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A Preliminary Study of MLSE/ACE-III Stages for Primary Progressive Aphasia Automatic Identification Using Speech Features 1

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Proceedings of the 18th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2023

作者： J. Valdés Cuervo, Amable Herrera, Elena A. de la Cal, Enrique Computer Science Department Faculty of Geology University of Oviedo Oviedo Spain Psychology Department Faculty of Psychology University of Oviedo Oviedo Spain

ISBN: (数字)9783031425363

ISBN: (纸本)9783031425356

Primary Progressive Aphasia (PPA) is a syndrome causing progressive deterioration of language and speech due to brain degeneration. Three variants exist: non-fluent variant (nfvPPA), semantic variant(svPPA) and logopenic variant (lvPPA). While fMRI is the most accepted diagnostic tool (and neurological exploration), it is expensive and takes even months to deliver results. Cheaper and faster tools are needed for earlier diagnosis and treatment initiation. Some studies have attempted automatic diagnosis using acoustic and linguistic features with ML and DL techniques. However, none have included Latin language patients or analyzed the effect of cognitive tests. This work proposes a methodology based on three main steps: i) a new assessment tool (PPATool) combining ACE-III and MLSE with three language tasks: verbal fluency, repetition and naming, and ii) an IDA process to obtain an ML model trained with our own two-class (PPA/Healthy) dataset, and iii) ranking the relevance of tasks in PPATool from models performance. The results obtained after deploying the IDA process on the dataset obtained from an early-stage clinical trial, show that the verbal fluency data outperforms the rest of the tasks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deterioration

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PDF Malware Detection Using Machine Learning 9

PDF Malware Detection Using Machine Learning

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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Tantikulvichit, Naragorn Kosolsombat, Somkiat Ratanavilisagul, Chiabwoot College of Interdisciplinary Studies Thammasat University Data Science and Innovation Pathum Thani Thailand King Mongkut's University of Technology Faculty of Applied Science Department of Computer and Information Science North Bangkok Thailand

ISBN: (纸本)9798350352214

In today's world, cyber-attacks are on the rise, and PDF files are commonly used as a means of attack. One common type of attack through PDF files is the covert embedding of dangerous malware and tricking users into clicking on malicious links. The victims may have their important data stolen and suffer various forms of damage. Detecting malware embedded in PDF files can help mitigate the harm to users. This project aims to study methods for detecting malware embedded in PDF files using machine learning techniques, with the best-performing model being Gradient Boosting, achieving an accuracy of 99% in 5-fold cross-validation and 97% using 10 features. Using Feature Selection Technique Voting from 3 Models consist of Logistic Regression, Random Forest, Lasso Regression, Each model will choose matching features to use for machine learning analysis. This project approach reduce feature for machine learning to detect PDFMalware. © 2024 IEEE.

关键词： Adversarial machine learning

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Integrated dominating and hit set-inspired unequal clusteringbased data aggregation in wireless sensor networks

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International Journal of Intelligent Computing and Cybernetics 2022年第4期15卷 642-655页

作者： G.Vennira Selvi V.Muthukumaran A.C.Kaladevi S.Satheesh Kumar B.Swapna Department of Computer Science and Engineering Global Institute of Engineering and TechnologyVelloreIndia Department of Mathematics School of Applied SciencesREVA UniversityBangaloreIndia Department of Computer Science and Engineering Sona College of TechnologySalemIndia Department of Computer Science School of Applied SciencesREVA UniversityBangaloreIndia Department of Electronics and Communication Engineering Dr MGR Educational and Research InstituteChennaiIndia

Purpose-In wireless sensor networks,improving the network lifetime is considered as the prime objective that needs to be significantly addressed during data *** the traditional data aggregation techniques,cluster-based dominating set algorithms are identified as more effective in aggregating data through cluster ***,the existing cluster-based dominating set algorithms suffer from a major drawback of energy deficiency when a large number of communicating nodes need to collaborate for transferring the aggregated ***,due to this reason,the energy of each communicating node is gradually decreased and the network lifetime is also *** increase the lifetime of the network,the proposed algorithm uses two sets:Dominating set and hit ***/methodology/approach-The proposed algorithm uses two sets:Dominating set and hit *** dominating set constructs an unequal clustering,and the hit set minimizes the number of communicating nodes by selecting the optimized cluster head for transferring the aggregated data to the base *** simulation results also infer that the proposed optimized unequal clustering algorithm(OUCA)is greater in improving the network lifetime to a maximum amount of 22%than the existing cluster head selection approach considered for ***-In this paper,lifetime of the network is prolonged by constructing an unequal cluster using the dominating set and electing an optimized cluster head using hit *** dominator set chooses the dominator based on the remaining energy and its node degree of each *** optimized cluster head is chosen by the hit set to minimize the number of communicating nodes in the *** proposed algorithm effectively constructs the clusters with a minimum number of communicating nodes using the dominating and hit *** simulation result confirms that the proposed algorithm prolonging the lifetime of the network efficiently when compared with the existing algorithms.

关键词： Unequal clustering Hit set Dominating set Network lifetime

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Optimized Weighted Ensemble Using Dipper Throated Optimization Algorithm in Metamaterial Antenna

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computers, Materials & Continua 2022年第12期73卷 5771-5788页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz AAbdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

Metamaterial Antennas are a type of antenna that uses metamaterial to enhance *** bandwidth restriction associated with small antennas can be solved using metamaterial *** learning is gaining popularity as a way to improve solutions in a range of *** learning approaches are currently a big part of current research,and they’re likely to be huge in the *** model utilized determines the accuracy of the prediction in large *** goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna’s bandwidth and *** basic models employed in the developed ensemble are Support Vector Regression(SVR),K-NearestRegression(KNR),Multi-Layer Perceptron(MLP),Decision Trees(DT),and Random Forest(RF).The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization(DTO)*** choose the best features from the dataset,the binary(bDTO)algorithm is *** proposed ensemble model is compared to the base models and results are recorded and analyzed *** addition,two other ensembles are incorporated in the conducted experiments for *** ensembles are average ensemble and K-nearest neighbors(KNN)-based *** comparison is performed in terms of eleven evaluation *** evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models.

关键词： Metamaterial antenna dipper throated optimization feature selection parameters prediction

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Automated Text-based Depression Detection using Hybrid ConvLSTM and Bi-LSTM Model 3

Automated Text-based Depression Detection using Hybrid ConvL...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Firoz, Neda Beresteneva, Olga Grigorievna Vladimirovich, Aksyonov Sergey Tahsin, Mohammad Sadman Tafannum, Faiza Tomsk State University Institute of Applied Mathematics and Computer Science Tomsk Russia Tomsk Polytechnic University Department of I.T Institute of Applied Mathematics and Computer Sciences Tomsk Russia Brac University Department of Computer Sciences and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665462167

Depression and its symptoms are very common disorders of mental health. They affect the day-to-day activity of the person and degrade the quality of life. The article presents the comparative study of different deep learning models on natural language processing data for detection of depression using textual data. Several studies have been performed for depression detection using artificial intelligence and deep learning state of the art methods. This article investigates the state-of-the-art models and perform hyperparameter tuning for best accuracy results and develop our own hybrid model for detection of depression with improved accuracy scores. The aim of our study is to research and compare the existing findings in deep learning and machine learning models for depression detection and build a precise hybrid model for depression detection with higher accuracy and scores. © 2023 IEEE.

关键词： Long short-term memory

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Witness Test Program Generation through AST Node Combinations

Witness Test Program Generation through AST Node Combination...

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IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW

作者： HeuiChan Lim Department of Mathematics and Computer Science Davidson College USA

ISBN: (数字)9798331534677

ISBN: (纸本)9798331534684

Compiler bugs can be critical, but localizing them is challenging because of the limited debugging information and complexity of compiler internals. A common approach to address this challenge is to analyze the compiler’s execution behavior while it processes witness test programs. To accurately localize a bug in the compiler, the approach must generate effective witness programs by identifying and modifying the code sections in the seed program that are directly associated with the bug. However, existing methods often struggle to generate such programs due to the random selection of code parts for mutation. In this paper, we propose a novel automated technique to (1) identify abstract syntax tree nodes that are most likely to be related to the bug in the compiler, which can transform a failing program into a passing one, and (2) use this information to generate witness test programs that can effectively localize compiler bugs. To evaluate the effectiveness of our approach, we built a prototype and experimented with it using 40 real-world LLVM bugs. Our approach localized 40% and 100% of bugs within the Top-1 and Top-5 compiler files, respectively.

关键词： Location awareness Software testing Program processors Codes Conferences computer bugs Prototypes Transforms Debugging Syntactics

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ML-Driven Solutions for Chronic Liver Disease: Predictive Models and Prevention Strategies

ML-Driven Solutions for Chronic Liver Disease: Predictive Mo...

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2024 IEEE International Conference on Computing, ICOCO 2024

作者： Imam, Touhid Islam, Jahirul Sultana, Sharmin Uddin, M.D. Salah Uddin, Mohammad Shihab Uddin, Bushra Department of Computer Science University of South Dakota VermillionSD United States Department of Mathematics New Mexico Tech SocorroNM United States Department of Mathematics Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9798331530303

Chronic liver disease (CLD) is a major global health issue, often progressing silently until it reaches advanced stages, leading to severe illness and death if not detected early. The liver plays a critical role in filtering toxins and aiding digestion, so any disruption can have serious consequences. Raising awareness about liver health is crucial because early detection can change the disease’s course, opening doors to treatment and even reversing damage in some cases. In the United States, CLD has become a growing health crisis, with increasing deaths linked to conditions like hepatitis C, alcohol-related liver disease, and nonalcoholic fatty liver disease (NAFLD), largely due to rising obesity rates. These statistics highlight the urgent need for improved prevention, early diagnosis, and treatment strategies. Advances in machine learning (ML) offer new hope in fighting liver disease. Predictive models like RandomForest, LightGBM, XGBoost, Logistic Regression, Ensemble Learning Stacking, and ExtraTrees can accurately forecast the onset and progression of liver disease. These models enable earlier identification of at-risk individuals and more effective interventions, potentially transforming how we manage CLD. By leveraging these technological innovations, we can significantly reduce mortality rates and empower healthcare providers to take swift, decisive action in preventing and treating liver diseases. ©2024 IEEE.

关键词： Contrastive Learning

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Unifying Sky and Chain: A Perspective on Blockchain's Role in UAV Swarms 2

Unifying Sky and Chain: A Perspective on Blockchain's Role i...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Phadke, Abhishek Sekharan, Chandra N Vasudeva, Karthik Kumar Kar, Dulal Christopher Newport University Department of Physics Computer Science and Engineering Newport NewsVA United States Texas A&m University-Corpus Christi Department of Computer Science Corpus ChristiTX United States Texas Tech University Department of Mathematics and Statistics LubbockTX United States

ISBN: (纸本)9798331529529

UAV (Unoccupied Aerial Vehicle) swarms are coordinated agents working towards accomplishing a common goal in a dynamic environment. The nature of the environment is such that these swarms can always benefit from upgrades to their operation, behavior, and security. Incorporating blockchain technology into UAV swarms presents a transformative approach to enhancing their security, operations, and coordination. Blockchain is a decentralized ledger technology that offers a robust solution to scalability, data integrity, and trust challenges in many modern world systems. It can also be a potentially excellent method to enhance UAV swarm systems. By harnessing its security, transparency, and immutability characteristics, blockchain can offer a range of advantages to current deployments. This study categorizes the various areas of UAV swarm systems that benefit from this integration, briefly reviews current methods and architecture, and discusses three prominent case studies for integration. Thorough an examination of current trends, it is concluded that blockchain technology can significantly boost its target systems' capabilities, paving the way for advanced and reliable aerial swarm operations. © 2024 IEEE.

关键词： Blockchain

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Cell-based allometry:an approach for evaluation of complexity in morphogenesis

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Quantitative Biology 2023年第2期11卷 183-203页

作者： Ali Tarihi Mojtaba Tarihi Taki Tiraihi Department of Computer Engineering Faculty of Computer Science and EngineeringShahid Behshti UniversityTehran 19839-69411Iran Department of Computer Engineering Sharif University of TechnologyTehran 11365-11155Iran Department of Anatomical Sciences School of Medical SciencesTarbiat Modares UniversityTehran 14117-13116Iran

Background:Morphogenesis is a complex process in a developing animal at the organ,cellular and molecular *** this investigation,allometry at the cellular level was ***:Geometric information,including the time-lapse Cartesian coordinates of each cell's center,was used for calculating the allometric coefficients.A zero-centroaxial skew-symmetrical matrix(CSSM),was generated and used for constructing another square matrix(basic square matrix:BSM),then the determinant of BSM was calculated(d).The logarithms of absolute d(Lad)of cell group at different stages of development were plotted for all of the cells in a range of development stages;the slope of the regression line was estimated then used as the allometric ***,the lineage growth rate(LGR)was also calculated by plotting the Lad against the logarithm of the *** complexity index at each stage was *** method was tested on a developing Caenorhabditis elegans ***:We explored two out of the four first generated blastomeres in *** *** ABp and EMS lineages show that the allometric coefficient of ABp was higher than that of EMS,which was consistent with the complexityindexas well ***:The conclusion of this study is that the complexity of the differentiating cells in a developing embryo can be evaluated by allometric scaling based on the data derived from the Cartesian coordinates of the cells at different stages of development.

关键词： embryogenesis allometry complexity C.elegans bioinformatics skew matrix morphogenesis

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Measuring Fairness in AI Explanations with LEADR: Local Explanation Amplification Disparity Ratio 22nd

Measuring Fairness in AI Explanations with LEADR: Local Ex...

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22nd International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2024

作者： Shoeibi, Niloufar DeGange, Jonathan Shoeibi, Nastaran Shoeibi, Ali Department of Computer Science and Automation Faculty of Sciences Universidad de Salamanca Salamanca Spain LLP Madrid Spain LLP Washington United States Mazandaran University of Science and Technology Babol Iran

ISBN: (纸本)9783031704147

We investigate the fairness of local explanations in AI models by comparing the mean explanations for privileged and unprivileged groups across various datasets and model types. Specifically, we train linear, multi-layer perceptron, and explainable boosting machine (EBM) models, on several key academic datasets. Local explanations are generated using both post-hoc methods (LIME), and direct methods - logistic regression, integrated gradients and EBM local feature importance. By comparing these explanations across models and methods, we introduce a new metric, the Local Explanation Amplification Disparity Ratio (LEADR), to measure disparities in feature attribution between privileged and unprivileged groups. Our preliminary findings suggest that transparent Whitebox models may exhibit a tendency to display greater disparity in bias than opaque Blackbox models. This insight encourages further research into bias mitigation strategies that are tailored to different algorithm types, with the goal of minimizing undesired bias in AI systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Logistic regression

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