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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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Motion Profile Optimization in Industrial Robots using Reinforcement Learning

Motion Profile Optimization in Industrial Robots using Reinf...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Yunshi Wen Honglu He Agung Julius John T. Wen Electrical Computer and Systems Engineering Dept. Rensselaer Polytechnic Institute Troy NY USA

Path tracking problems are challenging with the absence of dynamic models and information about robot controllers. This paper presents a method of optimizing a motion profile constructed using a set of pre-defined motion primitives and a speed command to track a spatial trajectory with high accuracy, speed, and uniform motion using industrial robots. We use a bi-level optimization approach that optimizes execution accuracy using reinforcement learning and execution speed using bi-section search. We train and evaluate the reinforcement learning policy in simulation for an ABB robot. Experiment results demonstrate that the learned policy reduces the optimization cost to achieve the desired specifications. Additionally, the trained policy can generalize to trajectories not included in the training set.

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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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Assessment of Backup Generator Connection Barriers to Australian Medium Voltage Networks

Assessment of Backup Generator Connection Barriers to Austra...

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Energy Technologies for Future Grids (ETFG), IEEE International Conference on

作者： Mingchen Gu Chathuranga Wanninayaka Moudud Ahmed Kazi N. Hasan Lasantha Meegahapola Mahdi Jalili Richardt Wilkinson Brendan McGrath Xinghuo Yu Reza Razzaghi School of Engineering RMIT University Australia Dept. of Electrical and Computer Systems Engineering Monash University Australia

Demand response (DR) programs can benefit electricity consumers, distribution network service providers (DNSPs), system operators and the energy market. However, the complexity and characteristics associated with loads connected to customer premises, utilisation of backup generators for DR and the baseline calculation process still require considerable research to optimise and further unlock the capabilities of DR programs. This paper analyses the barriers to using backup generators in medium voltage (MV) networks in DR programs. Two representative Australian MV networks (e.g., Urban-NSW1 network, Urban-VIC network) are analysed in DIgSILENT PowerFactory under different scenarios, such as variation in locations, power export limits, and dynamic export limits of backup generators. The study has found that a backup generator located at the end of the network can achieve better performance in supporting the network voltage. Moreover, the voltage limit and thermal limit of the power network mainly constrain the export limits of backup generators. Furthermore, a backup generator with a dynamic export limit can export 1 to 5 times more energy than the static export limit.

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A Dementia Diagnosis Technique Based on AI and Hardware Acceleration

A Dementia Diagnosis Technique Based on AI and Hardware Acce...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Khalil, Kasem Khan Mamun, Mohammad M. R. Sherif, Ahmed Elsersy, Mohamed Imam, Ahmad Abdel-Aliem Abouzaid, Kamal University of Mississippi Electrical and Computer Engineering Department MS United States Tennessee Technological University Electrical and Computer Engineering Department TN United States University of Southern Mississippi School of Computing Sciences and Computer Engineering MS United States Higher Colleges of Technology Computer and Information Systems Dept. Al Ain United Arab Emirates William Carey University College of Osteopathic Medicine Mississippi MS United States

ISBN: (纸本)9798350322347

The effects of Alzheimer's disease (AD) are devastating, both personally and within the patient's family, as the disease progresses slowly over many years. It could significantly affect illness consequences and outcomes if detected early and accurately. Non-invasive medical testing often uses blood bio-samples because they are easy to collect and relatively cheap to analyze. This paper proposes a federated learning (FL)-based diagnostic model for AD using blood bio-samples. We have used blood bio-sample data sets downloaded from the ADNI website to test and compare the efficacy of our models. According to the enormous data collected for early AD detection, we used a hardware acceleration scheme to implement our FL model to fasten the training and testing operations. The hardware-Accelerator method is implemented using VHDL and Altera 10 GX FPGA. The simulation results show the proposed algorithms reach an accuracy and sensitivity of 89% and 87%, respectively, for early detection while taking less time to train than other state-of-The-Art algorithms. The proposed algorithms also consume 52-172 mW, which makes them suitable for constrained devices. © 2023 IEEE.

关键词： Diagnosis

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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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On-chip GaAs carrier lifetime investigation

arXiv

引用

arXiv 2025年

作者： Prayoonyong, Chawaphon Szydzik, Crispin Ren, Guanghui Corcoran, Bill Dubey, Aditya Murray, Caitlin E. Mitchell, Toby Mitchell, Arnan Photonic Communications Laboratory Dept. Electrical and Computer Systems Engineering Monash University ClaytonVIC Australia MelbourneVIC Australia

We propose an alternative way to determine GaAs carrier lifetime using pump-probe measurement based on fibre optics and integrated waveguides. We find that our GaAs samples have the lifetime ranging from 30-80 ps, supporting the bandwidth ≥ 12.5 GHz. The platform utilised in this work could offer a cost-effective way to investigate photocarrier lifetime. Moreover, it may have the potential for high-speed switches and detectors, or could be exploited for possible all-optical modulation. © 2025, CC BY-NC-ND.

关键词： Gallium arsenide

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TOPMG: Trust-Based Crowdsourcing through Multilateral Bargaining Game Theory

TOPMG: Trust-Based Crowdsourcing through Multilateral Bargai...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Panagiotis Charatsaris Adedamola Adesokan Aisha B Rahman Eirini Eleni Tsiropoulou Symeon Papavassiliou Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Crowdsourcing plays a critical role in modern information gathering and task execution, yet it faces challenges regarding the task selection and equitable monetary incentives distribution. In this paper, we introduce the TOPMG framework, which addresses these challenges by enabling the workers to select tasks based on their historically experienced monetary incentives and the platforms’ trustworthiness. Specifically, the TOPMG framework utilizes a reinforcement learning approach based on the principles of Optimistic Q-learning with Upper Confidence Bound (OQ-UCB) algorithm, guiding the platform selection process by considering the workers’ monetary incentives, profit, and the platforms’ trustworthiness. Also, the proposed framework introduces a multilateral bargaining game to allocate the platforms’ monetary incentives to the workers by prioritizing their information contribution, fairness, and the platforms’ reputation. Simulation results demonstrate TOPMG’s operational dynamics, scalability, and efficacy, as well as its superiority over existing methodologies.

关键词： Crowdsourcing Q-learning Social networking (online) Simulation Scalability Heuristic algorithms Games Global communication Game theory Faces

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