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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Rahman, Md Shahidur Ahamed, Asif Pranto, Md Nuruzzaman Islam, Md Ariful Al Masum, Abdullah Al-Sakib, Abdullah Rahman, S.M. Arafat Haque, Rezaul Rahman, Shafiur Westcliff University Dept. of Engineering Management CA92614 United States Westcliff University College of Technology and Engineering CA92614 United States International American University Dept. of Business Administration CA90010 United States Westcliff University Dept. of Information Technology CA92614 United States East West University Dept. of Computer Science and Engineering Dhaka1212 Bangladesh Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331534400

Cucumber farming plays a crucial role in Bangladesh's agricultural economy, significantly contributing to vegetable production. However, diseases like Downy Mildew, Bacterial Wilt, Anthracnose, and Belly Rot threaten crop quality and lead to financial losses. Effective management of these diseases is crucial for maintaining high crop yields and ensuring economic stability. Current disease identification methods are labor-intensive, costly, and dependent on expert knowledge. To address these challenges, this study presents an automated disease categorization system using advanced transfer learning models, including MobileNet, ShuffleNet, InceptionResNet-v2, AlexNet, and Ensemble Transfer Learning (ETL). Utilizing a dataset of 8,000 well-annotated images, enhanced through preprocessing techniques like GLRM feature extraction and InfoMAX-GAN feature selection, the ETL model achieved a remarkable accuracy of 99.79% and an F1-score of 99.78%. We also developed a user-friendly web tool for real-time diagnostics, providing invaluable support to farmers and agricultural specialists. This tool enables rapid disease detection, leading to increased productivity and a transformative approach to cucumber disease management. By reducing the reliance on chemical interventions, it promotes sustainable farming practices and safeguards human and animal health. © 2024 IEEE.

关键词： Feature Selection

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Multi-granularity Coal Mine Risk Conceptual Prediction Based on Time-varying Cloud Model

Multi-granularity Coal Mine Risk Conceptual Prediction Based...

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2023 International Conference on Cyber Security, Artificial Intelligence, and Digital Economy, CSAIDE 2023

作者： Dai, Jin Hu, Sunling Zhang, Lei Dept. of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China Dept. of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781510666597

The big data of coal mine was characterized by large scale, many influencing factors and weak correlation. The existing big data mining based on quantitative data analysis usually adopts fixed framework processing, which is easy to fall into the dilemma of overfitting and cannot acquire elastic knowledge across layers. From the perspective of coal mine monitoring and management, a multi-granularity time-varying cloud model is proposed based on a multi-granularity semantic transformation model and a grey prediction model to achieve the conceptual prediction of coal mine gas concentration risk. The new model first converts the gas concentration data into semantic concepts using a semantic transformation model, then processes the sequence of semantic concept sets for randomness and oscillation, and builds a grey prediction model for them, which is used to project future semantic concepts. The new model is compared with the existing model, and the experimental results show that the new method can not only predict coal mine risk concepts at different granularities, but also has high prediction accuracy. © 2023 SPIE.

关键词： Coal mines

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Unveiling The Arsenal of User Data Protection Tools and Practices

Unveiling The Arsenal of User Data Protection Tools and Prac...

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Humanitarian Technology Conference (KHI-HTC), IEEE Karachi Section

作者： Syed Moiz Abdul Ahad Abro Mansoor Ebrahim Abdul Basit Abro Dept. of Computer Science Iqra University Main Campus Karachi Pakistan Dept. of Software Engineering Iqra University Main Campus Karachi Pakistan Dept. of Computer Science Indus University Karachi Pakistan

In our rapidly evolving digital landscape, the imperative of safeguarding personal data has surged in significance. As lives increasingly intertwine with digital technologies, personal information has grown markedly, parallel to the escalating threats posed by cyberattacks and data breaches. This research paper provides a comprehensive exploration of data security and privacy, aiming to impart a profound understanding of the intrinsic value of data. It meticulously examines essential methodologies such as encryption, access control, multifactor authentication, and systems, all of which collectively form the bedrock of a resilient security framework. Additionally, this study delves into the intricate regulatory landscape that governs data protection, placing particular emphasis on well-established frameworks such as the General Data Protection Regulation and the Health Insurance Portability and Accountability Act. It highlights emergent trends in data security, including the integration of artificial intelligence and machine learning for threat detection, while also addressing the distinctive challenges posed by the proliferation of Internet of Things (IoT) devices. In conclusion, this paper underscores the paramount importance of personal data protection in our digital age, offering invaluable insights and guidance for organizations and individuals endeavoring to bolster their defenses and uphold data integrity within our interconnected world.

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Deep Reinforcement Learning Algorithms for Option Hedging

arXiv

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arXiv 2025年

作者： Neagu, Andrei Godin, Frédéric Kosseim, Leila Dept. of Computer Science and Software Engineering Concordia University Montréal Canada Dept. of Mathematics & Statistics Concordia University Montréal Canada

Dynamic hedging is a financial strategy that consists in periodically transacting one or multiple financial assets to offset the risk associated with a correlated liability. Deep Reinforcement Learning (DRL) algorithms have been used to find optimal solutions to dynamic hedging problems by framing them as sequential decision-making problems. However, most previous work assesses the performance of only one or two DRL algorithms, making an objective comparison across algorithms difficult. In this paper, we compare the performance of eight DRL algorithms in the context of dynamic hedging;Monte Carlo Policy Gradient (MCPG), Proximal Policy Optimization (PPO), along with four variants of Deep Q-Learning (DQL) and two variants of Deep Deterministic Policy Gradient (DDPG). Two of these variants represent a novel application to the task of dynamic hedging. In our experiments, we use the Black-Scholes delta hedge as a baseline and simulate the dataset using a GJR-GARCH(1,1) model. Results show that MCPG, followed by PPO, obtain the best performance in terms of the root semi-quadratic penalty. Moreover, MCPG is the only algorithm to outperform the Black-Scholes delta hedge baseline with the allotted computational budget, possibly due to the sparsity of rewards in our environment. © 2025, CC BY.

关键词： Learning algorithms

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Enhancing Energy Consumption of a Node in Wireless Sensor Networks Using Quick Response Code 3

Enhancing Energy Consumption of a Node in Wireless Sensor Ne...

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3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Nidhya, M.S. Veronica, Benita K. J. Jayakarthik, R. Parvathavardhini, K. Pmist Dept of Software Engineering Thanjavur India Mother Teresa University Dept of Computer Science Kodaiakanal India Vistas Dept of Computer Science Chennai India

ISBN: (纸本)9780738146270

Sending messages, retransmission of identical messages, and sensing information in the monitored environment can quickly reduce the energy consumption of a sensor node present in a wireless sensor network. Due to repeated delivery of the same message, the battery loses power and drops dead. Node closer to the sink node will quickly deplete its energy by forwarding the messages. Total network lifetime will also be decreased. To overcome this, this research work use Quick response code (QR) for message transmission and reject the transmission of the same message several times. © 2021 IEEE.

关键词： Energy utilization

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Spatial Mini Golf: Game-Based Spatial Intelligence Testing and Training Using VR and Non-VR Platforms

Spatial Mini Golf: Game-Based Spatial Intelligence Testing a...

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Patrick Oliver Andy Fong Zixuan Wang Burkhard C. Wünsche Dept. of Software Engineering University of Auckland Auckland New Zealand School of Computer Science University of Auckland Auckland New Zealand

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

Spatial intelligence is a key determinant of students' motivation and success in pursuing a STEM career. To improve students' participation in STEM subjects it is important to test and train spatial skills in an enjoyable and unobtrusive manner. We present Spatial Mini Golf, a game designed to test and enhance spatial intelligence, targeting four sub-skills: visual perception, mental rotation, spatial visualization, and visuospatial working memory. Two versions-VR and desktop-were compared. While VR increases engagement, it also presents control challenges that raise cognitive load and extend task completion times. Performance correlates with spatial intelligence, particularly in desktop environments, but VR reduces performance gaps among lower-ability participants. Our research suggest that both VR and desktop games are suitable for testing and training spatial reasoning skills. VR tools might be more accessible for users with low spatial skills if intuitive and easy-to-use interfaces are provided, but results might get affected by users' hand-eye coordination.

关键词： Training Visualization engineering profession Games Cognitive load Testing Visual perception

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Work in Progress: Congestion Control in mmWave Fluctuating Scenarios in 5G-A/6G

Work in Progress: Congestion Control in mmWave Fluctuating S...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Jorge Ignacio Sandoval Sandra Céspedes Department of Electrical Engineering University of Chile Santiago Chile Dept. of Computer Science & Software Eng Concordia University Montreal Canada

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

The development of the next generation of cellular networks, 5G-Advanced and 6G (5G-A/6G), offers higher speeds, sub-millisecond latency, and providing wider coverage, which are key to meeting the requirements of new and demanding applications. As such, there are several challenges in managing and leveraging this opportunity to ensure that channel conditions do not impact the application due to mobility and obstructions. 5G introduces the use of millimeter wave (mmWave), and the commercial deployment of this technology brings to light the issues related to propagation and mobility in high-frequency bands, and their impact on applications has begun to be studied by exploring the response of the congestion control algorithms (CCAs) in representative scenarios. However, the highly variable channel conditions in mmWave require that they be defined as fluctuating bandwidth scenarios. It is necessary to determine how the transport layer can take advantage of the maximum available capacity and not affect the applications, and whether the network promises are fulfilled. This work aims to design and evaluate a CCA capable of efficiently adapting to the fluctuating bandwidth of extreme condition scenarios of 5G-A/6G mobile networks and to compare it with the state-of-the-art algorithms. As a first result, a set of simulation-based evaluation using 5GLENA was discussed. A combination of CCAs and different radio link control (RLC) buffer sizes allows us to highlight the importance of defining appropriate metrics and including convergence times as part of the evaluation performance of the algorithms. Algorithms such as HighSpeed and New Reno are among the slowest to converge, while BBR is the fastest. The analysis and characterization of the algorithms will allow us to define the requirements for designing an Machine Learning (ML) based algorithm with optimal performance and better convergence times.

关键词： Measurement Machine learning algorithms Millimeter wave technology Bandwidth Machine learning Radio links Millimeter wave propagation Millimeter wave communication Next generation networking Convergence

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Navigating Adverse Weather: An Exploration of YOLOv8's Object Detection in Fog 10

Navigating Adverse Weather: An Exploration of YOLOv8's Objec...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Haris, Muhammad Iqbal, Mansoor Malik, Muhammad Osama Bokhari, Syed Ahmad Saleem Alahmari, Saad Azam, Farooq Institute for Insight - J Mack Robinson College of Business Georgia State University Georgia United States University of Wah Department of Computer Science Wah Cantt Pakistan University of Tulsa Department of Business Analytics Oklahoma United States Harvard Extension School Harvard University CambridgeMA02138 United States Northern Border University Department of Computer Science Arar Saudi Arabia Nust Dept. of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798331532895

Object detection in adverse weather conditions, such as dense fog, poses a critical challenge for computer vision systems in applications like autonomous driving, surveillance, and robotics. Image quality degradation due to light scattering and absorption diminishes the performance of even the most advanced models trained on clear-weather datasets. This paper uses the Foggy Cityscapes dataset to evaluate YOLOv8, a cutting-edge object detection model, under extreme foggy conditions. We utilize YOLOv8's sophisticated architecture, featuring the CSPDarknet backbone and Path Aggregation Network (PANet), for efficient feature extraction and multi-scale detection. Our approach incorporates image enhancement techniques and data augmentation to mitigate the effects of fog on feature visibility. We conduct a comprehensive performance assessment using metrics like mean average precision (mAP) precision-recall curves to evaluate the model's robustness and reliability. Additionally, we benchmark YOLOv8 against previous YOLO versions, detailing improvements and trade-offs in detection speed and accuracy. We also propose a hybrid framework combining multi-task learning to improve image quality and object detection in low-visibility scenarios. © 2024 IEEE.

关键词： Light scattering

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Blockchain-based Reputation System For IoT Data Ecosystem: A Utility Maximization Approach 19

Blockchain-based Reputation System For IoT Data Ecosystem: A...

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19th IEEE International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI, HONET 2022

作者： Khezr, Seyednima Yassine, Abdulsalam Benlamri, Rachid Lakehead University Dept. of Electrical and Computer Engineering Thunder BayON Canada Lakehead University Dept. of Software Engineering Thunder BayON Canada University of Doha for Science and Technology Dept. of Information Technology Doha Qatar

ISBN: (纸本)9781665461979

Recently, blockchain technology has been used to build private and secure internet of things (IoT) data markets and trading systems. Reputation of the trading parties is an important attribute that affect their profitability and trading prosperity. However, current reputation systems are prone to malicious manipulation. Such an issue can be avoided by registering all reputation scores in the blockchain which guarantees trans-parency, encryption, privacy, and security. To this end, this paper proposes a blockchain-based reputation system where market players can maximize their utility while engaged in the IoT data ecosystem. We also propose an adaptive learning mechanism that allows the data providers and consumers to enhance their reputation and review credibility scores. We provide extensive experiments to ensure our system satisfies the feasibility, usability, and availability properties. These properties are essential for functional deployment in real IoT data markets. © 2022 IEEE.

关键词： Blockchain

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XGSwap: eXtreme Gradient boosting Swap for Routing in NISQ Devices

arXiv

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arXiv 2024年

作者： Waring, Jean-Baptiste Pere, Christophe Le Beux, Sébastien Dept. of Electrical & Computer Engineering Concordia University MontréalQC Canada Dept. of Computer Science & Software Engineering INTRIQ Laval University QuébecQC Canada PINQ2

In the current landscape of noisy intermediate-scale quantum (NISQ) computing, the inherent noise presents significant challenges to achieving high-fidelity long-range entanglement. Furthermore, this challenge is amplified by the limited connectivity of current superconducting devices, necessitating state permutations to establish long-distance entanglement. Traditionally, graph methods are used to satisfy the coupling constraints of a given architecture by routing states along the shortest undirected path between qubits. In this work, we introduce a gradient boosting machine learning model to predict the fidelity of alternative–potentially longer–routing paths to improve fidelity. This model was trained on 4050 random CNOT gates ranging in length from 2 to 100+ qubits. The experiments were all executed on ibm_quebec, a 127-qubit IBM Quantum System One. Through more than 200+ tests run on actual hardware, our model successfully identified higher fidelity paths in approximately 24% of *** Codes 81P68 © 2024, CC BY-NC-SA.

关键词： Quantum entanglement

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