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1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Balamurugan, K.S. Kalyani, Gedela Al-Fatlawy, Ramy Kurdi, Waleed Hadi Madhloom Alsalamy, Fatima Al-Aboudy, H.M. Manikandan, K. Karpaga Vinayaga College of Engineering and Technology Department of ECE Tamil Nadu Chengalpattu India Karpaga Vinayaga College of Engineering and Technology Big Data Analytics Tamil Nadu Chengalpattu India The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq Altoosi University College Department of Computer Science Najaf Iraq Al-Mustaqbal University Medical Laboratories Techniques Department Hillah Iraq Mazaya University College DhiQar Department of Computer Techniques Engineering Iraq Sona College of Technology Department of Biomedical Engineering Tamil Nadu Salem India

ISBN: (纸本)9798350367003

Wireless sensor network (WSN) installation and power allocation optimization for cybersecurity purposes are difficult tasks which include a well-thought-out method that strikes a balance between goals such as the energy efficiency, connectivity and cyber threat resistance while improving overall system performance and cybersecurity. A novel multi-task walrus optimization (MTWO) technique is offered in this study. The installation and power allocation problem (IPAP) with several jobs is established in this research. The suggested MTWO is used to split the IPAP into numerous scalar components, which are consequently grouped and treated by their desired goals. We evaluate the proposed MTWO approach using simulations and show that it works well in practice. The findings demonstrate that power distribution and installation for cybersecurity activities on WSNs can be improved by the MTWO. The superiority of the problem specific MTWO over the MOGA has been demonstrated by simulation outcome in several network cases, offering a wide range of excellent network designs to aid in the decision-maker selection. © 2024 IEEE.

关键词： Energy efficiency

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Productive Crop Field Detection: A New dataset and Deep Learning Benchmark Results

arXiv

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arXiv 2023年

作者： Nascimento, Eduardo Just, John Almeida, Jurandy Almeida, Tiago The Department of Computer Science Federal University of São Carlos São Paulo Brazil The Department of Data Science and Analytics John Deere IA United States

—In precision agriculture, detecting productive crop fields is an essential practice that allows the farmer to evaluate operating performance separately and compare different seed varieties, pesticides, and fertilizers. However, manually identifying productive fields is often time-consuming, costly, and subjective. Previous studies explore different methods to detect crop fields using advanced machine learning algorithms to support the specialists’ decisions, but they often lack good quality labeled data. In this context, we propose a high-quality dataset generated by machine operation combined with Sentinel-2 images tracked over time. As far as we know, it is the first one to overcome the lack of labeled samples by using this technique. In sequence, we apply a semi-supervised classification of unlabeled data and state-of-the-art supervised and self-supervised deep learning methods to detect productive crop fields automatically. Finally, the results demonstrate high accuracy in Positive Unlabeled learning, which perfectly fits the problem where we have high confidence in the positive samples. Best performances have been found in Triplet Loss Siamese given the existence of an accurate dataset and Contrastive Learning considering situations where we do not have a comprehensive labeled dataset available. Copyright © 2023, The Authors. All rights reserved.

关键词： Precision agriculture

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BoilerTAI: A Platform for Enhancing Instruction Using Generative AI in Educational Forums

arXiv

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arXiv 2024年

作者： Sinha, Anvit Goyal, Shruti Sy, Zachary Kuperus, Rhianna Dickey, Ethan Bejarano, Andres Department of Computer Science Purdue University West LafayetteIN47906 United States Institutional Data Analytics and Assessment Purdue University West LafayetteIN47906 United States

Contribution: This Full paper in the Research Category track describes a practical, scalable platform that seamlessly integrates Generative AI (GenAI) with online educational forums, offering a novel approach to augment the instructional capabilities of staff. The platform empowers instructional staff to efficiently manage, refine, and approve responses by facilitating interaction between student posts and a Large Language Model (LLM). Background: This study is anchored in Vygotsky’s sociocultural theory, with a particular focus on the concept of the More Knowledgeable Other (MKO). It examines how GenAI can augment the instructional capabilities of course staff in educational environments, acting as an auxiliary MKO to facilitate an enriched educational dialogue between students and instructors. This theoretical backdrop is important for understanding the integration of AI within educational contexts, suggesting a balanced collaboration between human expertise and artificial intelligence to enhance the learning and teaching experience. Research Question: How effective is GenAI in reducing the workload of instructional staff when used to pre-answer student questions posted on educational discussion forums? Methodology: Employing a mixed-methods approach, our study concentrated on select first and second-year computer programming courses with significant enrollments. The investigation involved the use of an AI-assisted platform by designated (human) Teaching Assistants (AI-TAs) to pre-answer student queries on educational forums. Our analysis includes a qualitative examination of feedback and interactions, focusing on the AI-TAs’ experiences and perceptions. While we primarily analyzed efficiency indicators such as the frequency of modifications required to AI generated responses, we also explored broader qualitative aspects to understand the impact and reception of AI-generated responses within the educational context. This approach allowed us to gather insights into

关键词： Students

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Enhancing Breeding Control: The IoT-Driven Automated System for Budgie Farming 11

Enhancing Breeding Control: The IoT-Driven Automated System ...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Nooyimsai, Saranpong Thaloy, Jarinya Jansengrat, Parinya Janratchakool, Weena Krohkaew, Jaturapith Crisnapati, Padma Nyoman Thwe, Yamin Rajamangala University of Technology Srivijaya Department of Information Technology Nakhon Si Thammarat Thailand Rajamagala University of Technology Department of Big Data Management and Analytics Thanyaburi Pathum Thani Thailand Rajamagala University of Technology Department of Computer Science Thanyaburi Pathum Thani Thailand Institute of Technology and Business Stikom Bali Department of Information System Bali Indonesia

ISBN: (纸本)9798350305968

In Thailand's Pak Phanang Nok Hong Yok Farm, where budgies are raised, it emphasizes the value of proper care and surroundings. The primary problem this research addresses is the need for efficient and automated management of budgie farms, encompassing aspects such as feeding, watering, and temperature control and overcoming language barriers for Thailand's budgie farmers. An innovative Internet of Things (IoT) solution is suggested, departing from conventional approaches to handle challenges connected to manual care and environmental management for farmers. This system provides solutions for these challenges while enhancing monitoring and facilitating automated control. Through this IoT-based solution, the research intends to increase productivity, save time, and decrease bird mortality while enabling users to manage their farms remotely. Comprehensive methodologies involving breeding, environmental considerations, and hardware and software design are outlined, resulting in significant advancement in budgie farming practices and showcasing IoT's potential in agriculture. © 2023 IEEE.

关键词： Birds

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Augmenting Industrial Chatbots in Energy Systems using ChatGPT Generative AI

Augmenting Industrial Chatbots in Energy Systems using ChatG...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Gihan Gamage Sachin Kahawala Nishan Mills Daswin De Silva Milos Manic Damminda Alahakoon Andrew Jennings Centre for Data Analytics and Cognition La Trobe University Melbourne Australia Department of Computer Science Virginia Commonwealth University Richmond USA

Chatbots, the automation of communicative labor, have been widely deployed in industrial applications and systems. Built upon the Generative Pre-trained Transformer 3 (GPT-3), ChatGPT is a Generative Artificial Intelligence (AI) primed to transform all pre-existing chatbot capabilities with human-like conversation skills. It has already disrupted many disciplines including tertiary education and academic research methods, with increasing adoption in simple to complex tasks. However, the augmentation of pre-existing industrial chatbots with generative AI capabilities has not been fully investigated and demonstrated in recent literature. In this paper, we address this gap by presenting the augmentation of a pre-existing chatbot using ChatGPT generative AI capabilities. Our contribution encompasses the ten primary human-like conversation capabilities of ChatGPT, its augmentation of the pre-existing functionalities and the adopted prompt engineering strategies. Each capability is empirically demonstrated on Cooee, a functionally deployed chatbot in the microgrid energy systems of the La Trobe Energy analytics Platform (LEAP).

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EmoZen: A Robust Word Embedding for Implicit and Explicit Expressions of Emotion

EmoZen: A Robust Word Embedding for Implicit and Explicit Ex...

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Annual Conference of Industrial Electronics Society

作者： Prabod Rathnayaka Gihan Gamage Daswin De Silva Damminda Alahakoon Milos Manic Centre for Data Analytics and Cognition La Trobe University Melbourne Australia Department of Computer Science Virginia Commonwealth University Richmond USA

Machine perception of emotions is integral to the development of human-centric Artificial Intelligence (AI) in sustainable industrial applications. Human expressions of emotions are not always direct. Word embeddings are mature techniques that can extract the semantics of such indirect expressions from text data. However, they are not primed to extract emotions. In this paper, we propose a novel approach that generates robust word embeddings for implicit and explicit expressions of emotion. This approach consists of two techniques, mask and rogue, we evaluate both techniques on two benchmark datasets for emotion classification. Our results confirm the effectiveness of the proposed approach in extracting emotions from diverse contexts. We have shared the emotion word embedding for public use.

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Machine Learning Approaches for Proactive Phishing Attack Detection 7

Machine Learning Approaches for Proactive Phishing Attack De...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Geetha, B. Malathi, P. Thirumalaikumari, T. Janakiraman, V. Basha, H. Anwer Rukmani Devi, S. Saveetha Institute of Medical and Technical Sciences Science Saveetha College of Liberal Arts and Sciences Department of Computer Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of ECE Tamil Nadu Chennai India

ISBN: (纸本)9798350372816

Phishing attacks remain a serious problem in cybersecurity, necessitating advances in detection methods. The study describes a novel method for detecting phishing attacks that use advanced machine learning (ML) techniques, notably convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs and RNNs were chosen because of their ability to detect spatial and sequential patterns in phishing data. The system achieves improved performance by iterative model refinement and ensemble learning, beating existing rule-based systems in terms of precision (96%), recall (94%), and F1-score (95%). Furthermore, the proposed strategy has significantly lower false positive (3.8%) and false negative (5.7%) rates than existing systems, indicating greater dependability in distinguishing between legitimate and malicious emails. Cross-validation validates that the proposed system is stable and ready for real-world use. As a result, the proposed ML-driven strategy represents a substantial leap in phishing detection, providing increased accuracy (F1-score: 95%) and resilience to emerging cybersecurity threats. © 2024 IEEE.

关键词： Phishing

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Bi-SIS Epidemics on Graphs - Quantitative Analysis of Coexistence Equilibria

Bi-SIS Epidemics on Graphs - Quantitative Analysis of Coexis...

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IEEE Conference on Decision and Control

作者： Vishwaraj Doshi Jie Hu Do Young Eun Data Science and Advanced Analytics Team at IQVIA Department of Electrical and Computer Engineering NCSU

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We consider a system in which two viruses of the Susceptible-Infected-Susceptible (SIS) type compete over general, overlaid graphs. While such systems have been the focus of many recent works, they have mostly been studied in the sense of convergence analysis, with no existing results quantifying the non-trivial coexistence equilibria (CE) - that is, when both competing viruses maintain long term presence over the network. In this paper, we prove monotonicity of the CE with respect to effective infection rates of the two viruses, and provide the first quantitative analysis of such equilibria in the form of upper bounds involving spectral radii of the underlying graphs, as well as positive equilibria of related single-virus systems. Our results provide deeper insight into how the long term infection probabilities are affected by system parameters, which we further highlight via numerical results.

关键词： Epidemics Analytical models Upper bound Statistical analysis Stars Topology Numerical models

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AUTODEEPSLICE: A data Driven Network Slicing Technique of 5G network using Automatic Deep Learning

AUTODEEPSLICE: A Data Driven Network Slicing Technique of 5G...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Chowdhury, Deepraj Das, Rupanjan Rana, Risav Dwivedi, Ashutosh Dhar Chatterjee, Pushpita Mukkamala, Raghava Rao International Institute of Information Technology Department of Electronics and Communication Naya Raipur India Copenhagen Business School Centre for Business Data Analytics Department of Digitization Frederiksberg Denmark Tennessee State University Department of Computer Science Nashville United States

ISBN: (纸本)9781665459754

With the advancement of mobile networks, and the revolution of mobile communication from 1G to 5G, human dependency has increased, so it has become the most priority to provide an efficient and highly reliable network. The 5G networks need high capacity and high-speed data transfer, with the least latency and increased security. 5G service providers are trying to find a programmable solution that can handle a huge number of independent service users on the same physical infrastructure. Network slicing is used to provide end-to-end network resource allocation. To accelerate the 5G network performance, Artificial Intelligence (AI) powered data-driven based decisions will be used for future communication. In this proposed work, Automatic deep learning has been implemented, where the hyperparameter will automatically select the algorithm and tune the hyperparameter. The proposed model AUTODEEPSLICE efficiently makes intelligent decisions and will choose the best network slice at the time of network breakdown. © 2022 IEEE.

关键词： 5G mobile communication systems

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A Cybersecurity Framework for Home Energy Management Systems using Artificial Intelligence

A Cybersecurity Framework for Home Energy Management Systems...

引用

IEEE International Symposium on Industrial Electronics (ISIE)

作者： Lakshitha Gunasekara Harsha Moraliyage Daswin De Silva Nishan Mills Damminda Alahakoon Andrew Jennings Milos Manic Centre for Data Analytics and Cognition La Trobe University Melbourne Australia Department of Computer Science Virginia Commonwealth University Richmond USA

Home Energy Management Systems (HEMS) have become a pivotal component of the smart grid infrastructure as they facilitate energy exchange and trading. With the increasing use of micropower renewables generation from wind and solar within residential houses and buildings, HEMS now plays a critical role in not just managing energy usage but assisting the smart grid with schedule optimisation. However, HEMS are typically installed with limited security measures, which makes them vulnerable to cyber-attacks. A coordinated attack on multiple HEMS has the potential to compromise the entire power grid. In this article, we aim to address this gap by proposing a robust security framework for HEMS. The framework centralises security in the design of HEMS and leverages Artificial Intelligence for the security requirements. The proposed framework consists of five layers with built-in security measures that use AI to mitigate cyber threats. This is a key contribution that has been neglected in related work in optimizing HEMS operations.

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