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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Jayasekara, Charith Banneka, Sajani Pasindu, Gihan Udawaththa, Yukthi Wellalage, Sasini Abeygunawardhane, Pradeep K.W. Sri Lanka Institute of Information Technology Dep. of Computer Systems Engineering Malabe10115 Sri Lanka

ISBN: (纸本)9781665408622

Greenhouses are a type of cultivation method used to optimize the production of crops by using controlled climatic conditions and other external factors. They are widely used in agriculture both globally and locally. Vertical farming is the modern practice of growing crops in vertically stacked layers in warehouses that aims to optimize and develop plant growth by using controlled environment agriculture. This type of agriculture concept is practiced in Sri Lanka at present. This research paper proposes a robot which consists of a specific navigation system and harvesting mechanism that can be used inside greenhouses. The proposed system can be implemented to harvest lettuce in the vertical farming greenhouses where lettuce needs to be harvested with care to ensure the supermarket quality. The above-mentioned system helps to detect the diseases of plants also where a lot of time can be saved, and less effort is made with the use of this type of implementation. © 2021 IEEE.

关键词： Greenhouses

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Secure Password Using EEG-based BrainPrint System: Unlock Smartphone Password Using Brain-Computer Interface Technology

Secure Password Using EEG-based BrainPrint System: Unlock Sm...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Alkhyeli, Zuwaina Alshehhi, Ayesha Alhemeiri, Mazna Aldhanhani, Salma Albalushi, Khalil Alnuaimi, Fatima Ali Belkacem, Abdelkader Nasreddine UAE University CIT Dep. Computer Science and Software Engineering Al Ain United Arab Emirates UAE University CIT Dep. Information Systems and Security Al Ain United Arab Emirates United Arab Emirates University CIT Dep. Computer & Network Engineering Al Ain United Arab Emirates Autonomous Robotics Research Center TII Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665468190

As security becomes a strong factor in daily activities, finding secure ways to unlock machines and smartphones is a challenge due to hardware limitations and the high risk of hacking. Considering the level of security and privacy in the digital world, attackers tend to be one step ahead. Therefore, this technical paper introduces a brain-computer interface (BCI) for increasing subject-based security using unique biometric features as a solution to build complex passwords. The BCI measures brain changes and extracts relevant bio-features from each subject using non-invasive electroencephalogram (EEG) tests. The proposed system allows users to gain access to their devices using brain waves (bypass) instead of inserting their password manually (normal path), which saves the user time and upgrades the level of privacy as no physical actions are required during this process. This system is also well suited for individuals with mobility impairments. We used the P300-based BCI controlling paradigm which dep.nds on reading the electrical brain activity of the user when observing a particular object. The other feature of the system is that it can extract unique features of each individual brain to produce a network that uniquely identifies them, which is used as a security layer. Users need to enter their unique network to access their device with failed attempts requiring an EEG test to identify the user. The system plays an active role in facilitating user processes for authentication while accessing devices. The system establishes an urgent call whenever the user's brain currents command it to. The project outcomes were assessed by simulating the BCI before real-time implementation to determine errors and resolve the validity of the project scope. © 2022 IEEE.

关键词： Brain computer interface

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Bayesian Network Structure Discovery Using Antlion Optimization Algorithm.

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2022年第1期7卷 46-65页

作者： Awla, Hoshang Qasim Kareem, Shahab Wahhab Mohammed, Amin Salih Computer science department Faculty of Science Soran University Erbil Iraq Dep. of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Poly-technic University Erbil Iraq Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Erbil Iraq

Bayesian networks have recently been used for discovering an optimal learning structure in machine learning. Bayes networks can describe possible dep.ndencies of explanatory variables. As a novel approach to studying the structure of a Bayesian network, the authors present the Antlion Optimization Algorithm (ALO). In the algorithm;deletion, rewind, insertion, and change are utilized to produce ALO to reach the best hull solution. Essentially, the technique used in the ALO algorithm imitates the antlions’ behaviors while hunting. The suggested approach is contrasted with simulated Annealing, Simulated Annealing Hybrid Bee, Greedy Search Hybrid Bee, optimization inspired by Pigeon, and greedy search using the BDe Score function. The researchers also studied the representation of the confusion matrix of these techniques using different reference data sets. The findings of the assessments reveal that the proposed algorithm works better than the other algorithms and has better consistency and score values. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values). © 2022. All Rights Reserved.

关键词： Antlion Optimization Bayesian Network Hunting Search Local & Global Search Metaheuristics Search and Score Structure Learning

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Hybrid Algorithm for Real-Time Data Forecasting with Density-Based Clustering and Penalty Splines

Hybrid Algorithm for Real-Time Data Forecasting with Density...

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International Conference on Industrial engineering, Applications and Manufacturing (ICIEAM)

作者： Elena Kochegurova Vladislav Denisov Maria Galkina Information Systems and Technologies Dep. Tomsk Polytechnic University Tomsk Russian Federation School of Nuclear Science and Engineering Tomsk Polytechnic University Tomsk Russian Federation

ISBN: (数字)9798350395013

ISBN: (纸本)9798350395020

We propose a hybrid forecasting model that includes time series segmentation, which is based on the DBScan clustering algorithm, and a penalty spline. Each step of the hybrid model required modification for real-time use. The DBScan density-based algorithm was supplemented with technology for working with noise and transition between clusters. A recurrent penalty spline model has low computational complexity and, accordingly, high forecasting speed inside the hybrid model. The forecast efficiency was assessed by a number of indicators that reflect predictive efficiency and extrapolation accuracy of using synthetic, model and real TS. Accuracy indicators (MSPE, MAPE) are below 4%. In comparison with other widely-spread forecasting methods, the proposed hybrid algorithm ranks among the known algorithms.

关键词： Extrapolation Accuracy Computational modeling Noise Clustering algorithms Predictive models Prediction algorithms

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Logistic Regression Classification for Assessing the Risk of Kidney Tumor

Logistic Regression Classification for Assessing the Risk of...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Dalia Alzu'bi Rabia Emhamed Al Mamlook Ahmad Nasayreh Mohammad Aljaidi Rami AlAzab Hasan Gharaibeh Qais Al-Na'amneh Dep. Information and Systems Engineering Concordia University Montreal Canada Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University Zawia Tripoli Libia Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. Surgery and Urology Jordan University of Science and Technology Irbid Jordan Dep. of Cyber Security Applied Science Private University Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

The kidneys have the important function of filtering waste products and toxins from the blood in the human body. Any problems affecting the kidneys can potentially impact their efficiency and overall function. Kidney Tumor (KT) is a disease that specifically affects kidney cells and causes the abnormal growth of tissue in one or both kidneys. Assessing the risk of developing a kidney tumor and identifying the key factors that contribute to this risk are crucial for ensuring patient well-being. Additionally, this information assists doctors and specialists in making faster and more accurate diagnoses, determining appropriate treatment methods, and potentially influencing the course of the disease to minimize its severity and impact. Machine learning algorithms have recently been introduced to evaluate disease risks, and in this study, we specifically focus on examining the risk of kidney tumor development and investigating the influencing factors. We employed a logistic regression model to predict if a patient is at risk of developing a kidney tumor or not, and further categorized the samples into high-risk and low-risk groups. Our model was trained and tested using a unique dataset obtained from the (KAUH) hospital in Jordan, consisting of well-balanced metadata from 120 patients with kidney issues. Our work demonstrated accuracy results ranging from 90% to 98%. Ultimately, specialists can utilize our model as an additional tool to enhance the speed and accuracy of patient diagnoses

关键词： Logistic regression Analytical models Accuracy Predictive models Genetics History Reliability

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Feature Selection for Robust Spoofing Detection: A Chi-Square-based Machine Learning Approach

Feature Selection for Robust Spoofing Detection: A Chi-Squar...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Qais Al-Na'amneh Mohammad Aljaidi Hasan Gharaibeh Ahmad Nasayreh Rabia Emhamed Al Mamlook Sattam Almatarneh Dalia Alzu'bi Abla Suliman Husien Dep. of Cyber Security Applied Science Private University Amman Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University of Zawia Tripoli Libia Dep. of Data Science and Artificial Intelligence Zarqa University Zarqa Jordan Dep. Information and Systems Engineering Concordia University Montreal Canada

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Nowadays, the Internet of Things (IoT) system is vulnerable to spoofing attacks that can easily where attackers can easily pose as a legal entity of the network. A “spoofing attack” refers to a type of cyber-attack when an attacker purposefully impersonates or masquerades as someone or something else to deceive the target or obtain unauthorized access to systems, information, or resources. In such attacks, the attacker alters their name, IP address, or other attributes to fool the victim into thinking they are engaging with a legitimate entity. Spoofing attacks can take place via a variety of channels, including, ARP and DNS spoofing. Therefore. Spoofing attacks can have serious consequences. We proposed a new approach based on three machine learning models LightGBM, Gradient Boost, and XGBoost to classify attacks on spoofing, we used Chi-square to select the best features to get the highest performance, and we demonstrated that the results using Chi-square achieved higher results than without Chi-square and improved the result with a rate three percent of accuracy. In terms of results, LightGBM outperformed other models by achieving 89%, 91 %, 87 %, and 89 % for accuracy, precision, recall, and f1-score, respectively. This shows the potential and efficiency of Chi-square to achieve the best performance by selecting the best features, thus providing a secure system and identifying cyber-attacks on systems.

关键词： Electric potential Accuracy Law Machine learning Learning (artificial intelligence) Feature extraction Internet of Things

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Efficient News Synopsis Generation with T5 Transformers

Efficient News Synopsis Generation with T5 Transformers

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International IEEE Conference on Intelligent systems, IS

作者： João António Lyudmila Mihaylova Petia Georgieva Dep. of Electronics Telecom. and Informatics (DETI) University of Aveiro Aveiro Portugal Dep. of Automatic Control and Systems Engineering University of Sheffield Sheffield UK University of Aveiro/DETI Institute of Electronics and Informatics Eng. of Aveiro (IEETA) Institute of Telecommunications Aveiro Portugal

ISBN: (数字)9798350350982

ISBN: (纸本)9798350350999

In this paper we propose a Text-To-Text Transfer Transformer-based (T5) model to generate concise, human-like synopses of Merger and Acquisition (M&A) economic news. Leveraging recent advancements in artificial intelligence and natural language processing, we have focused our resources on the creation of a specific summarizer, opting for a translation of all news into English to ensure consistency and mitigate complexity. The implementation of Graphics processing unit (GPU) resources for model training played a crucial role in optimizing the learning process, allowing us to effectively utilize the time window provided by platforms such as Google Colab Pro and Free. Through iterative fine-tuning, our model was trained to generate high-quality, human-like synopses of M&A events. Our findings underscore the enhanced transparency and interpretability of the proposed generative AI-driven text summariser, suggesting promising avenues for future exploration and development in this field.

关键词： Training Economics Technological innovation Corporate acquisitions Graphics processing units Transformers Natural language processing Internet Iterative methods Intelligent systems

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Predicting Yarn Breaks in Textile Fabrics: A Machine Learning Approach 26

Predicting Yarn Breaks in Textile Fabrics: A Machine Learnin...

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26th International Conference on Knowledge-Based and Intelligent information and engineering systems, KES 2022

作者： Azevedo, João Ribeiro, Rui Matos, Luís Miguel Sousa, Rui Silva, João Paulo Pilastri, André Cortez, Paulo EPMQ - IT Engineering Maturity and Quality Lab CCG ZGDV Institute Guimarães Portugal ALGORITMI Center Dep. Information Systems University of Minho Guimarães Portugal Somelos Ronfe Portugal Fluxodata Ronfe Portugal

In this paper, we propose a Machine Learning (ML) approach to predict faults that may occur during the production of fabrics and that often cause production downtime delays. We worked with a textile company that produces fabrics under the Industry 4.0 concept. In particular, we deal with a client customization requisite that impacts on production planning and scheduling, where there is a crucial need of limiting machine stoppage. Thus, the prediction of machine stops enables the manufacturer to react to such situation. If a specific loom is expected to have more breaks, several measures can be taken: slower loom speed, special attention by the operator, change in the used yarn, stronger sizing recipe, etc. The goal is to model three regression tasks related with the number of weft breaks, warp breaks, and yarn bursts. To reduce the modeling effort, we adopt several Automated Machine Learning (AutoML) tools (H2O, AutoGluon, AutoKeras), allowing us to compare distinct ML approaches: using a single (one model per task) and Multi-Target Regression (MTR);and using the direct output target or a logarithm transformed one. Several experiments were held by considering Internet of Things (IoT) historical data from a Portuguese textile company. Overall, the best results for the three tasks were obtained by the single-target approach with the H2O tool using logarithm transformed data, achieving an R2 of 0.73 for weft breaks. Furthermore, a Sensitivity Analysis eXplainable Artificial Intelligence (SA XAI) approach was executed over the selected H2OAutoML model, showing its potential value to extract useful explanatory knowledge for the analyzed textile domain. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent information & engineering Syste

关键词： Machine learning

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Tracking of Multiple Spawning Targets with Heterogeneous Sensors for Seabed-To-Space Situational Awareness

Tracking of Multiple Spawning Targets with Heterogeneous Sen...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Domenico Gaglione Leonardo M. Millefiori Paolo Braca Peter Willett Moe Z. Win NATO STO-CMRE La Spezia Italy Electrical and Computer Engineering Dep. University of Connecticut Storrs CT USA Laboratory for Information and Decision Systems MIT Cambridge MA USA

Seabed-to-space situational awareness (S3A) aims to organize, fuse, and synthesize the massive volume of information collected from heterogeneous sensors, i.e., underwater, terrestrial, and space-based sensors, and therefrom extract knowledge thence available to defence operators, enabling informed decision-making. Heterogeneous sensors provide observations of targets in different domains (e.g., air, water surface, undersea), and with different modalities, perspectives, latencies, and update rates. They complement each other, and an effective approach is needed to combine the data they generate. This paper introduces a comprehensive framework for multi-target tracking based on the sum-product algorithm (SPA) that models the different characteristics of the sensors and handles the appearance of targets in a complex multi-domain environment both through spontaneous births and through spawning from existing targets. The efficacy of this approach is demonstrated through a simulated maritime scenario informed by real-world observation streams.

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Ka-Band Phased Antenna Array Concept for High-EIRP Satellite Connections

Ka-Band Phased Antenna Array Concept for High-EIRP Satellite...

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European Conference on Antennas and Propagation, EuCAP

作者： Stefano Moscato Alessandro Fonte Steven Caicedo Mejillones Alessandro DiCarlofelice Emidio DiGiampaolo Piero Tognolatti Emilio Arnieri Giandomenico Amendola Luigi Boccia Matteo Oldoni Research and Development Dep. SIAE MICROELETTRONICA Cologno Monzese MI Italy Dep. of Industrial and Information Engineering and Economics Univ. of L'Aquila L'Aquila Italy Dep. of Computer Eng. Modeling Electronics and Systems Univ. of Calabria Rende CS Italy Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy

This work deals with an innovative architecture of a phased antenna array operating in the Ka band. It envisages the adoption of custom designed PA together with 64 hollow metallic horns as radiating elements. The proposed architecture is designed to work in the 25.5 to 27 GHz frequency span with a target EIRP higher than 70 dBm, even considering the maximum beam steering configuration of $\pm 20^{\circ}$ . The proposed concept highlights the flexibility in targeting different power levels, antenna gain and steering capabilities to finally address a large number of use-cases. The exploitation of non-uniform array is also taken into account to prevent grating lobes and increasing the steering capabilities of the phased array.

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