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Integrating Deep and Handcrafted Features for Enhanced Remote Sensing Image Classification

Integrating Deep and Handcrafted Features for Enhanced Remot...

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ACS/IEEE international conference on Computer Systems and Applications

作者： Vian Abdulmajeed Ahmed Khaled Jouini Amel Tuama Ouajdi Korbaa MARS Research Lab LR17ES05 ISITCom University of Sousse Sousse Tunisia Computer Engineering Techniques Department Northern Technical University Mosul Iraq

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Satellite imagery supports critical applications such as land cover mapping, environmental monitoring, disaster assessment, and urban planning. Despite significant advancements, challenges in analyzing satellite imagery persist, primarily due to data variability, atmospheric conditions, and complex land cover patterns. Traditional handcrafted descriptors like Scale-Invariant Feature Transform (SIFT) and encoding techniques such as Bag-of-Visual-Words (BoVW) are effective but often fall short in capturing global context and spatial relationships due to their inherent local nature. The advent of deep learning (DL), propelled by ample data and computational resources, has markedly improved satellite image analysis. However, the reliance on extensive annotated data constrains the wider applicability of DL methods. This study harnesses the strengths of both deep and hand-crafted features to enhance the classification accuracy of remote sensing images. Specifically, we synergize SIFT descriptors with pretrained MobileNetV2 and VGG16 deep features. While SIFT excels in capturing local features essential for identifying specific image characteristics, pretrained DL models provide enriched representations with global context, spatial relationships, and hierarchical features. This integration aims to overcome the individual limitations of each method, enabling the model to effectively handle perturbations, scale variations, and diverse landscapes. Extensive evaluations on the EuroSAT dataset demonstrate that our approach outperforms, not only SIFT, VGG16, and MobileNetV2 when used separately, but also surpasses state-of-the-art remote sensing image classification approaches. Another salient advantage of our approach is its robust applicability in scenarios with limited labeled data - a prevalent challenge in remote sensing image classification.

关键词： Perturbation methods Urban planning Transfer learning Land surface Transforms Propulsion Satellite images Environmental monitoring Remote sensing Image classification

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Deep Neural Network Evaluation for Emotion Recognition through Facial Expressions Analysis

Deep Neural Network Evaluation for Emotion Recognition throu...

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international conference on Electrical engineering, Computing Science and Automatic Control (CCE)

作者： Nirvana C. Amnarán-Orteza Juan A. Ramírez-Quintana Mario I. Chacón-Murguia Alma D. Corral-Saenz PVRLab Tecnológico Nacional de México / 1. T. Chihuahua Chihuahua México

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

Emotion recognition through facial expressions is paramount in marketing, healthcare, affective computation, and security applications. This research evaluates Deep Neural Network architectures for emotion recognition based on facial expression images and discrete emotion classification. The datasets used are JAFFE and a custom dataset. The selected Deep Neural Networks are VGG16, InceptionV3, ResNet50, Transformer model, VGG16 with LstM and VGG16 with a Transformer. The Networks use transfer learning with pre- trained ImageNet weights. Results show that VGG16 outperforms other models in accuracy for both datasets, while hybrid models like VGG16 with Transformer show lower accuracy. These results indicate that further fine-tuning and exploration of these architectures are necessary. Investigating different methods to combine the strengths of each architecture could lead to better outcomes. This research highlights the strengths and limitations of each architecture, guiding future advancements in emotion recognition technology.

关键词： Emotion recognition Accuracy Computational modeling Computer architecture Transformers data models Long short term memory Tuning Residual neural networks Overfitting

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An Overview Of The Latest Machine learning Trends In Short-Term Load Forecasting

An Overview Of The Latest Machine Learning Trends In Short-T...

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international conference on Environment and Electrical engineering (EEEIC)

作者： Pedro M.R. Bento Jose A.N. Pombo Silvio J.P.S. Mariano Maria R.A. Calado IT-Instituto de Telecomunicacoes University of Beira Interior Covilha Portugal

ISBN: (数字)9781665485371

ISBN: (纸本)9781665485388

The role of electricity in our daily lives has been steadily growing, and the socioeconomic data shows an indisputable link between the flourishing of modern societies and the expansion of its electric power systems infrastructure. The 21st century brought an increased awareness of global warming as a serious threat, which is deeply rooted in the prolonged greenhouse emissions. This prompted policy makers and societies to adopt several transformations that, in turn, result in a continuously challenging environment. In face of this scenario, short-term load forecasting (stLF) continues to be a crucial task for a better decision-making, not only for the System Operator, but also for market participants, distributors, and retailers. In addition, the proposal of more disaggregated load forecasting has been a new spark to this field, much due to the progress of smart-grids projects, where the implementation of self-consumption, load-scheduling and demand-response requires an estimated knowledge of the load consumption. The stLF literature offers a comprehensive variety of different methods to perform the prediction task. Currently, machine-learning (ML) based methods are the predominant choice to build forecasters, primarily due to their capability to deal with complex load patterns. This study aims to characterize this rich framework, by briefly delving into the working principle, architecture, and other performance issues, that help contextualize the cherry-picked influential contributions. These comprise the main ML-based stLF methods. A discussion of the strengths and weaknesses of each method is provided in the end, resulting in a useful overview and guidance for future research work.

关键词： Training Load forecasting Scalability Decision making Market research Feature extraction Smart grids

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Apnea/Hypopnea Detection Using Modified YOLOv8n Model on Non-EEG Signals

Apnea/Hypopnea Detection Using Modified YOLOv8n Model on Non...

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international conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Denphum Aphimeteetamrong Phunsak Thiennviboon Soraaut Intarawichian Witaya Sungkarat Jiraporn Laothamatas Dept. of Elect. Engineering Kasetsart University Bangkok Thailand Fac. of Medicine Ramathibodi Hospital Mahidol University Bangkok Thailand Fac. of Health Sci. Technology Chulabhorn Royal Academy Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

Sleep apnea is a sleep breathing disorder, causing ineffective sleep, that may lead to serious problems. Severity of sleep apnea is determined by an apnea-hypopnea index (AHI) that is usually obtained manually with both labor intensive and time consuming. Additionally, while the popularity of home sleep testing (Hst) is increasing, many Hst devices still lack EEG monitoring. Thus, our goal is to develop a machine learning model for detecting apnea and hypopnea events using 3 non-EEG signals to determine a severity level of sleep apnea for a full overnight record. We propose a modified YOLOv8n model for this task. The proposed model was trained, validated, and tested using 2 datasets including 2,056 and 23 full overnight records from the Multi-Ethnic study of Atherosclerosis (MESA) and the st. Vincent's University Hospital/University College Dublin Sleep Apnea (UCD), respectively. Testing performances of the proposed model were evaluated using 15% record-based data splitting for MESA and 23-fold cross validation for UCD. By comparing with selected existing models, the proposed model achieves reasonably good sample-based testing performances. To assess more realistic testing performances, the severity levels of sleep apnea were predicted using the detected apnea/hypopnea events with and without groundtruth total sleep times (Tsts). The results show that the proposed model provides severity-level (event-based) performances substantially better than the existing model. The proposed model also achieves moderate to substantial agreement with the groundtruth severity levels where the Cohen's kappa coefficients are between 0.61 and 0.81.

关键词： Training Atmospheric modeling Machine learning Brain modeling Sleep apnea Telecommunications Task analysis

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Optimizing the Energy Consumption Level in LoRaWan Networks

Optimizing the Energy Consumption Level in LoRaWan Networks

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Information Technology (ACIT)

作者： Ala’ Khalifeh Khaled Aldahdouh Sahel Alouneh School of Elec. Eng. and IT German Jordanian University Amman Jordan College of Engineering School of Elec. Eng. and IT Al Ain University German Jordanian University Abu Dhabi UAE

ISBN: (数字)9781728188553

ISBN: (纸本)9781728188560

Long range wide area network (LoRaWAN) is a type of Low Power Wide Area (LPWA) technologies, which is designed to support long-range communication with low data rates and low power consumption requirements. LoRaWAN consists of many nodes that sense the environment and send their measurements to the gateway, which in turn directs the received data to the network server to process the collected data. Each one of these nodes is configured using five transmission parameters, (the spreading factor (SF), center frequency (CF), bandwidth (BW), coding rate (CR), and transmission power (TP). The optimal selection of the values plays a vital role in providing an efficient and energy preserving network implementation. In this paper, an algorithm that selects the optimal transmission power for each node in the network depending on the receiver sensitivity and the path loss of the communication link between the node and the gateway is developed by utilizing the Reinforcement learning algorithm that is used to choose the best value of spreading factor. The simulation results of our proposed algorithm showed a significant decrease in the consumed power in the network compared with the other techniques in literature.

关键词： Multiaccess communication Receivers Power demand Logic gates Energy consumption Bandwidth Wireless sensor networks

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Fusion of strategic Queueing Theory and AI for Smart City Telecommunication System

Fusion of Strategic Queueing Theory and AI for Smart City Te...

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IEEE Internatonal conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Thi Thi Zin Aung Si Thu Moe Cho Nilar Phyo Pyke Tin Graduate School of Engineering University of Miyazaki Miyazaki Japan

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

This paper explores the innovative intersection of queueing theory and artificial intelligence (AI), addressing emerging challenges for Smart City Telecommunication System. We propose a novel approach that integrates queueing theory with AI methodologies, particularly artificial neural networks (ANNs), to optimize strategic parameters in Markov Decision Process (MDP) problems in communication systems. These parameters include server numbers, customer waiting times, queue lengths, and other critical metrics. Our fusion approach demonstrates high efficacy and indicates significant advancements in applying machine learning to complex queueing theory issues. The study underscores the practical applications of this integration across various domains. We provide real-world examples illustrating the strategic use of AI-enhanced queueing models in improving user experiences and optimizing system efficiencies. Our discussions cover the benefits of intelligent resource allocation, dynamic load balancing, and adaptive service time optimization. We extend our exploration through specific case studies demonstrating the efficacy of this integration in industries such as telecommunications, healthcare, and transportation. For instance, in telecommunications, AI-driven queue management systems can dynamically allocate bandwidth to ensure optimal service quality. In healthcare, queueing models enhanced by AI can improve patient flow and reduce wait times, potentially leading to better healthcare outcomes. In this paper, we investigate the application of deep learning models to queueing theory, specifically focusing on [mention specific problem or application, e.g., “forecasting queue lengths in real-time systems”]. We implement and evaluate several deep learning architectures to determine their effectiveness in modeling and predicting queue dynamics. The paper concludes by emphasizing the necessity for continued interdisciplinary research, encouraging collaboration between AI expert

关键词： Industries Adaptation models Smart cities Collaboration Predictive models Telecommunications Artificial intelligence Queueing analysis Optimization Load modeling

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TransFiLM: An Efficient and Lightweight Audio Enhancement Network for Low-Cost Wearable Sensors

TransFiLM: An Efficient and Lightweight Audio Enhancement Ne...

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IEEE Internatonal conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Shilin Chen Jianhao Weng Shicong Hong Yanbo He Yongpan Zou Kaishun Wu College of Computer Science and Software Engineering Shenzhen University Information Hub Hong Kong University of Science and Technology (Guangzhou)

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Wearable devices such as headphones are increasingly popular in people's lives, and there is an increasing focus on how to achieve continuous and reliable information input using these devices. However, due to constraints in computing power, low power consumption, and low operating frequencies, such devices often record and transmit signals at lower sampling rates, the resultant lower-quality signals often have catastrophic implications for system performance. Efficient real-time conversion of low-resolution speech signals to full-resolution high-quality signals using low-cost wearable sensors on edge devices is a challenging research endeavor. To address this, this paper designs TransFiLM, a mobile deep learning network. It allows users to obtain full-resolution high-quality audio signals using low-cost wearable sensors on edge devices. TransFiLM integrates residual learning and super-resolution networks and employs effective signal processing strategies to achieve audio upscaling and noise reduction, significantly improving audio quality. We implement a prototype on commercial devices and conduct a series of experiments to evaluate its performance. Using signal-to-noise ratio (SNR) and log-spectral distance (LSD) as evaluation metrics, TransFiLM exhibits superior performance compared to other time-domain methods in cross-user, cross-corpus, and cross-noise environment testing. Additionally, our TransFiLM network handles 8192 samples with a response time of 181 ms, which meets the requirement to run in real-time on edge devices.

关键词： Performance evaluation Deep learning Noise reduction Real-time systems Time factors Wearable devices Time-domain analysis Wearable sensors intelligent sensors Signal to noise ratio

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FedLoRa: IoT Spectrum Sensing Through Fast and Energy-Efficient Federated learning in LoRa Networks

FedLoRa: IoT Spectrum Sensing Through Fast and Energy-Effici...

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IEEE Internatonal conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Fabio Busacca stefano Mangione Giovanni Neglia Ilenia Tinnirello Sergio Palazzo Francesco Restuccia Department of Electrical Electronics and Computer Engineering (DIEEI) University of Catania Italy Department of Engineering University of Palermo Italy Inria Université Côte d'Azur France Institute for the Wireless Internet of Things Northeastern University Boston USA

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Effective spectrum sensing is quintessential to decrease spectrum congestion across time, space and frequency in Internet of Things (IoT) networks. To circumvent the severe bandwidth constraints of IoT networks, federated machine learning (FML) can be used, but it is still unclear whether FML can be successfully performed in resource-constrained bandwidth-limited IoT networks. In this paper, we demonstrate for the first time that FML can tolerate losses up to a certain percentage and still converge. Then, we leverage this key result to design FedLoRa, an optimization networks for LoRa that is (i) fast, as it reduces the FML round time in comparison with other resource allocation schemes; (ii) energy-efficient, as the time reduction does not imply a higher energy consumption. The key idea is to balance the network load over the available spreading factors, and to exploit sequential polling of nodes to maximize the number of simultaneous non-interfering transmissions, leading to a shorter FML round time. As the problem is NP-Hard, we provide an approximation algorithm. We evaluate the performance of FedLoRa through experimental evaluation on the Colosseum channel emulator, as well as with real-world data collection with off-the-shelf LoRa devices in an 5kmx5km urban setting in Portland, Maine. Our results show that FedLoRa reduces the round time by up to about 35%, as compared to the baselines.

关键词： Energy consumption Federated learning Wireless networks LoRa Approximation algorithms Energy efficiency Sensors Resource management Internet of Things Optimization

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An I/O Request Packet (IRP) Driven Effective Ransomware Detection Scheme using Artificial Neural Network

An I/O Request Packet (IRP) Driven Effective Ransomware Dete...

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IEEE international conference on Information Reuse and Integration (IRI)

作者： Md. Ahsan Ayub Andrea Continella Ambareen Siraj Department of Computer Science Tennessee Tech University Cookeville USA Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede NL

ISBN: (数字)9781728110547

ISBN: (纸本)9781728110554

In recent times, there has been a global surge of ransomware attacks targeted at industries of various types and sizes from retail to critical infrastructure. Ransomware researchers are constantly coming across new kinds of ransomware samples every day and discovering novel ransomware families out in the wild. To mitigate this ever-growing menace, academia and industry-based security researchers have been utilizing unique ways to defend against this type of cyber-attacks. I/O Request Packet (IRP), a low-level file system I/O log, is a newly found research paradigm for defense against ransomware that is being explored frequently. As such in this study, to learn granular level, actionable insights of ransomware behavior, we analyze the IRP logs of 272 ransomware samples belonging to 18 different ransomware families captured during individual execution. We further our analysis by building an effective Artificial Neural Network (ANN) structure for successful ransomware detection by learning the underlying patterns of the IRP logs. We evaluate the ANN model with three different experimental settings to prove the effectiveness of our approach. The model demonstrates outstanding performance in terms of accuracy, precision score, recall score, and F 1 score, i.e., in the range of 99.7%±0.2%.

关键词： Ransomware Cryptography Neural networks Microsoft Windows Kernel data collection

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Artificial Intelligence-Driven Algorithms for Precision Forecasting in Electricity Pricing

Artificial Intelligence-Driven Algorithms for Precision Fore...

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IEEE India conference (INDICON)

作者： Ashis Pradhan Rekha Pilliadugula Nareddy Nageswara Reddy Jagadish Kumar Bokam Hanumantha Rao Bokkisam Department of Electrical Engineering National Institute of Technology Rourkela Odisha India department of Medicine (Nanomaterials) University of Atacama Chile Department of EEE VNRVJIET Telangana India Department of Electrical Electronics and Communication Engineering GITAM University Andhra Pradesh India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Electricity price forecasting is essential for ensuring the efficient and cost-effective operation of the power industry. Accurate and precise prediction of electricity prices is critical in the electricity market due to the inherent challenges posed by factors such as high volatility, seasonality, calendar effects, and non-linearity. This paper presents four deep learning-based models—Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LstM), and a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN- LstM)—to forecast electricity prices in the Nord Pool Spot Market. The approach utilizes historical electricity price data, making it a univariate time-series forecasting method. The performance of these models is assessed using various accuracy metrics and visualized through box plots. The simulation results indicate that the CNN-LstM model consistently outperforms the other three models across most evaluation metrics.

关键词： Measurement Solid modeling Accuracy Recurrent neural networks Electricity Pricing Predictive models Convolutional neural networks Forecasting Long short term memory

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