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Dementia diagnosis in young adults: a machine learning and optimization approach

Neural Computing and Applications 2024年第34期36卷 21451-21464页

作者： Talaat, Fatma M. Ibraheem, Mai Ramadan Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt Information Technology Department Faculty of Computers and Information Kafrelsheiksh University Kafrelsheikh Egypt

Individuals who are younger and have dementia often start experiencing its symptoms before they turn 65, with cases even documented in people as young as their thirties. Researchers strive for accurate dementia diagnosis to slow or halt its progression. This paper presents a novel Enhanced Dementia Detection and Classification Model (EDCM) comprised of four modules: data acquisition, preprocessing, hyperparameter optimization, and feature extraction/classification. Notably, the model uses texture information from segmented brain images for improved feature extraction, leading to significant gains in both binary and multi-class classification. This is achieved by selecting optimal features via a Gray Wolf Optimization (GWO)-driven enhancement model. Results demonstrate substantial accuracy improvements after optimization. For instance, using an Extra Tree Classifier for "normal" cases, the model achieves 85% accuracy before optimization. However, with GWO-optimized features and hyperparameters, the accuracy jumps to 97%. © The Author(s) 2024.

关键词： Adversarial machine learning

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Prediction of Heart Disease Using Machine Learning Algorithms 2

Prediction of Heart Disease Using Machine Learning Algorithm...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Burle, Rushikesh Gaurkhede, Sanskruti Dewangan, Leelkanth Pacharaney, Utkarsha Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Computer Science and Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798350360523

In India, heart disease claims the lives of about one person every day. The human body's most vital organ is the heart. The heart has two primary purposes: first, it gathers blood from body tissues and pumps it to the lungs;second, it gathers blood from the lungs and pumps it to all body *** disease has claimed the lives of many people. Predicting that disease at the appropriate moment is crucial, for this reason. Making use of machine learning Additionally, illnesses are easily identified and anticipated. It also covers the use of many algorithms on the Heart Disease dataset. The dataset used in this research was obtained from Kaggle. Analyzing fundamental machine learning algorithms for the prediction of heart disease, such as Random Forest, K-Nearest Neighbours, Decision Tree Classifier, and Support Vector Machine, is the primary goal of this study. Our Acquired Model for The Decision Tree Classifier has a score of 79.0%. And the Random Forest Classifier has an 84.0% score. With a linear kernel, the Support Vector Classifier scores 83.0%. Next, K Neighbours Classifier~ outperformed all previously employed methods, achieving the maximum score of {87%} with ~8 nearest result. RF (Random Forest), K-Nearest Neighbours Algorithm, and Decision Tree, together with Python programming and Visual Studio, which excels at both data and code. © 2024 IEEE.

关键词： Heart

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Enhanced Crop Yield Forecasting Using Deep Reinforcement Learning and Multi-source Remote Sensing data

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 426-442页

作者： Chavan, Yogita Rahulsing Swamikan, Brinthakumari Gupta, Megha Varun Bobade, Sunil Malhan, Anu Department of Computer Engineering New Horizon Institute of Technology and Management Thane India Department of Artificial Intelligence and Data Science New Horizon Institute of Technology and Management Thane India

Accurate crop yield predictions are essential for agricultural planning and food security. Traditional methods relying on historical data are enhanced by modern techniques using satellite imagery, weather data, and soil conditions. This study presents an advanced crop yield forecasting model integrating deep reinforcement learning and multi-source remote sensing data. The Hybrid Reinforcement-Supervised Learning (HRSL) model combines reinforcement learning and supervised learning for enhanced classification and decision-making. Machine learning and artificial intelligence techniques analyze historical yield data and current season variables, improving prediction accuracy. Preprocessing steps like imputation, normalization, and atmospheric correction ensure robust datasets. Feature extraction with cascaded artificial neural networks (CANN) automates the learning of complex data relationships. Implemented using Python, this methodology achieves an accuracy of 97.8%. The proposed model demonstrates superior performance in yield prediction, evaluated through rigorous accuracy, precision, and training efficiency metrics. Integrating multi-source data with advanced machine learning models offers significant benefits for precision agriculture and food security. Future work will expand these techniques to broader applications and continuously improve predictive models with evolving data and computational capabilities. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Crop yield forecasting Deep reinforcement learning Food security Machine learning Multi-source data integration Precision agriculture Remote sensing

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Classification of Breast Cancer Using Computational Machine Learning Algorithms 8th

Classification of Breast Cancer Using Computational Machine ...

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8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Gagana, P. Aladakatti, Darshan Kodipalli, Ashwini Rao, Trupthi Kamal, Shoaib Electronics and Communication Engineering Global Academy of Technology Bangalore India Computer Science and Engineering Global Academy of Technology Bangalore India Artificial Intelligence and Data Science Engineering Global Academy of Technology Bangalore India

ISBN: (纸本)9789819976324

Cancer is a disease that brings fear in everyone’s heart. In the top 5 cancers listed by Stephanie Watson and reviewed by Medically Reviewed by Jennifer Robinson, says that breast cancer stands in the top third position among other dreadful cancers like Prostate Cancer, Pancreatic Cancer, Colorectal Cancer, and Lung Cancer. Numerous kinds of research are made on this terrifying disease yet millions and millions of women have lost their precious life due to breast cancer. A huge number of patients have been victimized by this curse on women. In severe cases, doctors fail to give hope and treatment as the patient’s conditions reach beyond medical help. We all have heard the quote "Prevention is better than cure", well in our case earlier detection is better than late realization. As earlier as modern-day treatments are based on the concept of molecular heterogeneity, which is why they can be influenced by various features. Early breast cancer, usually confined to the breast, is considered a curable disease. This stage is commonly known as benign, which in our dataset is represented as however when the symptoms are not detected and treated at the early stage the treatment becomes more complicated and cannot to treated using the present therapeutic options and might have an associated risk of spreading to other parts of the body. This condition is called malignant and this is represented as M in the dataset. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diseases

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A Secure Approach to Avoid data Repetition in Cloud Storage Systems 2

A Secure Approach to Avoid Data Repetition in Cloud Storage ...

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2nd International Conference on artificial intelligence and Machine Learning Applications, AIMLA 2024

作者： Swedhaa, M.A. Sathya, V. Vanitha, M. Jothilakshmi, T. Kongunadu College of Engineering and Technology Artificial Intelligence and Data Science Trichy India Mohamed Sathak A.J. College of Engineering Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350349221

Cloud Storage will be current data research and data management field in terms of security and elimination of repeated data-sets. In simple terms, this current research introduces a strong system called "Cloud-SecureDedupe(C-SD)". to make sure data is safe and cloud storage works well. The system helps with a common problem called data repetition, where the same information is stored more than once. "Cloud-SecureDedupe (SDP)"uses a safe method to solve this issue, making sure your data is reliable in cloud storage. It adds extra protection by using advanced techniques to keep important information secure. Additionally, it smartly identifies and removes any repeated data, not only making your data more secure but also saving space in the storage, so it's used more efficiently. The system has been carefully tested and proven to work well, ensuring that your experience with cloud storage is both safe and smooth. © 2024 IEEE.

关键词： Cloud storage

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Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

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Journal of Ambient intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

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A Type-1 Fuzzy Logic Framework for Early Risk Assessment of Gestational Diabetes Mellitus Using Clinical Risk Factors 2

A Type-1 Fuzzy Logic Framework for Early Risk Assessment of ...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Suman, Saurabh Pacharaney, Utkarsha Gote, Pradnyawant M. Faculty of Engineering and - Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Gestational Diabetes Mellitus (GDM) is a condition that can put at risk both mother and fetus but early and proper risk assessment need to be carried out for effective management. This paper discloses a Mamdani-kind Fuzzy Inference System to evaluate GDM risk dependent on clinical parameters like age, parity, glucose levels, body mass index (BMI), and insulin levels. The system can simulate all the numerous risk factors in a more gentle way by the utilization of the triangular membership function that converts accurate clinical data into a fuzzy format. A clear rule base, got from the knowledge maps in the clinical field, is put together from the input data to determining the GDM risk levels. This is neither then defuzzied via the centroid method to generate a determinable score. Results show that this system can be used along with the experts, thus yielding the same results as them, meanwhile, it is capable of providing an intelligible output. It lends further credence to the role of fuzzy logic in uncertainties in medical data and their potential by decision support systems in clinical context. While reasonably beneficial results from the system are reported, the static nature of the membership functions implies, for the future, adaptability in methodologies for relative accuracy. This study still emphasizes that fuzzy logic finds application in settings of clinical delineation, which are distinctly more complex for their intended usefulness for conditions such as GDM here. © 2025 IEEE.

关键词： Risk assessment

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A Review on the Role of Blockchain technology for Decentralized Identity Management: A Future Without Passwords 4

A Review on the Role of Blockchain Technology for Decentrali...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Raut, Rohini Gourshettiwar, Palash Thakre, Gayatri Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Advanced identity mechanisms are essential for secure client-identifiable proof in the unambiguously computerized world. Because the board frameworks rely on aggregated databases, fraud, data breaches, and unauthorized access are significant security risks. By providing a decentralized, immutable structure, blockchain technology advances security and protection. Through Self-Sovereign Identity, blockchain gives people authority over their own data. While also improving confirmation and check mechanisms. The potential of blockchain-based Identity, this article examines the board frameworks, focusing on five essential components: authentication, privacy, trust, integrity, and simplicity. It examines how interoperability, flexibility, and collaboration with outdated frameworks prevent decentralized IDMS being more widely used. The study also covers customer satisfaction, legal concerns, and potential future developments, such as Web 3.0, security enhancements, and integration with decentralized money. By addressing these problems and looking into new possibilities, blockchain-based IDMS may be able to reform advanced identity across the entire board. © 2025 IEEE.

关键词： Decentralized finance

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An IoT Based Electromyography Signal Transmission from sEMG Electrodes to Client's Server with ICT Infrastructure 10

An IoT Based Electromyography Signal Transmission from sEMG ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Pavithra, P.M. Balakrishnan, M. Anandaraj, A. Ramya, M. Sudharsan, R. Raja Sri Shanmugha College of Engineering and Technology Department of Artificial intelligence and Data Science Salem India Dr. Mahalingam College of Engineering and Technology Department of Artificial intelligence and Data Science Pollachi India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Sri Shanmugha College of Engineering and Technology Department of Information Technology Salem India Sri Shanmugha College of Engineering and Technology Department of Biomedical Engineering Salem India

ISBN: (纸本)9798350384369

Bio-electric signal like Electromyography (EMG) can measure signal data from nerves and muscles separately in the particular region of affected nerve or muscles from a Human Being. For incorporating these measurements, can use a non-invasive technique. The Surface electrode can be used for measuring these problems in non-invasive fashion. Here, the idea of Internet of Things (IoT) can bring included advantage to applications with bio-electric signals to communicate to the doctors and patients such as human-PC cooperation by incorporating different advances for example, sensors, remote correspondence and information science. In this research, we worked on a remote bio-electric signals to another remote observing handling framework. A model with the contextual investigation of nerve and muscle signal acknowledgment utilizing 4-channel sEMG signals is executed. A Gaussian filter is prepared from one individual's sEMG signals with 4-nerve regions of human body such as ulnar, radial, median and radial. The introduced IoT application framework is actualized based on an 11-terminal bio-electric signal measuring device, with Arduino based Controller, Gateway, ESP8266 (Wifi) module, Server being the major mediums of remote transmission of signal data. The signal transmits at 35KB/s. In the framework, the ongoing sEMG signal from surface electrode is filtered from noise and other distortions using is given to the controller for performing the operation. © 2024 IEEE.

关键词： Gateways (computer networks)

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Model Based Quadriceps Activation Strategy in Squat Training with Exhaustion Method Under Different Loads 8

Model Based Quadriceps Activation Strategy in Squat Training...

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8th International Symposium on Computer science and Intelligent Control, ISCSIC 2024

作者： Xie, Cong Guo, Xin Qie, Botao Hebei University of Technology Control Engineering Technology Innovation Center of Hebei Province Hebei University of Technology School of Artificial Intelligence and Data Science Tianjin China

ISBN: (纸本)9798350380286

The aim of this study is to explore the muscle activation strategy of quadriceps muscle training in untrained sedentary people under different resistance training to exhaustion to provide control target for body state-based intelligence exercise equipment. Ten daily sedentary men with no training experience performed three sets of heavy load squats (single leg bench squat) in exhaustion mode (4RM) and one set of lower resistance squats (double leg squat with 10 KG garment) in exhaustion mode (18-26RM) to explore which and when the muscle fibers will be recruited as much as more. EMG signals from the dominant side of the vastus medialis), rectus femoris, and vastus lateralis in both squats were recorded, and the maximum RMS of 500 MS length in each movement was normalized to the maximum voluntary isometric contraction. Reliability analysis of the normalized EMG signal for each muscle between the three groups of heavy-load squats was as follows: Alpha (vastus medialis)= 0.885, Alpha (rectus femoris)=0.903, Alpha (vastus lateralis) = 0.925. Paired samples t-test of the three muscles under different resistance:p (vastus medialis) © 2024 IEEE.

关键词： Exercise equipment

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