientNet with customization over dataset generated from combination of multiple publicly available datasets, which consists of 12939 annotated images across three different categories, one being normal and other two being TB and cardiomegaly diseases.. RESULTS: EfficientNetB5 with optimization has shown excellent results amongst others in classifying Tuberculosis and Cardiomegaly with a remarkable accuracy of 97%. CONCLUSION: The proposed model is ready for clinical diagnosis and triaging of X-ray images. Our solution also offers efficient ways to show the presence of the above diseases using Grad-CAM technique.

  • Diagnosis of Glioma, Menigioma and Pituitary brain tumor using MRI images recognition by Deep learning in Python

    Appears in:
    ismla 24(1):

    Authors:
    Seyed Masoud Ghoreishi Mokri, Vera Grigoryeva, Newsha Valadbeygi

    Published:
    15th Apr 2024

    Abstract:
    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.

  • Influence of Promotion and Pricing on Purchase Incidence, Demand, and Sales Using Machine Learning

    Appears in:
    ismla 24(1):

    Authors:
    Rahul D Shanbhogue, Anwesh Reddy Paduri, Narayana Darapaneni

    Published:
    10th Apr 2024

    Abstract:
    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.

  • Enhancing Document Clustering with Hybrid Recurrent Neural Networks and Autoencoders: A Robust Approach for Effective Semantic Organization of Large Textual Datasets

    Appears in:
    ismla 24(1):

    Authors:
    Suresh Babu Alladi, Ratnam Dodda

    Published:
    18th Mar 2024

    Abstract:
    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.

  • Enhancing Document Clustering with Hybrid Recurrent Neural Networks and Autoencoders: A Robust Approach for Effective Semantic Organization of Large Textual Datasets

    Appears in:
    ismla 24(1):

    Authors:
    Suresh Babu Alladi, Ratnam Dodda

    Downloads:
    4

    Abstract:
    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.

  • Diagnosis of Glioma, Menigioma and Pituitary brain tumor using MRI images recognition by Deep learning in Python

    Appears in:
    ismla 24(1):

    Authors:
    Vera Grigoryeva, Newsha Valadbeygi, Seyed Masoud Ghoreishi Mokri

    Downloads:
    4

    Abstract:
    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.

  • Aspects-based representative significance of Machine Learning algorithms & natural language processing applications in nanotechnology.

    Appears in:
    ismla 24(1):

    Authors:
    Pascal Muam Mah

    Downloads:
    3

    Abstract:
    Introduction: The rapid changes in computational power of machine learning algorithms and natural language processing applications have led to multi-scale and many core designs in nanotechnology. Machine learning algorithms and natural language processing applications are easing the burden engineers have to go through to understand nanoparticles. Problem: There is still a challenge to predict and control particles of nanomaterials at nanoscale. Aspect-based climatic conditions are negatively impacting the world with huge modification on nanoparticles, nanomaterials and nanostructures. Objective: Study examines aspects of machine learning algorithms and natural language processing applications that can be used to predict and control particles, and structure of nanomaterials at nanoscale. Method and materials. The study examines significance of machine learning algorithms & applications in nanotechnology, examines aspects of machine learning algorithms & natural language processing applications applied in nanotechnology, and discusses current-future trends of nanotechnology based on learning algorithms & natural language processing applications. Results and conclusions. The findings result in the conclusion that machine learning & natural language processing application in nanotechnology is implementing an advanced microscopic revolution with the potential to metamorphose the world's industrialization and scale human existence. Machine learning algorithms have the potential to predict and classify nanomaterials and natural language processing has the potential to retrieve relevant data hidden within the classified nanomaterials which results has a huge significance in the pharmaceutical industry

  • Detecting Alzheimer’s Patients using Features in Differential Waveforms of Pupil Light Reflex for Chromatic Stimuli

    Appears in:
    ismla 24(1):

    Authors:
    Wioletta Nowak, Tomasz Krecicki, Minoru Nakayama

    Downloads:
    3

    Abstract:
    A procedure to detect irregular signal responses to pupil light reflex (PLR) was developed to detect Alzheimer’s Disease (AD) using a functional data analysis (FDA) technique and classification with an Elastic Net. In considering the differences in features of PLRs between AD and normal control (NC) participants, signals of summations and differentials between experimental conditions were analysed. The coefficient vectors for B-spline basis functions were introduced, and the number of basis was controlled for an optimised model. Model trained data was created using a data extension technique in order to enhance the number of participant observations. In the results, the required number of basis functions for differential signals is larger than the number for the their summation signals, and the features of differential signals contribute to classification performance.

  • Exploring The Efficiency of Metaheuristics in Optimal Hyperparameter Tuning for Ensemble Models on Varied Data Modalities

    Appears in:
    ismla 24(1):

    Authors:
    Vivek BC

    Downloads:
    3

    Abstract:
    Effective disease detection systems play an important role in healthcare by supporting diagnosis and treatment. This study provides a comparison of hyperparameter tuning methods for disease detection systems using four health datasets; kidney disease, diabetes detection, heart disease and breast cancer detection. The main objective of this research is to prepare datasets by normalizing the input and testing machine learning models such as Naive Bayes Support Vector Machine (SVM), Logistic Regression and k Nearest Neighbor (kNN). to identify effective models for each data set. After implementing the models, we apply three hyperparameter tuning techniques: Grid search, random search, and particle ensemble optimization (PSO). These methods are used to tune the model parameters. Improve overall performance metrics. The evaluation focuses on accuracy measurements to compare model performance before and after hyperparameter tuning. The results of this study illustrate how different tuning techniques can improve the performance of disease detection systems across a range of healthcare datasets. By conducting testing and analysis, we determine the appropriate tuning method for each data set, yielding valuable insights, to develop an accurate and effective disease detection system .These discoveries serve to advance the field of healthcare analytics and machine learning  to deliver outcomes for patients and healthcare services.

  • Influence of Promotion and Pricing on Purchase Incidence, Demand, and Sales Using Machine Learning

    Appears in:
    ismla 24(1):

    Authors:
    Rahul D Shanbhogue, Narayana Darapaneni, Anwesh Reddy Paduri

    Downloads:
    3

    Abstract:
    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.

  • Detection, Localization of Cardiomegaly and TB Disease of CXR Images using Deep Learning

    Appears in:
    ismla 24(1):

    Authors:
    Anwesh Reddy Paduri, Dinesh R, Ganesh Pradeep P V, Narayana Darapaneni

    Downloads:
    3

    Abstract:
    INTRODUCTION: Tuberculosis (TB) continues to pose a significant worldwide public health concern, as it stands as the primary contributor to mortality stemming from infectious illnesses. Cardiomegaly, characterized by an enlarged heart, poses medical concern as well. OBJECTIVES: Timely identification of Cardiomegaly is vital for effective management. Chest X-ray diagnosis is an easily available method with less radiation exposure to detect several lung infections and heart enlargement. Utilizing computer-aided diagnostic systems can aid in the early detection of lung conditions and the enlargement of the heart. METHODS: We worked on different state-of-the-art CNN architectures such as VGG, DenseNet and EfficientNet with customization over dataset generated from combination of multiple publicly available datasets, which consists of 12939 annotated images across three different categories, one being normal and other two being TB and cardiomegaly diseases.. RESULTS: EfficientNetB5 with optimization has shown excellent results amongst others in classifying Tuberculosis and Cardiomegaly with a remarkable accuracy of 97%. CONCLUSION: The proposed model is ready for clinical diagnosis and triaging of X-ray images. Our solution also offers efficient ways to show the presence of the above diseases using Grad-CAM technique.
Publisher
EAI
ISSN
3008-0940
Number of Volumes
1
Last Published
2024-03-18
EAI Logo

About EAI

Community

Publish with EAI