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International Conference on computer Communication and Informatics (ICCCI)

作者： Md Shariar Kabir Jakia Khanom Md. Atik Bhuiyan Zerin Nasrin Tumpa Sk. Fazlee Rabby Saurabh Bilgaiyan Dept. of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

Early dementia detection is a crucial but challenging task in Bangladesh. Often, dementia is not recognized until it is too late to receive effective care. This results in part from a lack of knowledge about the illness and its signs and symptoms. Recent improvements in machine learning algorithms, however, may change this. In a recent study, we developed a model that can identify early dementia in Bangladesh using machine learning algorithms. This research paper proposed an efficient machine learning-based approach for early detection of dementia disease A dataset of 199 people with dementia and 175 healthy controls was used to develop the model. In 96% of the cases, the algorithm correctly identified dementia. This is a significant accomplishment that could revolutionize Bangladesh's dementia detection process. For patients to get the care they require, early dementia detection is essential. This study offers a proof-of-concept for the use of machine learning in dementia early detection & The results of this study suggest that machine learning models can be used as a powerful tool for early detection of dementia.

关键词： Surveys Machine learning algorithms Sociology Machine learning Predictive models Prediction algorithms Task analysis

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SPORTS CENTER CUSTOMER SEGMENTATION: A CASE STUDY

arXiv

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arXiv 2024年

作者： Soto, Juan Carmenaty, Ramón Lastra, Miguel Fernández-Luna, Juan Manuel Benítez, José M. Computer Science and Artificial Intelligence Dept. DiCITS Lab iMUDS Universidad de Granada Spain Software Engineering Dept. DiCITS Lab iMUDS Universidad de Granada Spain

Customer segmentation is a fundamental process to develop effective marketing strategies, personalize customer experience and boost their retention and loyalty. This problem has been widely addressed in the scientific literature, yet no definitive solution for every case is available. A specific case study characterized by several individualizing features is thoroughly analyzed and discussed in this paper. Because of the case properties a robust and innovative approach to both data handling and analytical processes is required. The study led to a sound proposal for customer segmentation. The highlights of the proposal include a convenient data partition to decompose the problem, an adaptive distance function definition and its optimization through genetic algorithms. These comprehensive data handling strategies not only enhance the dataset reliability for segmentation analysis but also support the operational efficiency and marketing strategies of sports centers, ultimately improving the customer experience. © 2024, CC BY-NC-ND.

关键词： Genetic algorithms

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Establishing a Correlation of Clinical Characteristics with the Level of Tumor Mutation Burden in Urothelial Bladder Carcinoma

Establishing a Correlation of Clinical Characteristics with ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lujain Alshomali Rania Khorma Abdullah Al-Refai Abedalrhman Alkhateeb Software Engineering Dept. Princess Sumaya University for Technology Amman Jordan Computer Science Dept. Lakehead University Thunder Bay Canada

Bladder cancer is a complex disease and one of the most lethal types of cancer. Recently, some malignancies, including bladder carcinomas, have shown better results with immunotherapy using immune checkpoint inhibitors. Tumor mutational burden (TMB) is a potential biomarker for predicting tumor behavior and immunotherapy response as an outcome. Publicly available clinical data from the bladder cancer of TCGA project is used to analyze correlations of clinical variables with an increased tumor mutation burden (TMB) number compared with those with a lower number of mutations. The threshold for the high mutation burden in the analysis was set at 10 mutations (Mut) per Megabase (Mb). The Chi-Square test (χ 2 ) was used to compare categorical data. The Chi-Square "Best first" method was used to find a correlation between clinical variables and TMB, then compared with the p-value of significance (p<0.05). A significant correlation was found between TMB and Race, Neoplasm Histologic Grade, and gender when applying the Best First/Chi-Square method to clinical variables and level of TMB. This enables further investigation and application of the prediction models of the level of TMB, responsiveness to immunotherapy, and prognosis based on the clinical features of the patients.

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Wildfire Spread Prediction Using Attention Mechanisms in U-NET

Wildfire Spread Prediction Using Attention Mechanisms in U-N...

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Electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Kamen Shah Maria Pantoja Dept. Computer Science and Software Engineering California Polytechnic State University San Luis Obispo USA

Global warming has increased large-scale natural disasters at an alarmingly greater frequency. These natural disasters affect humans and the ecosystems that all species rely upon for food and shelter. A significant byproduct of global warming is the emergence of mega-fires, which are wildfires that burn more than 100,000 acres. Mega fires have exhibited the capacity to destroy entire ecosystems and urban areas. Researchers forecast a significant increase in wildfires during the next decade. This is a fundamentally growing problem that mandates modern solutions. Currently, simulation models are employed to predict wildfire propagation and aid firefighters in suppressing them. However, these methods are computationally expensive and often inaccurate. A recent advance in this area is the usage of neural networks (NN), which are systematically more accurate and computationally efficient. This paper focuses on predicting wildfire spread using NN with an attention mechanism to improve spatial recognition and overall performance in neural networks for wildfire spread prediction. We train different models on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. Results show that when augmenting segmentation models with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, we use ensemble modeling to reduce bias and variation, leading to more consistent and accurate predictions. The architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1% when inferencing on wildfire propagation at 30-minute intervals.

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AI Assisted Diagnosis of Dengue Amid Arboviral Diseases, Severity Prediction, and Post Effects

AI Assisted Diagnosis of Dengue Amid Arboviral Diseases, Sev...

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International Conference on Information Technology Research (ICITR)

作者： Nivasheni B Fernando V.G.S.O. Srihari M Sarah M.I.L. Vishan Jayasinghearachchi Samadhi Chathuranga Rathnayake Dept. of Computer Science Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

This paper aims to examine the applicability of machine learning models for aiding in the diagnosis and management of arboviral illnesses, especially Dengue. The study focuses on three key objectives: Diagnosis of Dengue, prediction of prognosis of severe disease, and the identification of the post dengue effects. Multiple strategies were used to address these challenges. They are feature engineering based multiclass classification of arboviral diseases, enhancement of standard conventional machine learning algorithms for binary classification of severe Dengue and finally MLSOL enhancing ensemble model for drawing future effects of Dengue infections. The comparison of traditional models included both Random Forest, XGBoost, and also ensemble method while applying the feature selection methods of SFA and RFE. This study presents the comparison of traditional individual models and optimal ensemble method for the multiclass classification in arboviral diseases. In the case of binomial classification, the best accuracy level was 0.93 achieved by the Random Forest with RFE feature selection compared to Logistic Regression as well as the Gradient Boosting. Furthermore, the study proposed an MLSOL augmented ensemble approach to handle label imbalance problem in the dataset which in turn substantially enhanced the prediction accuracy. This approach decreased Hamming Loss to 0. 12 and increased the F-measure to 0.82, this has the capability of handling the imbalanced dataset and improving the predictive accuracy and performance. This machine learning framework offers great potential for clinical use in such aspects as early intervention and in effects following Dengue fever. It is meant to help healthcare practitioners and policy makers on how to better prevent and combat Dengue in Sri Lanka.

关键词： Accuracy Machine learning algorithms Medical services Feature extraction Ensemble learning Object recognition Prognostics and health management Viruses (medical) Random forests Standards

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Computational Model of Swarm Algorithm for Optimizing Process of Keywords Extraction from Text Information Presented as Graph

Computational Model of Swarm Algorithm for Optimizing Proces...

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International Conference on Information Technologies in engineering Education (Inforino)

作者： Yury Kravchenko Elmar Kuliev Daniil Kravchenko Sergey Rodzin Nikolay Pavlov Lada Rodzina Dept. of Computer Aided Design Southern Federal University Taganrog Russia Department of Software Engineering Southern Federal University Taganrog Russia

ISBN: (数字)9798350371369

ISBN: (纸本)9798350371376

The article describes the scientific problem of optimizing the processes of acquiring knowledge when processing textual information presented in the form of a graph, based on the development of a modified bioinspired algorithm for extracting keywords. The solution proposed by the authors differs from the known ones by introducing new attractive operators and behavioral patterns of bacterial agents into the computational model of the bacterial swarm algorithm. This approach allows you to balance the ratio of the speed of convergence of the algorithm with the diversification of the search space for solutions. The article also presents an analytical study of the features of two types of parsing: constituency parsing and dependency parsing. As part of the study, algorithms for syntactic parsing of dependencies between elements of text information and a graph-based dependency parsing algorithm are described. The formulation of the problem of keyword extraction is given. The task of achieving a balance between accuracy and speed of a linguistic parser is an open topic that requires further research due to the importance of this problem for improving the efficiency of text analysis, especially in applications that require real-time accuracy. To increase the efficiency of solving the problem, the authors propose a modified bioinspired algorithm for extracting keywords. The computational experiment carried out allows us to conclude that the modified bacterial optimization method proposed by the authors is in a leading position, which confirms the need to continue research into the applicability of bioinspired methods for solving problems of acquiring knowledge when processing text information presented in the form of a graph. This article is focused on the direction of study in the master’s program “Informatics and computer Science”, as well as the scientific specialty of postgraduate training “Artificial Intelligence and Machine Learning”.

关键词： Microorganisms Accuracy Text analysis Machine learning algorithms Computational modeling Optimization methods Syntactics

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Exploring Blockchain Technologies: Insights Into Consensus Mechanisms, Mining Pool Dynamics, and Energy Consumption Patterns

Exploring Blockchain Technologies: Insights Into Consensus M...

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International Conference on Mechatronics (ICOM)

作者： Abdulkadir Tasdelen Mohamed Hadi Habaebi Md Rafiqul Islam Dept. of Software Engineering Faculty of Engineering and Natural Sciences Ankara Yildirim Beyazit University Ankara Türkiye Faculty of Engineering IIUM Malaysia Dept. of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Kuala Lumpur Malaysia

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

Blockchain technologies and cryptocurrencies have garnered significant attention for their potential to revolutionize various industries. This paper investigates the landscape of blockchain technologies in the context of cryptocurrencies, focusing on performance metrics, trade-offs, trends, and underlying issues. Through comprehensive analysis, the most utilized consensus mechanisms among the top cryptocurrencies are identified. The distribution of top mining pools by hash rate over the last two years reveals dominance by a few major pools, with implications for decentralization and security. In Bitcoin mining pool distribution, trends indicate the dominance of established pools like Foundry USA Pool and AntPool, alongside notable growth from smaller pools like MARA Pool and Luxor. Comparative analysis across different cryptocurrencies highlights variations in hashing rate, block production, and energy consumption, underscoring the complex dynamics of blockchain mining. Moreover, the comparative analysis of energy consumption in blockchain mining hardware reveals insights into energy efficiency and sustainability. Smaller mining pools demonstrate significant growth and momentum, contributing to the diversification and decentralization of mining operations.

关键词： Measurement Industries Energy consumption Mechatronics Production Market research Hardware

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Exploiting TAS schemes to Enhance the PHY-security in Cooperative NOMA Networks: A Deep Learning Approach

Exploiting TAS schemes to Enhance the PHY-security in Cooper...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Yushintia Pramitarini Ridho Hendra Yoga Perdana Kyusung Shim Beongku An Dept. of Software and Communications Engineering in Graduate School Hongik University Republic of Korea School of Computer Engineering and Applied Mathematics Hankyong National University Republic of Korea Dept. of Software and Communications Engineering Hongik University Republic of Korea

In this paper, we propose a novel antenna selection scheme to enhance the secrecy performance in a relay-aided non-orthogonal multiple access (NOMA) network against an eavesdropper. Different from the conventional antenna selection schemes that does not use channel information, the proposed antenna selection scheme can employ each channel information to maximize the main channel capacity and minimize the eaves-dropper channel capacity, respectively. In order to evaluate the secrecy performance, we propose a deep learning (DL)-based framework that can do real-time configuration since the DL-based framework is based on a compact mapping function. In detail, the proposed min-max relay transmit antenna selection (MMRTAS) scheme can improve the secrecy performance compared to that of the benchmark scheme. Numerical results show that the proposed MMRTAS scheme improves the secrecy performance compared to that of the benchmark scheme. The proposed DL-based framework can estimate the main channel and eavesdropper channel capacities for the near user and far user with an accuracy of 99.79%, respectively.

关键词： Deep learning NOMA Channel capacity Transmitting antennas Receiving antennas Channel estimation Benchmark testing

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Artifact: Co-zyBench: A Thermal Comfort Provision Benchmark for Smart Buildings

Artifact: Co-zyBench: A Thermal Comfort Provision Benchmark ...

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IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Jun Ma Dimitrije Panic Roberto Yus Georgios Bouloukakis Télécom SudParis Institut Polytechnique de Paris France Dept. of Computer Science and Electrical Engineering University of Maryland Baltimore County USA

This paper presents a documentation and testing artifact for Co-zyBench [1], a benchmarking platform to evaluate and optimize thermal comfort provision systems in smart buildings. Co-zyBench is based on a co-simulation middleware that mediates between a Digital Twin (DT) of the smart building and HVAC system and a DT of the occupants of the building that simulates their dynamic thermal preference in different spaces. The DTs that support Co-zyBench are generated based on information about the space in which the thermal comfort system has to be evaluated. Co-zyBench includes a set of metrics that consider energy consumption, thermal comfort, and equality towards the occupants. Also, the benchmark includes a set of DTs based on standard buildings and HVAC systems and occupants (with different thermal preference distributions to represent diversity). Co-zyBench is based on the following components (see Figure 1):

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Driving Behavior Classification Using Smartphone Sensor Data

Driving Behavior Classification Using Smartphone Sensor Data

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International Conference on computer Science and engineering (UBMK)

作者： Deniz Dikbıyık Fatih Alagöz Dept. of Software Engineering Boğaziçi University İstanbul Türkiye Dept. of Computer Engineering Boğaziçi University İstanbul Türkiye

Given the substantial number of accidents because of human error, the development of automatic driver behavior monitoring systems has become a pressing need. By providing real-time monitoring and analysis of driving behavior, these systems have the potential to decrease the incidence of accidents and improve overall road safety. This study presents a novel analysis framework for classifying driving behavior based on data gathered from passengers' smartphones. The data were collected using our mobile application installed on the smartphones and processed using machine learning algorithms. The study utilized several machine learning classification t echniques, w ith a focus on developing a Long Short Term Memory (LSTM) algorithm for improved accuracy. A Federated Learning algorithm was also developed to collect the data not into one area and train a global model to apply for labeling the driving behavior. The results show the efficacy of the proposed approach in accurately classifying driving behavior based on data obtained from smartphones.

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