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International Conference on Computing and Networking Technology (ICCNT)

作者： Debashis Kundu Md. Mushfiqur Rahaman Md. Nazmus Sakib Md. Faruk Abdullah Al Sohan Jannatul Fardaush Mithu Afzal Hossain Nishita Aktar Dept. Management Information Systems American International University- Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Daffodil Institute of IT Dhaka Bangladesh Dept. of Computer Science and Engineering World University of Bangladesh Dhaka Bangladesh Dept. of Computer Science American International University- Bangladesh Dhaka Bangladesh Dept. of Electrical and Computer Engineering North South University Dhaka Bangladesh Dept. of Computer Science and Engineering Army Institute of Business Administration (AIBA) Savar Dhaka Bangladesh

Qualitative approach for automated grading and quality assessment of fruits, machine learning techniques are crucial in agricultural applications. Automation enhances a nation’s agricultural quality, production, and economic prosperity. Fruit quality grading, particularly the surface fault identification of a fruit, is a crucial indicator in the export market. This is particularly important for mangoes, which are quite well-liked inBangladesh. On the other hand, the physical grading of mangoes is a procedure that is labor-intensive, prone to error, and very subjective. In this paper, we proposed a YOLOv7 integrated Discrete wave transformation computer vision system. The proposed model includes support vector machine (SVM) and decision tree for the classification of high-quality mangoes. The results of the experiments show that the proposed solution obtained 96.25% accuracy when the system was trained and tested using a publicly accessible mango database.

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Lightweight Spiking Neural Network Based Detector for Interweave Cognitive Radios

Lightweight Spiking Neural Network Based Detector for Interw...

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IEEE International Conference on Communications (ICC)

作者： Md Mehedi Hassan Galib Mohamed Younis Sultan Ahmed Dept. of Computer Science and Electrical and Engineering University of Maryland Baltimore County Baltimore Maryland Information Systems Department University of Maryland Baltimore County Baltimore Maryland

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The major advances in wireless communication technology have led to increased adoption across almost all application domains. However, the massive growth has caused spectrum scarcity despite the fact that many of the frequency bands are not fully-utilized. Cognitive radios have emerged as a viable means to support dynamic spectrum access. Particularly, supporting opportunistic access through passive spectrum monitoring is of great interest. Existing techniques for detecting white space either require modification to commodity radio transceivers, or involve computationally complex models that do not suit resource-constrained devices. This paper opts to fill the technical gap by proposing a novel lightweight white space detector that employs spiking neural networks (SNN). SNN is a bio-inspired technique for creating data-driven models. The proposed design relies on the sensed energy in the medium to determine whether a primary user is active. The validation results using live LTE data demonstrate the effectiveness of our novel detector. Suitability for edge devices is confirmed through implementation on a Raspberry-PI platform.

关键词： Accuracy Biological system modeling Image edge detection Computational modeling Dynamic spectrum access Detectors Spiking neural networks

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A Security Aware Lighting Control System for Public Open Spaces: An IoT Powered Approach

A Security Aware Lighting Control System for Public Open Spa...

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Computational Intelligence, Networks and Security (ICCINS), International Conference on

作者： Mohamed Fazil Mohamed Firdhous Walid Elbreiki Ibrahim Abdullahi Wesam Briki Shivaleela Arlimatti Dept. of Information Technology University of Moratuwa Moratuwa Sri Lanka Dept. of Computer Science College of Computer Technology Benghazi Libya Dept. of Computer Science Ibrahim Badamasi Babangida University Lapai Niger State Nigeria Dept. of Computer Science Higher Institute of Engineering Technologies Benghazi Libya Dept. of Computer Science and Engineering Dr. D Y Patil Prathisthan's College of Engineering Kolhapur Maharashtra India

Cities world over are becoming more congested as people moving into them for better living conditions. Such fast migration towards cities puts heavy demands on city resources. One of the resources that suffers an impact almost immediately is the open spaces in cities. Open spaces are the public spaces that are accessed by people freely for different purposes. Open spaces help citizens maintain a healthy life by relieving stresses. The open spaces visited by people at night must provide adequate lighting for them to engage in their activities. It is seen that many open spaces waste energy as the lights have been on throughout the nights irrespective of usage of the space. In this paper, the authors propose a security aware lighting control system that helps minimize the energy waste without compromising on security. A prototype implementation was tested and the results were positive promising real field implementations.

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Effective Dimension in Bandit Problems under Censorship

arXiv

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arXiv 2023年

作者： Guinet, Gauthier Amin, Saurabh Jaillet, Patrick AWS AI Labs United States MIT United States Dept. of Civil and Environmental Engineering Laboratory for Information and Decision Systems United States Dept. of Electrical Engineering and Computer Science & Laboratory for Information and Decision Systems United States

In this paper, we study both multi-armed and contextual bandit problems in censored environments. Our goal is to estimate the performance loss due to censorship in the context of classical algorithms designed for uncensored environments. Our main contributions include the introduction of a broad class of censorship models and their analysis in terms of the effective dimension of the problem – a natural measure of its underlying statistical complexity and main driver of the regret bound. In particular, the effective dimension allows us to maintain the structure of the original problem at first order, while embedding it in a bigger space, and thus naturally leads to results analogous to uncensored settings. Our analysis involves a continuous generalization of the Elliptical Potential Inequality, which we believe is of independent interest. We also discover an interesting property of decision-making under censorship: a transient phase during which initial misspecification of censorship is self-corrected at an extra cost;followed by a stationary phase that reflects the inherent slowdown of learning governed by the effective dimension. Our results are useful for applications of sequential decision-making models where the feedback received depends on strategic uncertainty (e.g., agents’ willingness to follow a recommendation) and/or random uncertainty (e.g., loss or delay in arrival of information). © 2023, CC BY.

关键词： Decision making

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Human Activity Recognition in Free Living Using a Wearable Sensor: A Two-Stage Approach

Human Activity Recognition in Free Living Using a Wearable S...

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International Conference on Awareness science and Technology (iCAST)

作者： Tianhui Li Xue Zhou Kota Kokubun Wenxi Chen Division of Information System The University of Aizu Aizu-Wakamatsu Japan Dept. of Computer and Information Systems The University of Aizu Aizu-Wakamatsu Japan Dept. of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan

A novel two-stage approach for human activity recognition (HAR) using a wearable sensor is proposed in this study. The goal is to achieve both coarse-level and fine-level recognition of activities. The study focuses on five common activities: desk-working, driving, walking, downstairs, and upstairs. Three-axis acceleration signals were collected using a wrist-worn accelerometer sensor, and the activities were annotated during the participant’s daily routines. The acceleration data were preprocessed through segmentation, feature extraction, and normalization. In the first stage, a traditional machine learning model (decision tree) was used for coarse-level classification, distinguishing between activities with exercise and without exercise. In the second stage, deep learning models were employed for fine-level classification to recognize specific activities within the coarse classes. The performance of the models was evaluated using accuracy, sensitivity, and specificity. The proposed approach showed promising results with accuracies of 93.53%, 93.52%, 85.70%, 98.11%, and 85.91% respectively for desk-working, driving, walking, downstairs, and upstairs, demonstrating the effectiveness of the two-stage strategy for HAR. The proposed two-stage approach outperforms the one-stage method (decision tree classifier and deep learning model), particularly in imbalanced data, achieving a graceful equilibrium between sensitivity and specificity. The study highlights the importance of considering the complexity and variability of daily activities for accurate classification and resource optimization.

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Evaluation of Meteorological Factors’ Effect on Daily Heart Rate Variability Over Five Years

Evaluation of Meteorological Factors’ Effect on Daily Heart...

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International Conference on Awareness science and Technology (iCAST)

作者： Kota Kokubun Tianhui Li Wenxi Chen Dept. of Computer and Information Systems The University of Aizu Aizu-Wakamatsu Japan Division of Information System The University of Aizu Aizu-Wakamatsu Japan Dept. of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan

The aim of this study is to investigate the effect of meteorological factors on heart rate variability (HRV) derived from daily electrocardiogram (ECG) data. Daily meteorological data were obtained from the Japan Meteorological Agency’s database. Daily ECG data were measured while bathing for five years. The subject was a healthy male in his fifties. After collecting ECG data, R-R intervals were calculated to perform HRV analysis in time domain and frequency domain. The evaluation of link between meteorological factors and HRV parameters was realized by utilizing meteorological factors’ importance calculated by Random Forest, and Steel-Dwass significant difference analysis of the values. Additionally, the evaluation was also performed by analyzing the obtained meteorological factors’ importance values using Granger Causality test. In Steel-Dwass test, several significant differences (p<0.05) were found, especially the importance value of air temperature was significantly different in three HRV parameters. I n Granger Causality test, we evaluate the temperature’s importance value for three HRV parameters, and there were significant differences (p<0.05) for HF and LF/HF.

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A Novel Transfer Learning Framework for Diagnosing Covid-19 from Chest X-Ray Images with Minimal Computational Resources

SSRN

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SSRN 2023年

作者： Abdelwahed, Ahmed Helmi, Ahmed M. Issa, Mohamed Computer and Systems Department Faculty of Engineering Zagazig University Egypt Computer Engineering Dept. Engineering and Information Technology College Buraydah Private Colleges Saudi Arabia Faculty of Computer Science and Information Technology Egypt−Japan University for Science and Technology Egypt

Deep learning proved effective in detecting COVID-19 from chest X-ray images. Although computing is the current bottleneck in training and deploying models, few studies focus on developing efficient frameworks while many aim only at performance. We propose a novel framework for detecting COVID-19 cases from chest X-ray images efficiently. Our study utilizes four high-performing architectures, including MobileNetV3, RegNetX, NasNetMobile, and EfficientNetV2, which use minimal resources, to extract features from X-ray images and classify COVID-19 cases using various statistical models and machine learning algorithms. We evaluated our proposed framework using metrics such as accuracy, precision, recall, specificity, f1-score, and AUC scores. The false negative rate for each feature extractor and classifier pair is reported, and the average inference time, to measure resource efficiency, is calculated. The results show that EfficientNetV2 was the best-performing feature extractor, while MobileNetV3 had the best average inference time and the lowest false negative rate. The Neural Network classifier performed the best, with state-of-the-art accuracy reaching 99.28%, followed by both RBF-SVM and XGBoost. The proposed framework is more robust, reliable, and resource-efficient for COVID-19 detection compared to existing literature. Our findings indicate the prospects of using the proposed framework in efficient clinical diagnosis. © 2023, The Authors. All rights reserved.

关键词： COVID-19

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Learning-Based Rich Feedback HARQ for Energy-Efficient Uplink Short Packet Transmission

arXiv

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arXiv 2023年

作者： Vejling, Martin V. Chiariotti, Federico Kalør, Anders E. Gündüz, Deniz Liva, Gianluigi Popovski, Petar Dept. of Electronic Systems Aalborg University Denmark Dept. of Information Engineering University of Padova Italy Dept. of Information and Computer Science Keio University Japan Dept. of Electrical and Electronic Engineering Imperial College London United Kingdom Wessling Germany

The trade-off between reliability, latency, and energy efficiency is a central problem in communication systems. Advanced hybrid automated repeat request (HARQ) techniques reduce retransmissions required for reliable communication but incur high computational costs. Strict energy constraints apply mainly to devices, while the access point receiving their packets is usually connected to the electrical grid. Therefore, moving the computational complexity from the transmitter to the receiver may provide a way to improve this trade-off. We propose the reinforcement-based adaptive feedback (RAF) scheme, a departure from traditional single-bit feedback HARQ, introducing adaptive rich feedback where the receiver requests the coded retransmission of specific symbols. Simulation results show that RAF achieves a better trade-off between energy efficiency, reliability, and latency, compared to existing HARQ solutions. Our RAF scheme can easily adapt to different modulation schemes and can also generalize to different channel statistics. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

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A Deep Learning for Predicting Possibility of Bluetooth Low Energy Tag Signals

A Deep Learning for Predicting Possibility of Bluetooth Low ...

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International computer Symposium (ICS)

作者： Hao-Pei Teng Vincent S. Tseng Josh Jia-Ching Ying Dept. Management Information Systems National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering National Yang Ming Chiao Tung University Hsinchu City Taiwan

ISBN: (数字)9798331509071

ISBN: (纸本)9798331509088

With the rapid growth of IoT technology, using Bluetooth Low Energy (BLE) gateways to track the trajectory of elderly individuals can help prevent them from getting lost. However, BLE tag signals do not persist, and BLE gateways often enter standby mode for extended periods, resulting in unnecessary energy waste and reducing the lifespan of the devices. To address this issue, predicting the likelihood of BLE tag signals is essential to conserve energy. This paper presents a deep learning approach to enhance the operational efficiency and lifespan of BLE gateways deployed in an IoT system. The system consists of two major modules: the Number of BLE Tags Estimation Module and the Next Accessed BLE Gateway Prediction Module. The Number of BLE Tags Estimation Module leverages a CNN-GRU (Convolutional Neural Network - Gated Recurrent Unit) architecture to predict the time lapse between the sensing of signals from BLE tags. The Next Accessed BLE Gateway Prediction Module employs a self-attention mechanism to predict which specific BLE gateway will receive the BLE tag signal, further optimizing system performance. Experimental results show that the proposed prediction model effectively reduces unnecessary energy consumption, thereby improving the efficiency and service life of the entire IoT system.

关键词： Deep learning Urban areas Estimation Logic gates Transformers Sensors Convolutional neural networks Older adults Bluetooth Low Energy Protection

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Robust Heart Disease Prediction: A Hybrid Approach to Feature Selection and Model Building 4

Robust Heart Disease Prediction: A Hybrid Approach to Featur...

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4th International Conference on Ubiquitous Computing and Intelligent information systems. ICUIS 2024

作者： Kodete, Chandra Shikhi Pasupuleti, Vikram Thuraka, Bharadwaj Sangaraju, Varun Varma Tirumanadham, N. S. Koti Mani Kumar Shariff, Vahiduddin School of Technology Eastern Illinois University CharlestonIL61920 United States School of Computer Science and Information Systems Northwest Missouri State University MaryvilleMO64468 United States University of the Cumberlands WilliamsburgKY4076 United States Sir C R Reddy College of Engineering Dept. of Cse Eluru India

ISBN: (纸本)9798331529635

This research work integrates advanced data preparation methods and advanced modelling approaches to offer a complete approach for creating a predictive model for heart disease. Data cleansing starts the procedure in order to fix disparities and manage missing values, thereby guaranteeing the dependability and accuracy of the dataset. By producing synthetic samples for the minority class, the Synthetic Minority Over-sampling Technique (SMOTE) solves class imbalances and improves the accuracy of the model to forecast minority class occurrences. The z-score approach helps to control outliers thereby preventing distortion in the study. Using a hybrid Filter-Recursive Feature Elimination (FRFE) approach-which combines filter and wrapper techniques to find the most pertinent features, therefore lowering multicollinearity and boosting model performance-feature selection is accomplished. Important characteristics found are age, kind of chest discomfort, maximal heart rate attained, ST depression brought on by exercise, and fluoroscopy-induced coloration of several major arteries. Using AdaBoost, an ensemble learning technique combining several weak classifiers to create a strong one, the improved dataset is utilised to develop a predictive model. The resultant model shows great accuracy (93%), precision (93%), recall (94%), and an amazing ROC AUC score (0.98), therefore highlighting its potential use in healthcare environments and heart disease prediction ability. © 2024 IEEE.

关键词： Feature Selection

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