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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Jayasinghe, J.A.S.K. Thennakoon, T.M.H.L. Ruchira, U.G.C. Wimalasena, N.G.C.L. Jayakody, J.A.D.C.A. Pandithage, Dinithi Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331505264

The Internet of Things-empowered precision irrigation management system with LoRaWAN technology is presented given the growing food requirements across the world and pressing calls for judicious use of water in agriculture. Flow Pilot, specifically designed for chili farms, optimizes water application while enhancing crop health using precise and automated irrigation. The Flow Pilot features advanced sensors such as soil moisture, temperature, humidity, and rain sensors that collect real-time environmental data transmitted over a LoRaWAN network for analysis. Flow Pilot uses the Machine learning algorithm by analyzing sensor data to make localized irrigation recommendations based on crop needs, environmental factors, and weather forecasting. LoRaWAN gateways will support this for long-ranged communications over power-consuming agricultural fields. Besides this, it can use different network architectures. There is also an automated water tank management module that regulates the supply of water and pH levels to have optimal conditions for crop growth. A visually simple dashboard interface enables farmers to inspect and operate irrigation processes remotely, hence rendering the whole process far more efficient. Finally, over the extensive period of field tests executed by the project, Flow Pilot was able to save water, improve crop yields, and help support sustainable agriculture. For example, this testing supports the potential of IoT with LoRaWAN technologies, such as the Flow Pilot implementation, for a game change in conventional irrigation practices into a modern, scalable, and reliable solution. © 2024 IEEE.

关键词： Soil moisture

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Polymorphic Security Enhancements for Corporate Environments Utilizing Behavior Analytics

Polymorphic Security Enhancements for Corporate Environments...

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2024 International Conference on Computer and Applications, ICCA 2024

作者： Wijesinghe, W.M.A.J. Abeysinghe, Y.P.P. Rukshana, M.A.F. Adhikari, A.M.P.S. Abeywardhana, Kavinga Yapa Siriwardhana, Deemantha Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350367560

Employers should recognize that employees are the most vulnerable aspect of business environments since these cyber hazards are growing because of user neglect, lack of fundamental security discipline, and a fast-changing threat context. The theoretical approach proposed in the study bridges the difference by carefully following and handling the staff's internet presence. Overall, the study describes a comprehensive solution to govern the security of employees' internet presence by utilizing Artificial Neural Networks (ANN) for advanced intrusion detection, Natural Language Processing (NLP) for implementation and enforcement of browser-based security policies, visual and text data recognition to identify phishing attempts, and lastly analyzing and profiling users based on their online presence via a mathematical model. As a result, this study provides a comprehensive solution for mitigating risks related to human factors in corporate environments. © 2024 IEEE.

关键词： Phishing

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Assessing Quality of Service Performance in SDN Controllers: POX vs. OpenDaylight 6

Assessing Quality of Service Performance in SDN Controllers:...

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6th International Conference on Advancements in computing, ICAC 2024

作者： Lokuliyana, Shashika Wijesiri, Pipuni Jayakody, Anuradha Abeygunawardhana, Pradeep K.W. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

This research offers a comparative analysis of Qual-ity of Service (QoS) within software Defined Networks (SDN) by assessing the POX and OpenDaylight controllers across various network topologies. The results show that OpenDaylight has a 32.59% higher average throughput than POX, demonstrating its superior data handling capabilities. Conversely, POX exhibits a 54.53% lower average delay and an 85.05% higher average jitter compared to OpenDaylight, highlighting its effectiveness in maintaining lower latency and variability in packet transmission times. These contrasting performances provide essential insights for network engineers in selecting the optimal SDN controller based on specific network demands, significantly influencing the overall network efficiency and stability. © 2024 IEEE.

关键词： Quality of service

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Seeking information with a More Knowledgeable Other

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Interactions 2023年第1期30卷 70-73页

作者： Ekstrand, Michael D. Pera, Maria Soledad Wright, Katherine Landau Department of Computer Science Boise State University United States Web Information Systems Group Delft University of Technology Software Technology Department Netherlands

This forum provides a space to engage with the challenges of designing for intelligent algorithmic experiences. We invite articles that tackle the tensions between research and practice when integrating AI and UX design. We welcome interdisciplinary debate, artful critique, forward-looking research, case studies of AI in practice, and speculative design explorations. - - Juho Kim and Henriette Cramer, Editors © 2023 ACM.

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Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples 41

Et Tu Certifications: Robustness Certificates Yield Better A...

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41st International Conference on Machine Learning, ICML 2024

作者： Cullen, Andrew C. Liu, Shijie Montague, Paul Erfani, Sarah M. Rubinstein, Benjamin I.P. School of Computing and Information Systems University of Melbourne Parkville Australia Defence Science and Technology Group Adelaide Australia

In guaranteeing the absence of adversarial examples in an instance's neighbourhood, certification mechanisms play an important role in demonstrating neural net robustness. In this paper, we ask if these certifications can compromise the very models they help to protect? Our new Certification Aware Attack exploits certifications to produce computationally efficient norm-minimising adversarial examples 74% more often than comparable attacks, while reducing the median perturbation norm by more than 10%. While these attacks can be used to assess the tightness of certification bounds, they also highlight that releasing certifications can paradoxically reduce security. Copyright 2024 by the author(s)

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Rare Collision Risk Estimation of Autonomous Vehicles with Multi-Agent Situation Awareness 27

Rare Collision Risk Estimation of Autonomous Vehicles with M...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Zaker, Mahdieh Blom, Henk A.P. Soudjani, Sadegh Lavaei, Abolfazl School of Computing Newcastle University United Kingdom Delft University of Technology Netherlands Max Planck Institute for Software Systems Germany

ISBN: (纸本)9798331505929

This paper offers a formal framework for the rare collision risk estimation of autonomous vehicles (AVs) with multi-agent situation awareness, affected by different sources of noise in a complex dynamic environment. The estimation framework consists of two complementary parts: modeling formalism and a rare event estimation method using sequential Monte Carlo (MC) simulation instead of importance sampling. By defining incremental levels of severity that must be passed before a collision, a sequence of MC simulations can be applied from one level to the next. This particular sequential MC method consists of the simulation of an Interacting Particle System (IPS) in combination with Fixed Assignment Splitting (FAS) of particles that reach the next level. We model AVs equipped with the situation awareness as general stochastic hybrid systems (GSHS), including the IPS-FAS relevant severity levels, and assess the probability of collision in a lane-change scenario where two self-driving vehicles simultaneously intend to switch lanes into a shared one while utilizing the time-to-collision measure for decision-making as required. The IPS-FAS method is subsequently used to estimate collision risk for this GSHS model of the lane-changing scenario. The results show that in contrast to straightforward MC simulation, IPS-FAS is able to quantify the very low collision risk for the scenario of interest. © 2024 IEEE.

关键词： Importance sampling

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A Deep Learning for Alzheimer’s Stages Detection Using Brain Images

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Computers, Materials & Continua 2023年第1期74卷 1457-1473页

作者： Zahid Ullah Mona Jamjoom Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

Alzheimer’s disease(AD)is a chronic and common form of dementia that mainly affects elderly *** disease is dangerous because it causes damage to brain cells and tissues before the symptoms appear,and there is nomedicinal or surgical treatment available yet *** loss of memory and functionality control in multiple degrees according to AD’s progression ***,early diagnosis of AD can hinder its *** imaging tools such as magnetic resonance imaging(MRI),computed tomography(CT)scans,positron emission tomography(PET),*** help in medical diagnosis of ***,computer-aided diagnosis(CAD)such as deep learning applied to brain images obtained with these tools,has been an established strategic methodology that is widely used for clinical assistance in prognosis of *** this study,we proposed an intelligent methodology for building a convolutional neural network(CNN)from scratch to detect AD stages from the brain MRI images dataset and to improve patient *** is worth mentioning that training a deep-learning model requires a large amount of data to produce accurate results and prevent the model from overfitting ***,for better understanding of classifiers and to overcome the model overfitting problem,we applied data augmentation to the minority classes in order to increase the number of MRI images in the *** experiments were conducted using Alzheimer’s MRI dataset consisting of brain MRI scanned *** performance of the proposed model determines detection of the four stages of *** results show high performance of the proposed model in that the model achieved a 99.38%accuracy rate,which is the highest so ***,the proposed model performance in terms of accuracy,precision,sensitivity,specificity,and f-measures is promising when compared to the very recent state-of-the-art domain-specific models existing in the literature.

关键词： Dementia MRI images CNN model detection data augmentation decision support system

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Edge Intelligence for Wildlife Conservation: Real-Time Hornbill Call Classification Using TinyML 2nd

Edge Intelligence for Wildlife Conservation: Real-Time Hornb...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Hing, Kong Ka Behjati, Mehran Saleh, Vala Meng, Yap Kian Majeed, Anwar P. P. Abdul Zheng, Yufan Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya47500 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya47500 Malaysia Department of Electrical Engineering Shiraz University of Technology Shiraz Iran Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Hornbills, an iconic species of Malaysia's biodiversity, face threats from habitat loss, poaching, and environmental changes, necessitating accurate and real-time population monitoring that is traditionally challenging and resource intensive. The emergence of Tiny Machine Learning (TinyML) offers a chance to transform wildlife monitoring by enabling efficient, real-time data analysis directly on edge devices. Addressing the challenge of wildlife conservation, this research paper explores the pivotal role of machine learning, specifically TinyML, in the classification and monitoring of hornbill calls in Malaysia. Leveraging audio data from the Xeno-canto database, the study aims to develop a speech recognition system capable of identifying and classifying hornbill vocalizations. The proposed methodology involves preprocessing the audio data, extracting features using Mel-Frequency Energy (MFE), and deploying the model on an Arduino Nano 33 BLE, which is adept at edge computing. The research encompasses foundational work, including a comprehensive introduction, literature review, and methodology. The model is trained using Edge Impulse and validated through real-world tests, achieving high accuracy in hornbill species identification. The project underscores the potential of TinyML for environmental monitoring and its broader application in ecological conservation efforts, contributing to both the field of TinyML and wildlife conservation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Environmental monitoring

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Optimized Deep Learning Mechanism for Intrusion Detection: Leveraging RFE-Based Feature Selection and PCA for Improved Accuracy 5

Optimized Deep Learning Mechanism for Intrusion Detection: L...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Rajkumar, M. Vs, Lakshi Karthik, R. Pavithra, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Smart Computing Tamil Nadu India B. Tech-Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu India

ISBN: (纸本)9798331530013

With the growing demand for wireless networks, network traffic has surged, particularly involving large-scale data transmission. This results in an increased number of network security threats and the emergence of various cyber-attacks. To overcome these threats, the development of an efficient intrusion detection system (IDS) has become crucial. By leveraging the recent advancements in Artificial intelligence (AI), this project introduces an optimized deep learning mechanism utilizing a prevalent deep learning model, the Deep Neural Network (DNN) algorithm. This proposed method uses feature engineering techniques like feature selection and feature reduction. Given the high dimensionality of the selected datasets, these techniques are employed to prevent over-fitting. Recursive feature elimination performs feature selection followed by Principal Component Analysis for the feature reduction process, thus enhancing the model's efficiency. The system is assessed using two benchmark datasets: KDD and UNSW-NB15. The experimental results indicate that the presented DNN model on the KDD dataset has achieved 92.45% accuracy and 91.35% accuracy on the UNSW-NB15 dataset for intrusion classification. © 2024 IEEE.

关键词： Network intrusion

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Sinhala Sign Language Detection Approach for Deaf People Using Human Pose Estimation

Sinhala Sign Language Detection Approach for Deaf People Usi...

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2024 International Research Conference on Smart computing and systems Engineering, SCSE 2024

作者： Nadeesha, S.J.M.J. Wasalthilaka, W.V.S.K. Sabaragamuwa University of Sri Lanka Faculty of Computing Department of Computing and Information Systems Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Faculty of Computing Department of Software Engineering Belihuloya Sri Lanka

ISBN: (纸本)9798350375688

People with disabilities have physical, sensory, cognitive, or behavioral health problems that limit their participation in activities and interaction with the environment. Deafness is one of the most common types of disability in society. Since verbal communication is impossible for the deaf, sign language is one of the most often used forms of communication among those disable people. However, a third party who is fluent in sign language is necessary for efficient communication. This research study proposed a sign language detector for Sinhala using Logistic Regression, Ridge Classifier, Random Forest, and Gradient Boosting algorithms with human pose estimation. The method used upper-body stance landmarks with 100 human pose videos and 40 widely used signals to extract features for body language classification. With an overall 86.75% success rate, this study is a major step toward improving deaf people's communication in Sri Lanka. © 2024 IEEE.

关键词： Logistic regression

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