EAI Endorsed Transactions on Intelligent Systems and Machine Learning Applications
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: GoogleScholar, CrossRef, Dimensions, Semantic Scholar, Lens eISSN: 3008-0940
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
The Relationship Between the Exchange Rate and the General Index of the Istanbul Stock Exchange (BIST100) by Machine Learning Application
The purpose of this research is to understand the type of relationship between the exchange rate for the period (2020-2022) and the Istanbul Stock Exchange index by using machine learning application. This research stems from the assumption that exchange rate fluctuations directly affect the overall market indicator. The interval model of self-regression distribution is used in general market indicators, and the GRANGER test was used to demonstrate the cause-effect relationship among economic variables. Turning to the market index, the study concluded that the exchange rate explains the changes in the general market index (best 100) (97%) and (3%) for variables other than the standard model, and there is a long-term joint integration relationship between the research variables, the result is that the exchange rate explains the changes in the general market index (best 100) (97%) and (3%) for variables other than the standard model.
Copyright (c) 2026 Adil R N, Abdul Razzaq H H https://creativecommons.org/licenses/by-nc-sa/4.0
A Unified Hand-Landmark-Based Deep Learning Framework for Static and Dynamic Vietnamese Sign Language Recognition
Sign language recognition plays a crucial role in supporting communication between deaf communities and hearing individuals. In particular, Vietnamese Sign Language (VSL) recognition remains a challenging task due to the complexity of hand gestures and limited available datasets. However, most existing approaches address static gestures and dynamic sign phrases as separate recognition problems, often employing different feature representations and independent processing pipelines. This fragmentation increases system complexity and limits scalability for real-time sign language applications. This study proposes a unified hand-landmark-based deep learning framework for recognizing both static and dynamic VSL gestures within a single integrated system. The system begins by detecting hand landmarks through the MediaPipe hand tracking pipeline. Based on temporal motion analysis of landmark sequences, a gesture routing mechanism automatically determines whether the input corresponds to a static gesture or a dynamic sign phrase. Static gestures are classified using a convolutional neural network (CNN), while dynamic gestures are processed using a long short-term memory (LSTM) network to capture temporal dependencies. Experiments were conducted on a VSL dataset consisting of 23 static hand signs and 4 dynamic sign phrases collected from multiple participants with variations in hand shape and gesture execution style. The system performance is evaluated using accuracy, precision, recall, and F1-score. Experimental results demonstrate that the proposed framework achieves an average recognition accuracy of 92% for static gestures and 88.4% for dynamic sign phrases, outperforming traditional machine learning baselines. The proposed system provides a practical and efficient solution for VSL recognition and has potential applications in real-time assistive communication systems.
Copyright (c) 2026 Linh Duong Thanh, Don Pham Kim https://creativecommons.org/licenses/by-nc-sa/4.0
Acoustic and Tonal Modeling of the tpuri Language through a Multi-Modular Hybrid Approach
Automatic speech recognition (ASR) for tonal low-resource languages remains challenging due to the scarcity of labelled data and the need to model complex prosodic systems. This paper presents a hybrid multimodular ASR architecture for tpuri, a Mboum-Day Niger-Congo language spoken in Cameroon and Chad that exhibits contrastive lexical tone, vowel length and nasalisation. The system combines a self-supervised Wav2Vec 2.0 acoustic encoder with a tonal processing module based on YIN pitch estimation and STFTderived spectral features, and an adaptive fusion mechanism that integrates acoustic and tonal representations before decoding. We pretrain the acoustic encoder on 45 hours of read and spontaneous speech and finetune it on 19h35 of scripted speech. On the scripted test set, our best configuration reaches a word error rate (WER) of 10.4%, a phone error rate (PER) of 8.7% and a tone error rate (TER) of 6.1%. Ablation experiments show that removing the tonal module (+1.5 WER, +2.3 TER) or self-supervised pretraining (+3.4 WER) substantially degrades performance, while adaptive fusion and tone-aware data augmentation yield smaller but consistent gains. A fine-grained error analysis across tonal, grammatical, syllabic and morphological dimensions indicates that the architecture is particularly effective at modelling lexical tone and clause-level syntax, but still struggles with complex syllable structures and rich morphology. Overall, the results demonstrate that competitive ASR is attainable for under-resourced tonal languages such as tpuri by tightly coupling self-supervised acoustic modelling with explicit tonal representations, and provide a reusable blueprint for extending ASR to other Niger-Congo languages.
Copyright (c) 2025 Jules Paulin Bayang Souloukna, Patrick Nounamou Dabou, Paul Dayang, Kolyang https://creativecommons.org/licenses/by-nc-sa/4.0
Explainable Machine Learning for Multiclass Classification of Concurrent Child Undernutrition in Ethiopia
Child undernutrition remains a major public-health challenge in Ethiopia, often occurring in concurrent forms that are clinically more severe than single deficits. We develop a supervised machine-learning framework to classify children into concurrent nutritional states using World Health Organization anthropometric indicators. Using baseline data from the Young Lives Cohort Study, we model seven observed nutritional categories under substantial class imbalance. Models were evaluated using imbalance-aware metrics, including Macro-F1, Balanced Accuracy, and ROC-AUC. Random Forest achieved the strongest overall performance and provided improved discrimination for concurrent undernutrition cate-gories. Explainability analysis using SHAP highlighted the importance of house-hold and caregiver-related factors. These findings demonstrate the potential of explainable machine-learning approaches for modeling concurrent undernutrition and provide a foundation for future longitudinal and multi-label extensions.
Copyright (c) 2025 Getnet Begashaw, Temesgen Zewotir, Haile Fenta, Mulu Asmamaw, Abebe Legass https://creativecommons.org/licenses/by-nc-sa/4.0
EyeFusionNet: A Hybrid CNN-Transformer Network for Automated Diagnosis of Eye Diseases using Colour Fundus Imaging
INTRODUCTION: The timely and accurate diagnosis of eye conditions, such as myopia, cataracts, and diabetic retinopathy, is critical for preventing vision loss. Conventional diagnostic techniques depend on the manual examination of eye fundus images, a process that is often labour-intensive and susceptible to human errors. OBJECTIVES: The proposed work aims to develop an automated deep learning-based solution for classifying eye diseases using the newly released Eye Disease Image Dataset with 5,335 images and 10 classes, addressing limitations in traditional manual diagnostic techniques. METHODS: The proposed EyeFusionNet employs a dual-track architecture combining DenseNet169 for detailed local feature extraction and Transformer-iN-Transformer for capturing global context and long-range dependencies. The outputs are fused and refined using efficient channel attention to focus on important regions within the fundus images, with explainable artificial intelligence used to provide visualisations to doctors to validate the diagnosis. RESULTS: The EyeFusionNet achieved an accuracy of 89% on the publicly available dataset of eye fundus images, outperforming state-of-the-art CNNs and transformer models, showcasing the potential of deep learning for eye fundus image diagnosis. CONCLUSION: The proposed EyeFusionNet introduces a dual-track architecture for eye disease classification, providing a strong foundation for advancing automated diagnostic tools in clinical settings with the help of deep learning and explainable artificial intelligence to empower clinicians.
Copyright (c) 2025 Akshaj Singh, Sagar Singh Chauhan, Armaano Ajay, Rishi Manda, Prasanna Bharathi https://creativecommons.org/licenses/by-nc-sa/4.0
Quality Aware Content-Based Image Retrieval Using QIM and Deep Learning for Big Visual Data
Modern technology has made storing, sharing, and organizing huge amounts of data simple through the Internet of Things. Search engines and query-based retrieval databases made access to relevant data easy through ranking and indexing based on stored content. In this paper, a secure CBIR scheme based on watermarking is proposed. Firstly, the image owner embeds the watermark in the image using quantization index modulation (QIM) in the luminance (Y) color space. The watermarked images are then uploaded to the cloud server, which extracts image feature vectors. In this article, features derived from a pre-trained network model from a deep-learning convolutional neural network trained for large image classification have been used for the retrieval of similar images. The image similarity is calculated using Euclidean distance, and the precision (P) is used as the performance measure of the model that achieved nearly 100%. Extensive experiments are carried out, and assessment results reveal the outperforming result of the proposed technique compared to other related schemes. The scheme can be used in many applications that need CBIR, such as digital libraries, historical research, fingerprint identification, and crime prevention.
Copyright (c) 2025 Amit Phadikar https://creativecommons.org/licenses/by-nc-sa/4.0
Hybrid Template Matching and Faster R-CNN for Robust Multimodal Object Detection
This paper introduces a hybrid object detection framework that integrates template matching with the Faster R-CNN deep learning algorithm to improve robustness in challenging conditions such as occlusion, clutter, and low resolution. The novelty of this work lies in systematically combining a traditional template-matching branch with a two-stage detector, enabling the system to capture predefined structural cues alongside learned deep features. The proposed score-based fusion mechanism further refines detections by weighting outputs from both branches. Experimental results on COCO and LASIESTA datasets show that the hybrid model achieves an F1 score of 88.6% and a mAP@0.75 of 69.4%, surpassing both template-only and Faster R-CNN-only baselines. These findings highlight the effectiveness of the hybrid strategy in enhancing detection accuracy and robustness while maintaining practical computational efficiency.
Copyright (c) 2025 Hewa Majeed Zangana https://creativecommons.org/licenses/by-nc-sa/4.0
Al-Driven Qualitative Research in Smart Cities: Enhancing Emotional Resilience in Youth and Children
INTRODUCTION: Industry 5.0 has brought advanced AI-driven technologies into qualitative research and data analysis, particularly in systems that are very important to the purpose. This research examines the use of AI algorithms to evaluate emotional resilience in kids and children in smart cities. The study underscores Al's role in qualitative research to substantiate the efficacy of these algorithms in assessing emotional resilience and advocating for interventions that improve emotional well-being. The main goal of this research is to see how accurate and reliable AI algorithms are when they measure emotional resilience. The goal of the project is to leverage these technologies to make treatments that make kids and teens in smart cities feel better emotionally, which will help them grow up in a caring environment. METHODOLOGY: A quantitative, descriptive, and exploratory methodology is used, using data gathered from children to examine emotional reactions via deep neural network models. These models are designed to find levels of resilience with amazing accuracy, sensitivity, and specificity, with the goal of getting accuracy rates above eighty percent. RESULTS: The results indicate that AI-driven technology may provide comprehensive qualitative insights into the emotional resilience of adolescents and children. The research underscores the capacity of these technologies to provide personalized treatments and assistance, hence improving emotional well-being in smart city contexts. The findings indicate that AI might enhance emotional resilience, facilitate early detection of emotional problems, and enable prompt assistance. The suggested model was able to find emotional resilience with 94% accuracy, 92% sensitivity, 88% specificity, and 95% AUC. These results demonstrate the efficacy of AI-driven approaches in the early detection of emotional problems among adolescents and teenagers inside smart city environments. The research shows that AI technologies are very important for figuring out how to help kids and teens become more emotionally strong. It backs the employment of these technologies in the public health and education systems of smart cities to help kids develop emotionally. This plan makes it simpler to get in early and helps create a strong, supportive community.
artificial intelligence, emotional resilience, youth, children, smart cities.
Copyright (c) 2025 Jesus Morgan Asch, Edwin Gerardo Acuña Acuña https://creativecommons.org/licenses/by-nc-sa/4.0
A Machine Learning-based approach to predicting tuberculosis in the Democratic Republic of Congo
INTRODUCTION: Tuberculosis remains a public health problem in Democratic Republic of Congo (DRC), despite advances in Machine Learning for the prediction of this disease. However, existing models are often adapted to Asian contexts and do not take into account the specific epidemiological and social characteristics of the DRC. Given this shortcoming, our study explores a Machine Learning approach specifically designed to improve the prediction of tuberculosis in the Congolese population. OBJECTIVES: Our problem is based on the following question: "What approach, based on Machine Learning and specific to the population of DRC, is likely to improve the prediction of tuberculosis?" To answer this, we adopted an exploratory paradigm with a sequential mixed design (qualitative and quantitative). The study was conducted on a sample of 1505 patients and six healthcare professionals in the health zones of Lubumbashi and Nzanza. METHODS: The data was collected using questionnaires and semi-structured interviews, then analysed using bivariate and multivariate approaches. RESULTS: The results show that incorporating Congolese specificities into Machine Learning models significantly improves the prediction of tuberculosis. Of the models tested, Random Forest and Decision Tree performed best in terms of precision, recall, F1-score and AUC, while Voting Classifier, Stacking and Adaboost showed a good compromise between precision and robustness. CONCLUSION: This study highlights the need to develop predictive models adapted to the local context in order to improve tuberculosis control in DRC. We propose an optimised model incorporating characteristics specific to the Congolese population, with a possible large-scale application to improve detection and prevention of the disease.
Copyright (c) 2025 Pierre Tshibanda wa Tshibanda, Bopatriciat Boluma Mangata, Marina Mbombo Kabongo , Guy-Patient Mbiya Mpoyi https://creativecommons.org/licenses/by-nc-sa/4.0
Development of Churn Prediction System for a Marketing Company Using Machine Learning Technique
INTRODUCTION: In today's competitive marketing landscape, customer churn prediction is vital for marketing organizations to identify patterns, factors, and indicators contributing to customer attrition. This paper focuses on developing a customer churn prediction system using machine learning algorithms. OBJECTIVES: This paper employed an e-commerce dataset, obtained from the Kaggle repository, and was preprocessed. Important features were selected from the preproccessed dataset before models’ development. The parameters of AdaBoost, Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) were optimized to improve their performance. METHODS: Techniques such as label encoder, mean imputation, and synthetic minority over-sampling technique (SMOTE) were applied during data preprocessing stage. Ensemble learning algorithms, namely AdaBoost, GB, and XGB were used to develop the model while random search was employed for parameter optimization. Accuracy, precision, recall, and F1-score metrics were used to evaluate the models’ performance. RESULTS: The results of the models with 15 important selected features before parameter tuning yielded the following scores: AdaBoost attained 87% accuracy, 77% precision, 81% recall, and an 79% F1-score. Gradient boosting outperformed AdaBoost with 89% accuracy, 80% precision, 82% recall, and an 81% F1-score. XGB outperformed the two algorithms (AdaBoost and GB), achieving 97% accuracy, 96% precision, 94% recall, and 95% F1-score. Notably, the Random Search significantly improves Gradient boosting's performance, increasing accuracy from 89% to 97%, precision from 80% to 97%, recall from 82% to 93%, and F1-score from 81% to 95%., making it comparable to XGB results. SHAP analysis reveals that the "Complain" feature was a consistent and key positive driver of predicted churn across all models, implying that customers who register complaints are significantly more likely to churn than those who do not. Additionally, the ‘tenure’ feature which has a strong negative impact on the prediction across the three models implies that longer tenure logically means less likely to churn, which makes the model's behaviour intuitive and trustworthy. CONCLUSION: The results demonstrate the effectiveness of the system in identifying at-risk customers, enabling businesses to proactively retain customers and reduce churn rates. The findings of this paper showcase the critical importance of effective complaint management and rapid response strategies in customer retention efforts, and suggest that fostering long-term relationships and increasing customer loyalty can be highly effective in reducing churn.
Copyright (c) 2025 Ismaila Yusuf, Sulaiman Olaniyi Abdulsalam, Temitope Oyewumi Jimoh, Ismail Akuji https://creativecommons.org/licenses/by-nc-sa/4.0
Timing for securing the biometric template transformation based on supervised learning using Double Random Phase Encoding Method
Background: Among optical encryption techniques, Double Random Phase Encoding (DRPE) is one of the most widely used. Individual identities and the process of recognition remain essential to ensuring proper data access security. Aim: The study aims to optimize an approach that ensures the significant performance effectiveness of the cancelable biometric methods for different templates and the associated time taken to transform biometric data. Problem: This study is majorly concerned about the performance effectiveness of cancelable biometric methods that measure the likelihood that an authorized effort may be mistakenly rejected as unauthorized. Also, when compromised, several non-renewability safety challenges arise, and insufficient matching performance templates are required to build a security protection method. Method and material. The study uses supervised learning for the Double Random Phase Encoding Method (DRPE), a 4F optical encryption system, and 20 randomly chosen photos from the ORL database of faces. Results. The result based on the supervised learning for the Double Random Phase Encoding Method revealed false positive rates for both the fingerprint and face templates. Conclusion. The study concluded that the performance effectiveness of the cancelable biometric in this study has a false positive rate likelihood that an authorized effort may not be mistakenly rejected as an unauthorized one.
Copyright (c) 2025 Mahmoud Nasr, Pascal Muam Mah https://creativecommons.org/licenses/by-nc-sa/4.0
Optimising performance indicators in the telecommunications sector
INTRODUCTION: This study analyses and predicts performance in the public telecommunication sector using neural networks on key performance indicators in the telecommunication sector. OBJECTIVES: Although there are several key performance indicators in the telecommunications sector, we have selected a few and assessed their correlation with the variable to be predicted. In this study, we used supervised learning based on a multi-layer neural network. METHODS: The algorithm used in this study is the retro propagation algorithm because of its simplicity and accuracy of estimation. RESULTS: The results show that the selected indicators, including accessibility, maintainability, satisfaction, network operating cost and availability, explain more than 80% of the performance in the telecommunications sector, and the area of the ROC curve is equal to 0.97, which means that the classifier is almost perfect. This is also justified by the sensitivity and specificity, which are close to 1 when observing the ROC curve and the confusion matrix. The classification error found from the confusion matrix is equal to 1%, which means that our model has very high accuracy. CONCLUSION: The other indicators presented were not selected in the model because of their low correlation with the variable of interest and the difficulty of collecting the data.
performance in the telecommunication sector
neural network on performance indicators
Fiston Chrisnovic Balanganayi Kabutakapua
Copyright (c) 2024 Fiston Chrisnovic Balanganayi Kabutakapua, Pierre Kafunda Katalay, Simon Ntumba Badibanga, Eugène Mbuyi Mukendi https://creativecommons.org/licenses/by-nc-sa/4.0
Deep Learning Approaches for Stock Price Prediction A Comparative Study on Nifty 50 Dataset
Stock price prediction is essential for investors and traders in financial markets. Deep learning methods have emerged as promising tools for capturing intricate patterns in stock market data. In this paper, we explore a comprehensive comparative study of various deep learning architectures for stock price prediction using the Nifty 50 dataset. The models evaluated include Linear regression, LSTM, GRU, CNN, RNN, Temporal Convolutional Network (TCN), as well as combination models such as LSTM+GRU, CNN+RNN, CNN+TCN, and LSTM+TCN. Our study aims to evaluate how well they perform and suitability of these methodologies in capturing the dynamics of stock price movements. Utilizing historical Nifty 50 data spanning multiple years, we evaluate the models’ predictive capabilities using standard evaluation metrics such as MSE, R2 Score, RMSE, MAE, and MAPE. Results from our experiments unveil distinct strengths and weaknesses among the different deep learning architectures. While linear regression provides a baseline for comparison, deep learning models like LSTM, GRU, CNN, RNN, and TCN exhibit superior performance in capturing the nonlinear and time-varying nature of stock market data. Additionally, hybrid architectures demonstrate promising results by leveraging the complementary strengths of individual models. This comparative study offers meaningful perspectives on the effectiveness of various deep learning approaches for stock price prediction, which can benefit researchers, practitioners, and stakeholders in the financial domain. By understanding the performance characteristics of these models, stakeholders can make informed decisions in their investment strategies.
Copyright (c) 2024 Sushma P Kallimath, Narayana Darapaneni, Anwesh Reddy Paduri https://creativecommons.org/licenses/by-nc-sa/4.0
Modelling attention levels using microsaccade rates in response to vibrations in the peripheral ﬁeld of vision
Viewer’s eye movements and behavioural responses were analysed in order to determine the relationship between selective perception and visual attention during a dual detection task in the central and peripheral ﬁelds of vision, in order to design better functioning information displays. Changes in visual attention levels were evaluated using temporal microsaccade rates. Response accuracy of stimulus detection and the microsaccade rate were analysed using a hierarchical Bayesian modelling technique. In the results, the dominance of the response in the peripheral ﬁeld of vision is conﬁrmed to be deviations in the estimated parameters. Also, chronological changes in levels of attention and the contribution of these changes to behavioural responses were examined. The relationship between behavioural responses, microsaccade rate, and the directional dominance of certain viewing areas in the peripheral ﬁeld of vision were discussed, in order to evaluate the level of visual attention of viewers.
Copyright (c) 2024 Minoru Nakayama, Takahiro Ueno https://creativecommons.org/licenses/by-nc-sa/4.0
Aspects-based representative significance of Machine Learning algorithms & natural language processing applications in nanotechnology.
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
Copyright (c) 2024 Pascal Muam Mah https://creativecommons.org/licenses/by-nc-sa/4.0
Enhancing Document Clustering with Hybrid Recurrent Neural Networks and Autoencoders: A Robust Approach for Effective Semantic Organization of Large Textual Datasets
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.
Copyright (c) 2024 Ratnam Dodda, Suresh Babu Alladi https://creativecommons.org/licenses/by-nc-sa/4.0
Influence of Promotion and Pricing on Purchase Incidence, Demand, and Sales Using Machine Learning
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.
Copyright (c) 2024 Rahul D Shanbhogue, Anwesh Reddy Paduri, Narayana Darapaneni https://creativecommons.org/licenses/by-nc-sa/4.0
Diagnosis of Glioma, Menigioma and Pituitary brain tumor using MRI images recognition by Deep learning in Python
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.
Copyright (c) 2024 Seyed Masoud Ghoreishi Mokri , Newsha Valadbeygi, Vera Grigoryeva https://creativecommons.org/licenses/by-nc-sa/4.0
Detecting Alzheimer’s Patients using Features in Diﬀerential Waveforms of Pupil Light Reﬂex for Chromatic Stimuli
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.
Copyright (c) 2024 Minoru Nakayama, Wioletta Nowak, Tomasz krecicki https://creativecommons.org/licenses/by-nc-sa/4.0
Exploring The Efficiency of Metaheuristics in Optimal Hyperparameter Tuning for Ensemble Models on Varied Data Modalities
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.
Copyright (c) 2024 Vivek BC https://creativecommons.org/licenses/by-nc-sa/4.0
Detection, Localization of Cardiomegaly and TB Disease of CXR Images using Deep Learning
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.
Copyright (c) 2024 Ganesh Pradeep P V, Dinesh R, Anwesh Reddy Paduri, Narayana Darapaneni https://creativecommons.org/licenses/by-nc-sa/4.0
Enhancing Object Recognition Through a Novel Adaptive Recognition Technique (ART) Framework
Object recognition is a critical capability in various computer vision applications, but traditional approaches often struggle with complex, real-world scenarios. This paper introduces a novel Adaptive Recognition Technique (ART) framework to enhance object recognition performance. The proposed ART framework leverages adaptive learning mechanisms to more accurately identify objects, even in the presence of variations in size, orientation, and environmental conditions. Through a series of experiments on benchmark datasets, the ART framework demonstrated significant improvements in recognition accuracy compared to existing methods. Key innovations include the integration of unsupervised feature learning, dynamic model adaptation, and ensemble-based decision making. The results suggest that the ART framework offers a promising approach to advancing the state-of-the-art in object recognition, with potential applications in areas such as autonomous vehicles, surveillance, and image analysis. Further research is underway to expand the capabilities of the ART framework.
Copyright (c) 2025 Hewa Majeed Zangana, Firas Mahmood Mustafa, Marwan Omar https://creativecommons.org/licenses/by-nc-sa/4.0