BEHeDaS: A Blockchain Electronic Health Data System for Secure Medical Records Exchange

Journal of Computing Theories and Applications, Journal Year: 2024, Volume and Issue: 1(3), P. 231 - 242

Published: Jan. 6, 2024

Blockchain platforms propagate into every facet, including managing medical services with professional and patient-centered applications. With its sensitive nature, record privacy has become imminent for patient diagnosis treatments. The nature of records continued to necessitate their availability, reachability, accessibility, security, mobility, confidentiality. Challenges these include authorized transfer on referral, security across platforms, content diversity, platform interoperability, etc. These, are today – demystified blockchain-based apps, which proffers platform/application achieve data features associated the records. We use a permissioned-blockchain healthcare management. Our choice permission mode hyper-fabric ledger that uses world-state peer-to-peer chain is smart contracts do not require complex algorithm yield controlled transparency users. Its actors patients, practitioners, health-related officers as users create, retrieve, store aid interoperability. population 500, system yields transaction (query https) response time 0.56 seconds 0.42 seconds, respectively. To cater scalability yielded 0.78 063 respectively, 2500

Language: Английский

---

Strategic Feature Selection for Enhanced Scorch Prediction in Flexible Polyurethane Form Manufacturing

Journal of Computing Theories and Applications, Journal Year: 2024, Volume and Issue: 1(3), P. 346 - 357

Published: Feb. 29, 2024

The occurrence of scorch during the production flexible polyurethane is a significant issue that negatively impacts foam products' resilience and generally jeopardizes their integrity. likelihood product failure can be decreased by optimizing variables based on machine learning algorithms used to predict scorch. Investigating technology required because prevention best approach dealing with this problem. Hence, were trained using thermodynamic profile foam, which made up recorded variables. A variety heuristics assessed for how well they performed, namely XGBoost, Decision trees, Random Forest, K-nearest neighbors, Naive Bayes, Support Vector Machines, Logistic Regression. XGboost ensemble was found perform best. It outperformed others an accuracy 98.3% (i.e., 0.983), followed logistic regression, decision tree, random forest, naïve yielding training 88.1%, 66.7%, 84.2%, 87.5%, 67.5% respectively. XGBoost finally used, 2-distinct cases non(occurrence) Ensemble demonstrates it quite capable effective way

Language: Английский

---

A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction

Heliyon, Journal Year: 2024, Volume and Issue: 10(3), P. e25369 - e25369

Published: Feb. 1, 2024

In recent years, scientific data on cancer has expanded, providing potential for a better understanding of malignancies and improved tailored care. Advances in Artificial Intelligence (AI) processing power algorithmic development position Machine Learning (ML) Deep (DL) as crucial players predicting Leukemia, blood cancer, using integrated multi-omics technology. However, realizing these goals demands novel approaches to harness this deluge. This study introduces Leukemia diagnosis approach, analyzing accuracy ML DL algorithms. techniques, including Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), Gradient Boosting (GB), methods such Recurrent Neural Networks (RNN) Feedforward (FNN) are compared. GB achieved 97 % ML, while RNN outperformed by achieving 98 DL. approach filters unclassified effectively, demonstrating the significance leukemia prediction. The testing validation was based 17 different features patient age, sex, mutation type, treatment methods, chromosomes, others. Our compares techniques chooses best technique that gives optimum results. emphasizes implications high-throughput technology healthcare, offering

Language: Английский

---

Handling Transactional Data Features via Associative Rule Mining for Mobile Online Shopping Platforms

International Journal of Advanced Computer Science and Applications, Journal Year: 2024, Volume and Issue: 15(3)

Published: Jan. 1, 2024

Transactional data processing is often a reflection of consumer's buying behavior. The relational records if properly mined, helps business managers and owners to improve their sales volume. Transaction datasets are rippled with the inherent challenges in manipulation, storage handling due infinite length, evolution product features, concept, oftentimes, complete drift away from feat. previous studies' inability resolve many these as abovementioned, alongside assumptions that transactional presumed be stationary when using association rules – have been found also hinder performance. As it deprives decision support system needed flexibility robust adaptiveness manage dynamics concept characterizes transaction data. Our study proposes an associative rule mining model four consumer theories RapidMiner Hadoop Tableau analytic tools handle such large dataset was retrieved Roban Store Asaba consists 556,000 records. 6-layered framework yields its best result 0.1 value for both confidence level(s) at 94% accuracy, 87% sensitivity, 32% specificity, 20-second convergence time.

Language: Английский

---

RICE DISEASE RECOGNITION USING TRANSFER LEARNING XCEPTION CONVOLUTIONAL NEURAL NETWORK

Jurnal Teknik Informatika (Jutif), Journal Year: 2023, Volume and Issue: 4(6), P. 1535 - 1540

Published: Dec. 26, 2023

As one of the major rice producers, Indonesia faces significant challenges related to plant diseases such as blast, brown spot, tugro, leaf smut, and blight. These threaten food security result in economic losses, underscoring importance early detection management diseases. Convolutional Neural Network (CNN) has proven effective detecting plants. Specifically, transfer learning with CNN, particularly Xception model, advantage efficiently extracting automatic features performing well even limited datasets. This study aims develop model for disease recognition based on images. Through fine-tuning process, achieved accuracies, precisions, recalls, F1-scores 0.89, 0.90, respectively, a dataset total 320 Additionally, outperformed VGG16, MobileNetV2, EfficientNetV2.

Language: Английский

---

Counterfeit Drugs Detection in the Nigeria Pharma-Chain via Enhanced Blockchain-based Mobile Authentication Service

Advances in Multidisciplinary & Scientific Research Journal Publication, Journal Year: 2024, Volume and Issue: 12(2), P. 25 - 44

Published: Jan. 1, 2024

Drugs has since become a major source of livelihood for Nigerians. It also accounts over 85% the total food consumed within her borders. The sector maintained improved productivity and profitability via concerted effort to address critical issues such as an unorganized regulatory system, lack safety data, no standards in agricultural produce, non-adaptation precision farming, non-harmony inventory trace supports. This study proposes blockchain-based tracer-support system continued ensure quality, consumer safety, trading assets. uses radio-frequency identification sensors register drugs manufacture cum administration process provide databank drug records shipment its distribution centers. To ascertain, if is genuine or fake, user scans QRcode mobile application API, which then generates feedback. Results achieves following: (a) presents framework roadmap adoption by National Agency Food Drug Administration Control (NAFDAC) pharmaceutical blockchain, (b) show ensemble scalable up-to 7500users yield performance 1138-transactions per seconds with response time 88secs page retrieval 128secs queries respectively, (c) yields slightly longer increased number users world-state stored permissionless blockchain hyper-fabric ledger. Thus, can directly query retrieve data without it traversing whole This, turn, improves efficiency effectiveness traceability system. Keywords: Nigerian Pharma-Chain, Fake/Counterfeit drugs, Healthcare, CORDA, hyper-ledger fabric, NAFDAC Ejeh, P.O., Okpor, M.D., Yoro, R.E., Ifioko, A.M., Onyemenem, I.S., Odiakaose, C,C., Ojugo, A.A., Ako, Emordi, F.U., Geteloma, V.O., Counterfeit Detection Nigeria Pharma-Chain Enhanced Blockchain-based Mobile Authentication Service. Journal Advances Mathematical & Computational Science. 2024, Vol. 12, No. 1. Pp 25-44. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N2P3

Language: Английский

---

Comprehensive Analysis and Classification of Skin Diseases based on Image Texture Features using K-Nearest Neighbors Algorithm

Journal of Computing Theories and Applications, Journal Year: 2023, Volume and Issue: 1(1), P. 31 - 40

Published: Sept. 20, 2023

Skin is the largest organ in humans, it functions as outermost protector of organs inside. Therefore, skin often attacked by various diseases, especially cancer. cancer divided into two, namely benign and malignant. Malignant has potential to spread increase risk death. detection traditionally involves time-consuming laboratory tests determine malignancy or benignity. there a demand for computer-assisted diagnosis through image analysis expedite disease identification classification. This study proposes use K-nearest neighbor (KNN) classifier Gray Level Co-occurrence Matrix (GLCM) classify these two types Apart from that, average filter also used preprocessing. The was carried out comprehensively carrying 480 experiments on ISIC dataset. Dataset variations were using random sampling techniques test smaller datasets, where 3297, 1649, 825, 210 images. Several KNN parameters, number neighbors (k)=1 distance (d)=1 3 tested at angles 0, 45, 90, 135. Maximum accuracy results 79.24%, 79.39%, 83.63%, 100% respectively 210. These findings show that method more effective working besides significant contribution increasing accuracy.

Language: Английский

---

Enhancing Lung Cancer Classification Effectiveness Through Hyperparameter-Tuned Support Vector Machine

Journal of Computing Theories and Applications, Journal Year: 2024, Volume and Issue: 1(4), P. 396 - 406

Published: March 25, 2024

This research aims to improve the effectiveness of lung cancer classification performance using Support Vector Machines (SVM) with hyperparameter tuning. Using Radial Basis Function (RBF) kernels in SVM helps deal non-linear problems. At same time, tuning is done through Random Grid Search find best combination parameters. Where parameter settings are C = 10, Gamma Probability True. Test results show that tuned improves accuracy, precision, specificity, and F1 score significantly. However, there was a slight decrease recall, namely 0.02. Even though recall one most important measuring tools disease classification, especially imbalanced datasets, specificity also plays vital role avoiding misidentifying negative cases. Without tuning, so poor considering both becomes very important. Overall, obtained by proposed method 0.99 for 1.00 0.98 f1-score, specificity. confirms potential SVMs addressing complex data challenges offers insights medical diagnostic applications.

Language: Английский

---

Breaking Boundaries in Diagnosis: Non-Invasive Anemia Detection Empowered by AI

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 9292 - 9307

Published: Jan. 1, 2024

This article evolved because several instances of anemia are still discovered too late, especially in communities with limited medical resources and access to laboratory tests. Invasive diagnostic technologies expensive expenses additional impediments early diagnosis. To detect anemia, an effective, accurate, non-invasive method is required. In this study, the conjunctival image eye analyzed as a detecting anemia. Various model approaches were tested endeavor categorize anemic healthy patients accurately possible. The Support Vector Machine (SVM) algorithm-integrated MobileNetV2 was determined be most effective plan. With combination, accuracy 93%, sensitivity 91%, specificity 94%. These findings show that can successfully identify while identifying patients. offers means on, making it promising for use clinical settings. SVM+MobileNetV2 technique relies on images conjunctiva has potential improve healthcare by people who may have had earlier. stands out solid option efficient precise diagnosis when accuracy, sensitivity, balanced.

Language: Английский

---

Attention-guided deep neural network with a multichannel architecture for lung nodule classification

Heliyon, Journal Year: 2023, Volume and Issue: 10(1), P. e23508 - e23508

Published: Dec. 10, 2023

Detecting and accurately identifying malignant lung nodules in chest CT scans a timely manner is crucial for effective cancer treatment. This study introduces deep learning model featuring multi-channel attention mechanism, specifically designed the precise diagnosis of nodules. To start, we standardized voxel size images generated three RGB varying scales each nodule, viewed from different angles. Subsequently, applied submodels to extract class-specific characteristics these images. Finally, nodule features were consolidated model's final layer make ultimate predictions. Through utilization an could dynamically pinpoint exact location without need prior segmentation. proposed approach enhances accuracy efficiency classification. We evaluated tested our using dataset 1018 sourced Lung Image Database Consortium Resource Initiative (LIDC-IDRI). The experimental results demonstrate that achieved classification 90.11 %, with area under receiver operator curve (AUC) score 95.66 %. Impressively, method this high level performance while utilizing only 29.09 % time needed by mainstream model.

Language: Английский

---