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4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Himabindu, C.H. Sreeramu, M. Chaganti, Koushik Reddy Gutta, Pruthvi Krishna Nagesh, C. Ruksana, S. Vignan Institute of Technology and Science Dept of Mba Hyderabad India University of the Cumberlands Dept. of Computer and Information Systems KY United States Tesla Dept of It AustinTX United States Srinivasa Ramanujan Institute of Technology Department of Cse Anantapur India Gates Institute of Technology and Science Department of Cse Anantapur India

ISBN: (纸本)9798331529635

This confluence of escalating energy costs and upsurges in the demand for high-quality crop yields under uncertain environmental conditions makes a strong case for the urgent optimization of greenhouse operations. The most currently used control systems for smart greenhouses are founded upon static or even narrowly focused optimization approaches, which severely limit their adaptability and efficiency in dynamic settings. These methodologies fail to address the challenge of multiple objectives, such as the consumption of energy and crop yield, with unpreparedness for disturbances caused by external influences and computational bottlenecks. This paper brings about a collective framework for three of the most advanced RL methodologies: Multi-Objective Adaptive Policy Gradient with Environmental Feedback, Hierarchical Reinforcement Learning with Temporal Decision Decomposition, and Neural Meta-Controller with Self-Organizing Sub-Networks. MAPGEF utilizes real-time environmental feedback to dynamically adjust policy weights, thus achieving up to a 25% reduction in energy use and an 18% increase in crop yield through accurate environmental tuning. The HRL-TDD utilizes hierarchical RL combined with temporal decomposition in order to handle tough tasks of a greenhouse, thus improving a decision by up to 30%, with energy efficiency improved by 20% and crop yield stabilized with 15%. NMCSOS introduces a neural meta-controller coordinating self-organizing sub-networks, ensuring robust responses to external disturbances with 33% faster recovery, 22% better energy optimization, and 18% reduced yield variability levels. Together, these methodologies address the pressing need for adaptable, scalable, and energy-efficient greenhouse control systems. By integrating multi-objective learning, temporal decomposition, and self-organizing networks, this work significantly advances greenhouse management, promoting sustainable agriculture and resilience in the face of environmental uncert

关键词： Adaptive Policy Gradient Multi-Objective Reinforcement Learning Neural Meta-Controller Scenarios Smart Greenhouse Sustainable Agriculture

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Neural Network-Based Prediction for Lateral Acceleration of Vehicles 2

Neural Network-Based Prediction for Lateral Acceleration of ...

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2nd IEEE Conference on Information technology and Data Science, CITDS 2022

作者： Kontos, Janos Kranicz, Balazs Vathy-Fogarassy, Agnes University of Pannonia Continental Automotive Hungary Ltd. Dept. of Computer Science and Systems Technology Veszprém Hungary Continental Automotive Hungary Ltd. Veszprém Hungary University of Pannonia Dept. of Computer Science and Systems Technology Veszprém Hungary

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

Lateral acceleration is a key element of vehicle dynamics. It is consumed by several control, stability and comfort functions of the vehicle. In this paper a neural network-based prediction method is demonstrated for predicting the value of lateral acceleration. The inputs of the method are the most accessible signals in any modern vehicle: wheel speed information, longitudinal acceleration and steering wheel angle. For training, validating and testing the neural network, experimental data was used. The hyperparameters of the neural network were tuned by a hybrid approach. The accuracy of the approach was evaluated by comparing the actual measured values to those predicted by the neural network. Evaluation results convincingly demonstrate the usefulness and reliability of the developed model. © 2022 IEEE.

关键词： Neural networks

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LightNN: An Optimized Deep Learning Framework for Enhanced Fruit Classification

LightNN: An Optimized Deep Learning Framework for Enhanced F...

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Computing, Applications and systems (COMPAS), IEEE International Conference on

作者： Maharin Afroj Israt Jahan Md Polash Islam Md. Saifur Rahman Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Dept. of Computer and Systems Sciences Stockholm University Stockholm Sweden

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

The crucial field of fruit classification in computer vision and deep learning brings about breakthroughs that have broad applications in the retail and agricultural industries. By automating the identification and sorting of various fruit types, these technologies enhance efficiency, accuracy, and scalability, benefiting processes from farm production to market distribution. The integration of sophisticated algorithms and neural networks not only improves the speed and reliability of fruit classification but also offers the potential for real-time quality control and inventory management, revolutionizing traditional methods and paving the way for more innovative, data-driven approaches in these sectors. This study introduces LightNN, a novel and effective paradigm with a lightweight architecture that tackles the complex problems related to computational efficiency. Combining a small classifier with a simplified feature extractor, LightNN achieves the ideal balance between simplicity and accuracy. The feature extractor is capable of extracting hierarchical features from RGB images by using convolutional layers with ReLU activations and max-pooling procedures. The flattened output is then processed by the classifier, which consists of fully connected layers with ReLU activations and dropout regularisation. Remarkably, LightNN, with just 298,835 parameters, manages to get a respectable 96.37% test accuracy in fruit categorization tasks. The results highlight LightNN’s promise as an effective and efficient fruit classifying method, particularly in situations with constrained computational resources.

关键词： Deep learning Training Visualization Accuracy Computational modeling Scalability computer architecture Feature extraction Computational efficiency Sorting

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Attack Tree Refinements Analysis and Verification by applying Coloured Petri Nets 48

Attack Tree Refinements Analysis and Verification by applyin...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Pasandideh, Shabnam Pereira, Pedro Gomes, Luis Nova School of Science and Technology-Nova University Lisbon Center of Technology and Systems-UNINOVA Dept. Electrical and Computer Engineering Caparica Portugal

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Recently, Graphical Security Models (GrSMs) became widely used for security analysis. The basic formalism called Attack Tree (AT) has been augmented with new attributes covering defence, response, and countermeasure aspects to support security modelling and analysis in vulnerable systems. Although the models have strength in visualising and analysing small attack-defence scenarios, these suffer from lack of scalability when increasing nodes and adaptability with other refinement models to show the dynamic nature and state of systems in interest. In this work, Coloured Petri net (CPN) is used to fulfil the mentioned shortcomings in GrSMs (specifically Tree-based models). It is applied for evaluating each component's interactions, the impact of threats as well as defence systems to mitigate those threats. For that end and pointing out the CPN adaptability with GrSMs, a set of mapping rules are proposed allowing translation of ATs extension into CPN and their analysis. The quantitative analysis aspect is addressed in this work by introducing computing transition. We validate our proposed approach by applying it in an example of SCADA systems cyber-security analysis. © 2022 IEEE.

关键词： Cybersecurity

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Comparative Evaluation of Machine Learning-Based Controller Area Network Intrusion Detection systems 14

Comparative Evaluation of Machine Learning-Based Controller ...

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14th International Conference on Information and Communication systems, ICICS 2023

作者： Al-Jarrah, Omar Y. Ramadan, Omar A. Alsaify, Baha Alkhushayni, Suboh Jordan University of Science and Technology Faculty of Computer and Information Technology Dept. Network Engineering and Security P.O. Box 3030 Irbid22110 Jordan Jordan University of Science and Technology Faculty of Engineering Dept. Mechanical Engineering P.O. Box 3030 Irbid22110 Jordan Yarmouk University Dept. Information Systems Irbid Jordan

ISBN: (纸本)9798350307870

The controller area network (CAN) protocol is widely used in vehicle networks. However, it lacks essential security features like confidentiality and authentication. To enhance vehicle security, researchers have proposed and developed various methods, including intrusion detection systems (IDSs). These IDSs play a key role in early detection of potential cyberattacks, safeguarding vehicles, passengers, and road users. Machine learning (ML) algorithms are increasingly employed in CAN IDSs but they face challenges due to diverse experimental settings, datasets, and metrics used for evaluation, impeding progress and comparison of different approaches in this field. This study aims to benchmark well-recognized ML algorithms, including gaussianNB, k-nearest neighbors (KNN), decision trees (DT), random forest (RF), long short-term memory (LSTM), and convolutional neural networks (CNN), for intrusion detection in CAN. We evaluate the performance of these algorithms using seven evaluation metrics on the real ORNL automotive dynamometer (ROAD) dataset, a state-of-the-art benchmark in this field. Experimental results demonstrate that KNN, DT, RF, LSTM, and CNN can detect various types of attacks (e.g., Fuzzing, targeted ID, and masquerade attacks) with high accuracy. However, the computational and time efficiency of tree-based classifiers, namely DT and RF, makes them a potentially appealing choice for real-time intrusion detection tasks in CAN. © 2023 IEEE.

关键词： Intrusion detection

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Enhancing Train Transportation in Sri Lanka: A Smart IOT Based Multi-Subsystem Approach Using MQTT

Enhancing Train Transportation in Sri Lanka: A Smart IOT Bas...

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IEEE Region 10 International Conference TENCON

作者： Dhanushka Balasingham Sadeesha Samarathunga Anuththara Bandara Gayantha Godakanda Arachchige Narmada Gamage Jaliya L. Wijayaraja Dept. of Computer Systems Engineering Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

This research proposes a system as a solution for the challenges faced by Sri Lanka's historic railway system, such as scheduling delays, overcrowding, manual ticketing, and management inefficiencies. It proposes a multi-subsystem approach, incorporating GPS tracking, RFID-based e-ticketing, seat reservation, and vision-based people counting. The GPS-based real-time train tracking system performs accurately within 24 meters, with the MQTT protocol showing twice the speed of the HTTP-based system. All subsystems use the MQTT protocol to enhance efficiency, reliability, and passenger experience. The study's data and methodology demonstrate the effectiveness of these innovations in improving scheduling, passenger flow, and overall system performance, offering promising solutions for modernizing Sri Lanka's railway infrastructure.

关键词： Meters Technological innovation System performance Machine vision Transportation Manuals Real-time systems Rail transportation Reliability Interoperability

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Iterative Detection Schemes for Uplink Highly Loaded User-Centric Hybrid Beamforming Based Frequency Selective Massive MIMO Networks

TechRxiv

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TechRxiv 2024年

作者： Karatas, Metehan Guvensen, Gokhan Muzaffer Dept. of Information Technology Division of Computer Systems Uppsala University Sweden Ankara Turkey

In cell-free (CF) networks, access points (APs) jointly serve the user equipments (UEs). In user-centric (UC) variant, only a handful of APs serve a particular UE, namely the ones with the best channels. In this paper, we propose a hybrid beamforming aided uplink receiver scheme for highly loaded UC massive MIMO (mMIMO) networks, operating in millimeter-wave(mmWave) frequencies, employing single carrier modulation under frequency selective channels. Every AP employs its own analog beamformer (AB) called the modified eigen-beamformer (MEB), which promotes a trend of fairness. In the digital domain, two detectors are proposed;Cell-Free Iterative Detector (CFI-D) and User-Centric Iterative Subset-Detector (UCIS-D). CFI-D introduces the iterative block decision feedback equalization (IBDFE) method for CF networks. UCIS-D is an alternative to CFI-D. UCIS-D is UC, has low computational complexity (CCP) and is scalable. UCIS-D relies on parallel interference cancellation (PIC) aided IB-DFE. Interfering UEs are divided into three sets. Weak interferers are processed with PIC, strong interferers with IBDFE which considers the residual interference from PIC. Weakest interferers are ignored. Both PIC and IB-DFE utilize the soft decisions from previous iterations. Simulation results show that these receivers can handle ultra high loads in terms of bit-error-rate (BER) and achievable information rate (AIR). © 2024, CC BY-NC-SA.

关键词： Decision feedback equalizers

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Dramatic Increase in Fear-Related Discussion on Twitter during COVID-19: Analysis, Topic Modeling and Tweets Classification

Dramatic Increase in Fear-Related Discussion on Twitter duri...

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2022 International Conference on Electrical Engineering and Sustainable Technologies, ICEEST 2022

作者： Karam, Muhammad Waleed Imran, Azhar Alzahrani, Abdulkareem Ali Alheeti, Khattab M. Abbas, Ahsan Najem Al-Aloosy, Abdulkareem A. Air University Dept. of Creative Technology Islamabad Pakistan Al Baha University Faculty of Computer Science and IT Albaha Saudi Arabia University of Anbar Ramadi Computer Networking Systems Dept. Iraq

ISBN: (纸本)9781665462099

COVID-19 is one the most lethal virus, causing millions of death to date. It was initially detected in Wuhan, China. It then spread rapidly around the globe, which resultantly created major setbacks in the public health sector. The reason of millions of deaths is not only due to the virus itself but it is also linked to peoples' mental state, and sentiments triggered by the fear of the virus. These sentiments are predominantly available on posts/tweets on social media. This paper presents a novel approach for exploratory data analysis of twitter to understand the emotions of general public;country wise, and user wise. Firstly K-Means clustering is employed for topic modeling to categorize the emotions in each tweet. Further supervised machine learning techniques are used to categorize the multi-label tweets. This research concluded that Fear was the most common emotion in twitter discussion. Furthermore, we classified the dataset by performing decision tree (DT), logistic regression (LR), and support vector machine (SVM), finally this paper concluded the results of classification, which shows that SVM can attain better classification accuracy (99%) for COVID-19 text classification. © 2022 IEEE.

关键词： COVID-19

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Dynamic Adaptation Gain Design and Tuning for Threat Discrimination 63

Dynamic Adaptation Gain Design and Tuning for Threat Discrim...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhang, Kangkang Chen, Kaiwen Polycarpou, Marios M. Parisini, Thomas University of Cyprus Kios Research and Innovation Center of Excellence Dept. of Electrical and Computer Engineering Nicosia1678 Cyprus Imperial College London Dept. of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

Considering potential threats to cyber-physical systems such as component faults and stealthy cyber-attacks, an adaptive observer-based threat discrimination method is proposed for identifying the occurring threat type. Typically, stealthy attacks have only weak effects easily obscured by disturbances on the system outputs. To solve this problem, a parameter adaptation algorithm based on a newly designed dynamic adaptive gain generator is proposed, aiming at improving the sensitivity of the adaptive threat discrimination scheme to potential threats. Only the strictly positive real condition of the proposed gain generator sufficiently ensures the stability of the adaptive observer error system. A moment-matching method is then developed to determine the proper parameters of the gain generator, allowing for the improvement of the sensitivity of the threat discriminators. A numerical example to demonstrate the effectiveness of the proposed methodology is presented. © 2024 IEEE.

关键词： Cyber attacks

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Remote Sensing–Based UAV Imaging in Heat Pattern Analysis Impact on Climate Change Detection Using Fuzzy Stacked Lasso Elastic-Net Model

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Remote Sensing in Earth systems Sciences 2024年第4期7卷 699-708页

作者： Sailaja, M. Kumar, M. Prema Jyothi, B. Swarna Vanguri, G. L. Narasamba Manjula, S. Priya, D. Divya Department of Computer Science and Engineering Ravindra College of Engineering for Women Andhra Pradesh Kurnool India Dept. of ECE Shri Vishnu Engineering College for Women (A) Andhra Pradesh Bhimavaram 534202 India Department of Computer Science & amp Engineering Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Information Technology Aditya College of Engineering & amp Technology(A) Andhra Pradesh Surampalem 533437 India Department of Computer Science and Business Systems Nehru Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Urban heat islands raise surface temperatures, which has an effect on city dwellers’ health and welfare. Urbanisation-related changes to the land surface, which are especially notable right after sunset, have an impact on radiative forcing. Recently, there has been a surge in the usage of unmanned aerial vehicle (UAV) technology, particularly for precision agriculture and plant phenotyping. This is due to the declining cost and increased accessibility of both UAVs and thermal imaging sensors. This interruption affects subsequent operations like target item categorisation and water quality parameter inversion. The goal of this study was to evaluate the potential for combining machine learning (ML) with unmanned aerial vehicle (UAV)–based imaging technology. In this research, the novel technique in UAV image–based temperature pattern detection of various regions of earth and their sea level rise analysis using image classification by machine learning model. Here, the input is collected as UAV images which have been processed for noise removal and normalisation. These image features have been extracted using convolutional active contour–based kernel component analysis and classified using region-based fuzzy stacked lasso elastic-net model. The classified output shows temperature analysis with sea level rise based on image analysis. The experimental analysis has been carried out for various UAV image dataset in terms of detection accuracy, RMSE, F-measure, mean average precision (MAP), and recall. Proposed technique F-measure of 89%, detection accuracy of 96%, MAP of 94%, recall of 92%, and RMSE of 60%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional active contour Heat pattern detection Kernel component analysis Lasso elastic-net model Remote sensing

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