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Detecting Persons in River Quarry Operations: Aerial Surveillance for Flood Pre-Disaster Monitoring

Detecting Persons in River Quarry Operations: Aerial Surveil...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Rochelle F. Madulara Earl Ryan M. Aleluya Jeanette C. Pao Steve E. Clar Francis Jann A. Alagon Carl John O. Salaan Maria Fe P. Bahinting Department of Computer Applications College of Computer Studies Department of Computer Engineering and Mechatronics College of Engineering Department of Electrical Engineering College of Engineering Graduate School of Engineering Mindanao State University-Iligan Institute of Technology Iligan City Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

During the flood disaster preparedness procedure, warning dissemination followed by response decision-making and implementation is usually time-consuming for disaster preparedness teams. Some inaccessible areas that are also prone to flooding is a crucial challenge for the disaster preparedness team in disseminating the early warnings, which may result in endangering the lives of people living in those areas. Moreover, the disaster preparedness team is dedicated to overseeing and managing river quarrying activities, as these are areas where workers or individuals are at high risk of flooding disasters. In this study, an Unmanned Aerial Vehicle (UAV) with Person Detection System that can assist the disaster preparedness team for flood pre-disaster monitoring was introduced. The study investigated the performance of advanced deep learning models, particularly on You Only Look Once, for accurate person detection utilizing UAV imagery focusing in a river-quarry setting. A novel approach to data collection was also presented, specialized datasets were created for capturing river quarry-specific scenarios and used this in facilitating realistic model training. Two models were evaluated in this study, the YOLOv5 model and YOLOv8 model, through their precision, recall, F1-score, and mAP performance. The trained YOLOv5 model showed better performance with 97.70% precision, 94.10% recall, 95.10% F1-Score, and 64.50% mAP than the YOLOv8 model. The significant difference of 2.00% for precision, 3.80% for recall, 3.00% for F1-score, and 0.40% for mAP, between YOLOv5 model and YOLOv8 model, provided beneficial findings for optimizing model selection for person detection using UAV imagery considering other areas needed for pre-disaster monitoring.

关键词： YOLO Training Disasters Surveillance Autonomous aerial vehicles Mathematical models Rivers

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Malware Classification using LSTM-CF Framework for Disk Forensic Analysis 2

Malware Classification using LSTM-CF Framework for Disk Fore...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Thirupathi, Chaithanyaka Yeddeli Sundaram, Jawahar Yeo, Kheng Cher Khan, Shujahat Ali Pritika Sellappan, Devaraju Energy and Resources Institute Charles Darwin University Faculty of Science and Technology Darwin Australia Christ Academy Institute for Advanced Studies Department of Computer Science and Applications Bengluru India Charles Darwin University Faculty of Science and Technology Darwin Australia School of Computing Science and Engineering VIT Bhopal University Bhopal India

ISBN: (纸本)9798350383652

In the current era, digital forensic investigators need specialized tools to extract digital footprints from hard disks. The number of crimes is rising dramatically. This has resulted in an increased number of unresolved cybercrime cases including malicious software, hacking and cyberfraud. Disk forensics or disk investigation is a massive task. It takes weeks to collect the traces of the 1TB hard drive and analyze them. It is a challenging task to detect reliable evidence because of the worldwide use and advancement of digital communication technologies. By using automated tools, only predictable areas of the disk are often investigated, and as a result, latent evidence in the hidden area might be missed. In this paper, we present Chaithu's Framework (CF) that gives us a clear picture of the steps to follow and implement a machine learning algorithm which is able to predict whether the request is related to malware or not by analyzing the large world Microsoft Malware Prediction dataset collected from Kaggle. It consists of a total of 7.8 million data samples with 84 features collected in real time from Windows 7,8,9 and 10 systems. Using predictive machine learning algorithms, frauds can be detected automatically and autonomous actions can be taken to prevent them. © 2024 IEEE.

关键词： Hard disk storage

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IOT Design and Water Monitoring of an Aquaponic System

IOT Design and Water Monitoring of an Aquaponic System

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Signal, Control and Communication (SCC), International Conference on

作者： Takrouni Hedfi Asma Houmia Mohamed Laabidi Omri Kaouther Electrical Engineering Department Higher Institute of Technological Studies Tozeur Tunisia Department of Electrical and Computer Network and Engineering University of Jeddah Saoudi Arabia

ISBN: (数字)9798350326390

ISBN: (纸本)9798350326406

The concept of smart agriculture involves integrating fish and plants in the same environment. This paper showcases an aquaponics system that utilizes an Arduino board, sensors, and hardware devices for its design and monitoring. The system is a closed-loop ecosystem that combines fish farming (aquaculture) and soilless plant growing (hydroponics). This innovative approach is beneficial for both the aquaculture and horticultural sectors as it enables the permanent reuse of water for breeding and reduces the use of chemical inputs in crop production management. The aquaponic system is self-regulating and can adjust itself when the pH, temperature, and water level sensors indicate that they are out of range. This is accomplished by combining the concept of embedded systems with the IOT (Internet of Things).

关键词： Temperature sensors Temperature measurement Smart agriculture Temperature distribution Hydroponics Fish Sensor systems

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A Robust Federated Learning Approach for Combating Attacks Against IoT Systems Under Non-IID Challenges

A Robust Federated Learning Approach for Combating Attacks A...

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International Conference on Smart Communications and Networking (SmartNets)

作者： Eyad Gad Zubair Md Fadlullah Mostafa M. Fouda Department of Computer Science University of Western Ontario London ON Canada Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

In the context of the growing proliferation of user devices and the concurrent surge in data volumes, the complex-ities arising from the substantial increase in data have posed formidable challenges to conventional machine learning model training. Particularly, this is evident within resource-constrained and security-sensitive environments such as those encountered in networks associated with the Internet of Things (IoT), Federated Learning has emerged as a promising remedy to these challenges by decentralizing model training to edge devices or parties, effectively addressing privacy concerns and resource limitations. Nevertheless, the presence of statistical heterogeneity in non-Independently and Identically Distributed (non-IID) data across different parties poses a significant hurdle to the effectiveness of FL. Many FL approaches have been proposed to enhance learning effectiveness under statistical heterogeneity. However, prior studies have uncovered a gap in the existing research land-scape, particularly in the absence of a comprehensive comparison between federated methods addressing statistical heterogeneity in detecting loT attacks. In this research endeavor, we delve into the exploration of FL algorithms, specifically FedAvg, FedProx, and Scaffold, under different data distributions. Our focus is on achieving a comprehensive understanding of and addressing the challenges posed by statistical heterogeneity. In this study, We classify large-scale loT attacks by utilizing the CICloT2023 dataset. Through meticulous analysis and experimentation, our objective is to illuminate the performance nuances of these FL methods, providing valuable insights for researchers and practitioners in the domain.

关键词： Training Accuracy Federated learning Scalability Computational modeling Robustness Internet of Things

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Eggplant Leaf Semantic Segmentation in Aerial Imagery for Precision Agriculture

Eggplant Leaf Semantic Segmentation in Aerial Imagery for Pr...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Nouran M. Usman Earl Ryan M. Aleluya Steve E. Clar Francis Jann A. Alagon Carl John O. Salaan Maria Fe P. Bahinting Department of Computer Applications College of Computer Studies Department of Computer Engineering and Mechatronics College of Engineering Department of Electrical Engineering College of Engineering Graduate School of Engineering Mindanao State University-Iligan Institute of Technology Iligan City Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

Eggplant is one of the prominent vegetable crops in the Philippines. Traditional farming practices, like agrochemical spraying, often lack real-time, high-resolution data crucial for informed decisions. Monitoring vegetation indices is vital for crop growth assessment, health monitoring, and precision agriculture. However, optimizing agrochemical spraying for specific crop needs remains a challenge. Unmanned aerial vehicles (UAV s) offer a transformative solution. RGB cameras, designed for visual imagery, may not provide accurate vegetation index measurements. computer vision, through leaf detection, plays a crucial role in assessing crop health. This study aims to distinguish eggplant leaves from their surroundings. Subsequently, the authors compared two open-source segmentation models, namely U-Net and FPN. Two pre-trained encoders, VGG16 and VGG19, were used in both models to further evaluate their performance. Using a drone-mounted RGB camera, the authors gathered images of mature eggplant leaves. The experimental findings demonstrate the effectiveness of the VGGl6-based FPN architecture for real-time leaf segmentation tasks as it acquires 97.24 % and 96.73 % accuracy with a latency time of 31.5 ms and 32.6 ms, on 0.5 and 0.75 threshold values. The study recommends FPN with VGG16 for its accurate and low-latency performance in time-sensitive leaf segmentation applications.

关键词： Precision agriculture Accuracy Semantic segmentation Crops Vegetation mapping Spraying Autonomous aerial vehicles

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Ground Robot Detection for Collaborative UAV-UGV Alignment and Retrieval Operations

Ground Robot Detection for Collaborative UAV-UGV Alignment a...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Jamaica Mae L. Pepito Earl Ryan M. Aleluya John Mel A. Bolaybolay Francis Jann A. Alagon Steve E. Clar Jeanette C. Pao Carl John O. Salaan Sherwin A. Guirnaldo Maria Fe P. Bahinting Department of Computer Applications College of Computer Studies Department of Computer Engineering and Mechatronics College of Engineering Department of Electrical Engineering College of Engineering Graduate School of Engineering Mindanao State University-Iligan Institute of Technology Iligan City Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

Volcanologists face considerable challenges in ex-ploring and monitoring harsh and dangerous environments like volcanoes. To gather essential data, they use robots, such as drones and mobile robots. However, drones have limited capacity and flight time, while mobile robots face mobility constraints. Combining drones and mobile robots has emerged as an inno-vative technology for long-range monitoring of volcanoes. This approach allows for fast deployment of ground robots using transport drones. However, the retrieval operation of the ground robot is challenging due to inaccurate drone positioning. A visual technique for detecting and aligning the ground robot is crucial during this procedure. In this study, the detection of the ground robot utilizing a drone was investigated. The drone-based detection approach involved (i) capturing ground robot images from various conditions, (ii) training a robust detection model, and (iii) assessing its performance. Furthermore, this study employed the You Only Look Once (YOLOv8) architecture for ground robot detection. Three models were investigated: the plate detection model, the ground robot detection model, and the model for both ground robot and plate detection. The trained ground robot detection model achieved a robust detection performance garnering the precision of 98.9% with a respectable recall of 100%, F1 score of 99.94%, and 98.6% mean average precision, compared to the other two trained models. The trained detection model presented a practical approach, which produced good performance and could predict and segment the ground robot during the alignment procedure for the retrieval operation by the drone.

关键词： Training YOLO Collaboration Predictive models Volcanoes Mobile robots Robots

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EADTC: An Approach to Interpretable and Accurate Crime Prediction

EADTC: An Approach to Interpretable and Accurate Crime Predi...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Ma, Yujunrong Nakamura, Kiminori Lee, Eung-Joo Bhattacharyya, Shuvra S. University of Maryland Dept. of Electrical and Computer Engineering United States University of Maryland Dept. of Criminology and Criminal Justice United States Mgh and Harvard Medical School Camca Dept. of Radiology United States Institute for Advanced Computer Studies Maryland Crime Research and Innovation Center University of Maryland Dept. of Electrical and Computer Engineering United States

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Machine learning applications related to high-stakes decisions are often surrounded by significant amounts of controversy. This has led to increasing interest in interpretable machine learning models. A well-known class of interpretable models is that of decision trees (DTs), which mirror a common strategy used by humans to arrive at solutions through a series of well-defined decisions. However, much of previous research on DTs for criminal justice predictions has focused primarily on collections (ensembles) of DTs whose results are aggregated together. Such DT ensembles are used to help improve accuracy;however, their increased complexity and deviation from human decision-making processes makes them much less interpretable compared to single-DT approaches. In this paper, we present a new DT model for criminal recidivism prediction that is designed with high interpretability, accuracy, and fairness as core objectives. The interpretability of the model stems from its formulation in terms of a single DT structure, while accuracy is achieved through an intensive optimization process of DT parameters that is carried out using a novel evolutionary algorithm. Through extensive experiments, we analyze the performance of our proposed EADTC (Evolutionary Algorithm Decision Tree for Crime prediction) method on relevant datasets. Our experiments show that the EADTC approach achieves competitive accuracy and fairness with respect to state-of-the-art ensemble DT models, while achieving higher interpretability due to the simpler, single-DT structure. © 2022 IEEE.

关键词： Decision trees

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Remote Patient Monitoring System with Wearable IoT Devices and Biosensors for Vital Signs 3

Remote Patient Monitoring System with Wearable IoT Devices a...

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3rd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2025

作者： Naveen, S. Sivakumar, T.B. Supreeth, B.R. Niranjana, R. Vishwa Priya, V. Saranya, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences-SIMATS Saveetha University Department of Mechanical Engineering Tamil Nadu Chennai India School of Computing Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Cse Tamil Nadu Chennai India Oxford Degree and Pg College Department of Computer Science Sir. M. Visveshwaraiah Layout Ullal Bangalore India Velammal College of Engineering and Technology Department of Cse Tamil Nadu Madurai India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Information Technology Tamil Nadu Chennai India Kpr Institute of Engineering and Technology Department of English Tamil Nadu Coimbatore India

ISBN: (纸本)9798331543358

Due to infrequent patient monitoring and the reactive character of the traditional healthcare system, delayed diagnoses and inadequate management of chronic diseases are common outcomes. The study presents a solution to these drawbacks: a Remote Patient Monitoring (RPM) system that combines advanced biosensors with wearable IoT devices to track vital signs continuously. By ensuring proactive healthcare through real-time data collecting and analysis, the entire system overcomes the drawbacks of conventional techniques. Early anomaly detection and timely medical interventions are made possible by the method's utilization of scalable cloud infrastructure and advanced machine learning (ML) methods, such as CNNs (Convolutional Neural Networks) and Long Short-Term Memory (LSTMs). The proposed system achieves a 90% early detection rate, 95% sensitivity, 97% specificity in anomaly detection, and 92% patient satisfaction, which is a substantial improvement over existing techniques, according to the results. It also exhibits better vital sign measurement accuracy than existing systems, indicating its potential to completely transform patient care through better results and economical resource utilization. © 2025 IEEE.

关键词： Remote patient monitoring

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Non-Hermitian Band Structure of Photonic Temporal Crystals

Non-Hermitian Band Structure of Photonic Temporal Crystals

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Park, Jagang Cho, Hyukjoon Lee, Seojoo Lee, Kyungmin Lee, Kanghee Park, Hee Chul Ryu, Jung-Wan Park, Namkyoo Jeon, Sanggeun Min, Bumki Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Centerfor Theoretical Physics of Complex Systems Institute of Basic Science Daejeon34126 Korea Republic of Department ofElectrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of School of Electrical Engineering Korea University Seoul02841 Korea Republic of

We experimentally confirm the non-Hermitian band structures of a photonic temporal crystal in the microwave. These non-Hermitian band structures are shown to be two measurable distinct subsets of complex eigenfrequenc... 详细信息

ISBN: (纸本)9781957171258

关键词： Band structure

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Energy Consumption Prediction for Home Appliances with Recurrent Neural Networks 4

Energy Consumption Prediction for Home Appliances with Recur...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Aancy, H. Mickle Mary, S. Anitha Janet Manikumar, T. Saravanan, G. Mohanavel, V. Gupta, Rahul Dev Panimalar Engineering College Department of Management Studies Tamil Nadu Chennai600123 India Noorul Islam Centre for Higher Education Department of Electrical and Electronics Engineering Tamil Nadu Kumaracoil629180 India Kalasalingam Academy of Research and Education Deemed to be University Department of Computer Science and Engineering Tamil Nadu 626126 India KPR Institute of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore641407 India Centre for Materials Engineering and Regenerative Medicine Bharath Institute of Higher Education and Research Tamil Nadu Chennai600073 India Department of Mechanical Engineering Haryana 133207 India

ISBN: (纸本)9798350385793

Efficient energy management requires a thorough understanding of individual appliance energy consumption in both residential and business environments. Because of the growing number of Internet of Things (loT) devices in smart homes, household appliances are a significant source of total energy consumption. Commonly used equipment such as heating systems, washing machines, and vacuum cleaners have a substantial impact on daily energy consumption. Significant energy savings can only be realized by careful management and forecasting of these appliances' energy consumption. This research employs Recurrent Neural Network (RNN) models for energy forecasting of housing appliances, with Long Short-Term Memory (LSTM) and a hybrid model known as Bi-LSTM-Attention Network. A Kaggle dataset with 28 features is used in this study, which is then reduced to 17 using a correlation plot. The processed data is used in both the training and testing phases of the RNN models. Many metrics are used to evaluate the performance of the models, including R-squared (R2), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The Bi- LSTM-Attention Network achieves excellent performance, with training and testing R2 values of 0.9978 and 0.9835, respectively. In addition, lower MSE and RMSE values demonstrate that Bi-LSTM-Attention Network constantly outperforms LSTM. The findings show that the proposed Bi-LSTM-Attention Network is capable of accurately predicting the amount of energy household appliances will consume. © 2024 IEEE.

关键词： Long short-term memory

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