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Data, Computation and Communication (ICDCC), International Conference on

作者： Akshat Rana Nivedita Sharma Department of Mathematics Chandigarh University Mohali Punjab India Department of Computer Science and Engineering Chandigarh University Mohali Punjab India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

This study, present a novel method with a unique combination of powerful deep learning techniques, specifically Long Short-Term Memory Networks (LSTM) networks, to detect and predict deep fakes. Deep fakes are a serious threat to media legitimacy because deep fakes produce very believable fake information. To exploit LSTMs' perceptive skills at analyzing temporal sequences, we exploit minute discrepancies in video frames beyond what traditional models can: Training and validation accuracy graph indicates 93% accuracy of the proposed model after training with diversified true and deepfake video datasets. This helps to show how well LSTM can tell true from false information, and made a reliable way to boost media authenticity. The paper provides evidence of how the results from our method can be used to improve the identification of deepfakes in order to facilitate additional efforts to defend digital material against the growing threat of media manipulation.

关键词： Training Deep learning Deepfakes Analytical models Accuracy prediction algorithms Feature extraction Reliability Long short term memory

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PM2.5 prediction Combined ANFIS with Meta-Heuristic Optimization algorithms: A Case Study in Wuhan

PM2.5 Prediction Combined ANFIS with Meta-Heuristic Optimiza...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yapei Qin Hong Fan Mohammed A.A. Al-Qaness Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan China College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Meta-heuristic algorithms and ANFIS have extraordinarily impressive performance for predicting time-series PM2.5 air quality data. This paper explores the possibility of mainstream meta-heuristic algorithms combined with the adaptive neuro-fuzzy inference system (ANFIS) applied to PM2.5 prediction in Wuhan. It is a major difficulty to choose appropriate methods to connect the strengths of both MHA and ANFIS for data prediction. Therefore, three sorts of meta-heuristic algorithms, including ant colony optimization, particle swarm optimization, and genetic algorithm, have been constructed to update the parameters of the neural network of ANFIS. In the case study in Wuhan, modified fuzzy inference systems possess the studying capacity of neural networks and integrate the human ability for reasoning and interpretation, thereby handling the uncertainty of data prediction better.

关键词： Fuzzy logic Uncertainty Metaheuristics Neural networks Geoscience and remote sensing prediction algorithms Inference algorithms

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Free-Space Optical Channel Turbulence prediction: A Machine Learning Approach

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IEEE COMMUNICATIONS LETTERS 2025年第5期29卷 1126-1130页

作者： Zobaer Islam, Md Abele, Ethan Ferdous Hossain, Fahim Ahmad, Arsalan Ekin, Sabit O'Hara, John F. Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA Texas A&M Univ Dept Engn Technol College Stn TX 77840 USA Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77840 USA

Channel turbulence is a formidable obstacle for free-space optical (FSO) communication. Anticipation of turbulence levels is highly important for mitigating disruptions but has not been demonstrated without dedicated, auxiliary hardware. We show that machine learning (ML) can be applied to raw FSO data streams to rapidly predict channel turbulence levels with no additional sensing hardware. FSO was conducted through a controlled channel in the lab under six distinct turbulence levels, and the efficacy of using ML to classify turbulence levels was examined. ML-based turbulence level classification was found to be >98% accurate with multiple ML training parameters. Classification effectiveness was found to depend on the timescale of changes between turbulence levels but converges when turbulence stabilizes over about a one minute timescale.

关键词： Accuracy Fans Adaptive optics Generators Training prediction algorithms Measurement by laser beam Boosting Optical imaging Optical distortion Free space optical communication channel turbulence prediction

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prediction of CD34+hematopoietic stem cells in healthy allogeneic stem cell donors on the Optia cell separator based on CE2: Which formula is more correlated with actual CD34+?

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TRANSFUSION 2024年第10期64卷 1959-1967页

作者： Ozkan, Sidika Gulkan Kimiaei, Ali Safaei, Seyedehtina Ercan, Tarik Sonmezoglu, Meral Demirel, Gulderen Yanikkaya Aktas, Sema Ozdemir, Sinem Ozalp, Helin Serda Ozkan, Hasan Atilla Bahcesehir Univ Fac Med Div Hematol Dept Internal Med Istanbul Turkiye Med Pk Goztepe Hosp Adult Hematol & Bone Marrow Transplantat Unit Istanbul Turkiye Yeditepe Univ Hosp Dept Infect Dis Istanbul Turkiye Yeditepe Univ Hosp Dept Immunol Istanbul Turkiye Yeditepe Univ Hosp Therapeut Apheresis Unit Istanbul Turkiye Turkish Stem Cell Coordinat Ctr Republ Turkey Minist Hlth Ankara Turkiye

Background: Peripheral blood-derived hematopoietic stem cells (HSCs) are widely used for various adult stem cell transplants. To obtain sufficient HSCs from healthy volunteer donors during the apheresis process and ensure that the donors are exposed to fewer apheresis-related side effects, calculation methods have been developed for the prediction of processed blood volume or CD34+ count. However, there is no consensus on a formula to predict the volume of blood to be processed or the number of stem cells to be obtained. Objective: This study aimed to estimate the predicted blood volume and CD34+ cell counts using collection efficiency (CE)-based formulas and evaluate their accuracy compared to the actual CD34+ cell counts. It also seeks to identify the factors that affect CE. Methods: Data from 397 healthy, unrelated stem cell donors were retrospectively analyzed. An algorithm using four different CE2 metrics (1st quartile, mean, 3rd quartile, and median) was developed to predict the volume of blood to be processed using the Spectra Optia continuous mononuclear cell collection procedure. Results: When employing the mean CE2 algorithm, the results revealed a strong correlation (r = .894, p < .001) between predicted and actual CD34+ values. The study also identified strong associations between pre-apheresis CD34+, pre-apheresis leukocyte count, the use of two doses of G-CSF, and low CE2. Conclusion: These findings suggest that the mean CE2 algorithm could be a potent, straightforward, and accurate tool for predicting CD34+ stem cell counts in healthy allogeneic stem cell donors and potentially optimizing stem cell collection procedures.

关键词： apheresis CD34+hematopoietic stem cells collection efficiency 2 prediction algorithms Spectra Optia cell separator stem cell transplantation

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Enhancing Breast Cancer prediction with XAI-Enabled Boosting algorithms

Enhancing Breast Cancer Prediction with XAI-Enabled Boosting...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Vinayak Gupta Richa Sharma School of Computer Science and Engineering Bennett University Greater Noida

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Breast cancer poses a significant health threat to the growing population of the world and as such calls for innovative methods to improve early detection and improve patient safety. The existing techniques in the field have made use of techniques like various image processing models like RetinaNet, You Only Look Once, Convolutional Neural Networks as well as tree-based models like the J48, decision trees and Naïve Bayes to predict Brest cancer but most these studies focus on the accuracy of the predictions without providing a basis for explanation and understandability of the results. Therefore, to provide transparency, this study utilizes the Local Interpretable Model-agnostic Explanations (LIME). This study investigated the performance of three boosting algorithms LightGBM, CatBoost and XGBoost in breast cancer prediction making use of the SEER dataset. The paper also makes use of SMOTE and the amalgamation of Tomek Links to handle class imbalance generally present with medical data to enhance the generalizability of the results. With hyperparameter tuning, the results show that LightGBM outperformed the other two in terms of accuracy as well as recall metrics. LIME explanations were then employed specific patient instances to understand the interplay of the various variables and how they contribute to the model predictions. These explanations highlighted the clinical relevance of variables such as T Stage and Grade of cancer, aiming to instill confidence among the clinicians. The research in all undermines the importance of integrating XAI methodologies in breast cancer detection, paving way for more clinically regulated and relevant models in the future.

关键词： Measurement YOLO Accuracy Medical services Predictive models prediction algorithms Boosting Breast cancer Safety Tuning

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Student Placement prediction and Skill Recommendation System using Machine Learning algorithms

Student Placement Prediction and Skill Recommendation System...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Rakesh Kadu Purshottam J. Assudani Tanmay Mukewar Jatin Kapgate Rajat Bijekar Information Technology Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

The Student Placement prediction and Skill Recommendation System is an innovative application designed to assist students in enhancing their employability and securing suitable job placements. In today's competitive job market, students often face challenges in understanding their strengths, identifying required skills, and effectively positioning themselves for job opportunities. This research work aims to address these challenges by leveraging machine learning algorithms to predict a student's likelihood of placement and recommend skills to improve their employability. The dataset used in this proposed system comprises various attributes such as academic performance (CGPA, marks), internship experiences, projects undertaken, certifications earned, aptitude test scores, soft skills ratings, and extracurricular activities. Leveraging this dataset, machine learning models, particularly the Random Forest algorithm, were employed to predict the probability of a student getting placed and the potential offers from different companies based on their academic, skill, and experiential profiles. Moreover, the system also offers personalized skill recommendations to students based on their current profile. For students who are not yet placed, the system generates skill recommendations using cosine similarity vectorization techniques, analyzing various attributes, including academic achievements, internship experiences, projects, certifications, aptitude scores, and soft skills. Recommendations are tailored to bridge skill gaps and enhance the chances of securing suitable job placements. The research work demonstrates the effectiveness of machine learning techniques in predicting placement outcomes and recommending personalized skill improvements, thus providing valuable insights for students to strategize and improve their employability prospects. The system serves as a useful tool for students seeking career guidance and advancement in the competitive job market.

关键词： Machine learning algorithms Engineering profession Navigation Predictive models prediction algorithms Data models Trajectory

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Investigating the Impact of Dataset Variants on Heart Disease prediction Using Supervised Learning algorithms

Investigating the Impact of Dataset Variants on Heart Diseas...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Uma Shanker Singh Ujjwal Richa Gupta Amit Gupta Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University R.S- Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

In today's modern world due to lifestyle or ancestry, uncertain death or diagnosis of heart diseases have increased significantly not only among senior citizens but also among youths. The early detection of heart disease can help to prevent the risk of one's life. In this paper, authors have compared several machine learning algorithms such as Logistic Regression (LR), Random forest (RF), K-Nearest Neighbour (KNN), Naive Bayes (NB), Support Vector Machine (SVM) and applied these machine learning algorithms on three variants of the dataset i.e. original, scaled and resampled to understand the impact of these variants on prediction system result such that an enhanced and optimal model can be considered or brought into use based on their performance metrics. The dataset consists of 4240 instances and 16 attributes that went through various stages of data preprocessing to handle the anomalies present in the dataset and further where utilised for final training and testing on machine learning models. The authors produced an enhanced performance with accuracy of 85% of Logistic Regression, Support Vector Machine and Random Forest models. Along with accuracy of 82% and 83 % of Naive Bayes and K-Nearest Neighbour models respectively on the original data. All these accuracies of machine learning algorithms were further compared with the accuracies of scaled and resampled dataset on similar algorithms to analyse and understand the impact of dataset variants. The outcome of the research paper encapsulates the concept of efficient working of Machine Learning algorithms/Models that will help in early detection and prevention of Heart/Cardiovascular diseases.

关键词： Heart Support vector machines Logistic regression Machine learning algorithms Accuracy Nearest neighbor methods prediction algorithms Bayes methods Random forests Diseases

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Heart Disease prediction Using Machine Learning algorithms

Heart Disease Prediction Using Machine Learning Algorithms

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International Conference on Smart Communications and Networking (SmartNets)

作者： Dina Jrab Derar Eleyan Amna Eleyan Tarek Bejaoui Department of Computer Science Faculty of Information Technology Palestine Technical University - Kadoorie Tulkarem State of Palestine Faculty of Telecomm. and Information Technology Nablus University for Technical and Vocational Education Nablus Palestine Department of Computing and Mathematics Manchester Metropolitan University Manchester Uk Computer Engineering Department University of Carthage Tunisia

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

Heart disease is a prevalent and complex condition that affects numerous individuals worldwide. Timely and accurate diagnosis of heart disease is of utmost importance in cardiology. In this research article, we propose an efficient and precise system for heart disease diagnosis, employing machine learning techniques. The system is designed based on various classification algorithms, including Support Vector Machine, Logistic Regression, Decision Tree, and Random Forest. Standard feature selection algorithms such as ANDV A, Chi-Squared, and Mutual Information Feature Selection (MIFS) are utilized to eliminate unrelated features. Furthermore, we introduce a novel fast experiment that contains a conditional mutual information feature selection algorithm, ANDVA feature selection algorithms, and a Chi-squared feature selection algorithm to address the feature selection challenge. These feature selection algorithms enhance classification model accuracy and reduce the compile time in the classification ML model. The cross-validation method evaluates the models and optimizes hyperparameters, ensuring reliable model assessment. Performance measuring metrics are utilized to assess the classifiers' performance. The classifiers are estimated based on the selected features determined by the feature selection algorithms. The experimental results show that the ANDV A F -test feature selection algorithm along with the Support Vector Machine classifier, is a viable approach for developing an advanced intelligent system that can identify heart disease. The proposed model can also be easily implemented in healthcare to facilitate heart disease identification.

关键词： Heart Support vector machines Accuracy Machine learning algorithms System performance Feature extraction prediction algorithms

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Analysis of Stunting prediction for Toddlers in Bekasi Regency Using the K-Nearest Neighbors and Random Forest algorithms

Analysis of Stunting Prediction for Toddlers in Bekasi Regen...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Kamelia Khoirunnisa Putu Harry Gunawan School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

Stunting, a condition where children are shorter than their age, is a serious nutritional issue in developing countries, including Indonesia. Research shows that Low Birth Weight (LBW) significantly affects children's growth. In Bekasi Regency, the stunting prevalence remains high at 17%, with a target reduction to 14%. Achieving this target requires prevention efforts focused on improving nutrition and regularly monitoring child growth. In such monitoring, innovative evaluation techniques using Machine Learning (ML) are needed to predict stunting potential. This study aims to develop a predictive model for early detection of stunting risks in Bekasi Regency, using machine learning techniques to analyze Low Birth Weight (LBW) and Low Birth Length (LBL) factors, which could potentially be integrated into the local health monitoring system for preventive intervention. RF excels in handling complex features and identifying important predictors, while KNN is effective at recognizing local patterns. The results show that RF achieved the best performance with 99.22% accuracy and an F1-score of 96.94%, compared to KNN with 96.19% accuracy and an F1-score of 87.16%, highlighting RF's greater stability and robustness over KNN in predicting stunting cases. This study is expected to provide an accurate predictive system that helps parents, health workers, and the government identify stunting potential early while also determining the appropriate ML algorithm for stunting case prediction in Indonesia. Future research is encouraged to test this model in other regions with different characteristics to ensure the generalizability and effectiveness of stunting prediction on a broader scale.

关键词： Radio frequency Accuracy Machine learning algorithms Prevention and mitigation Nearest neighbor methods Predictive models prediction algorithms Stability analysis Random forests Monitoring

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Flood prediction using Supervised Machine Learning algorithms

Flood Prediction using Supervised Machine Learning Algorithm...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： M. Vimala S. Ramasamy P.Ranjith Kumar N.Najmul Sahi A. Rajesh S. Siddharth Department of ECE P.S.R Engineering College Sivakasi

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

The most frequent type of calamity is flooding, which happens when water overflows and submerges normally dry terrain. Flood prediction models, which are typically based on historic data and specified thresholds, are intended to forecast when the water level will exceed a predetermined threshold. To minimize the complex numerical articulations of actual flood cycles, Machine Learning (ML) methods generate predictions about future events that are far more accurate than predictions made by humans. In this paper, various supervised machine learning algorithms are implemented. Among many ML techniques, classification is a widely used one. This paper uses various supervised learning algorithms, such as Logistic Regression, Random Forest, XGB classifier, ExtraTree classifier, LGBM classifier, and CatBoost classifier. Based on performance, supervised learning algorithm for flood prediction is analyzed and most appropriate models is predicted. This particular model can be effectively utilized by both the government and the general public to properly predict floods in advance.

关键词： Logistic regression Machine learning algorithms Supervised learning Training data Predictive models prediction algorithms Classification algorithms Numerical models Floods Random forests

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