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Hosseinzadeh S, Ashawa M, Owoh N, Larijani H, Curtis K. Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling. Sensors (Basel) 2024; 24:860. [PMID: 38339577 PMCID: PMC10857388 DOI: 10.3390/s24030860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 01/19/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024]
Abstract
This article explores the convergence of artificial intelligence and its challenges for precise planning of LoRa networks. It examines machine learning algorithms in conjunction with empirically collected data to develop an effective propagation model for LoRaWAN. We propose decoupling feature extraction and regression analysis, which facilitates training data requirements. In our comparative analysis, decision-tree-based gradient boosting achieved the lowest root-mean-squared error of 5.53 dBm. Another advantage of this model is its interpretability, which is exploited to qualitatively observe the governing propagation mechanisms. This approach provides a unique opportunity to practically understand the dependence of signal strength on other variables. The analysis revealed a 1.5 dBm sensitivity improvement as the LoR's spreading factor changed from 7 to 12. The impact of clutter was revealed to be highly non-linear, with high attenuations as clutter increased until a certain point, after which it became ineffective. The outcome of this work leads to a more accurate estimation and a better understanding of the LoRa's propagation. Consequently, mitigating the challenges associated with large-scale and dense LoRaWAN deployments, enabling improved link budget analysis, interference management, quality of service, scalability, and energy efficiency of Internet of Things networks.
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Affiliation(s)
- Salaheddin Hosseinzadeh
- Department of Cybersecurity and Networks, Glasgow Caledonian University, Glasgow G4 0BA, UK; (M.A.); (K.C.)
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Hamidi M, Moghadam HT, Nasri M, Kasraie P, Larijani H. The effect of ascorbic acid and bio fertilizers on basil under drought stress. BRAZ J BIOL 2022; 84:e262459. [PMID: 35830132 DOI: 10.1590/1519-6984.262459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 05/25/2022] [Indexed: 11/22/2022] Open
Abstract
Evaluate the effect of ascorbic acid application and coexistence of Mycorrhiza fungus and Azospirillium on basil (Ocimum basilicum L.) under drought stress. This experiment was performed as a split factorial in a randomized complete block design with three replications in the crop year 2017-2018 in Shahriar, Iran. In this experiment, irrigation was the main factor in three levels, including drought stress based on 40-70-100 mm from the evaporation pan of class A. Biofertilizer including growth-promoting bacteria (Azospirillium) and mycorrhiza fungus in four levels, including a(Non-consumption) B (Seeds of growth-promoting bacteria (Azospirillium)) C (Consumption of mycorrhiza fungus as seeds) D (Concomitant use of growth-promoting bacteria Azospirillium with mycorrhiza fungi as seeds) and ascorbic acid in two levels of foliar application, including A (Absence Application of ascorbic acid) and B (Application of ascorbic acid (two days after irrigation treatment)) was considered as a factorial factor. The results showed that the highest biological yield was obtained in drought stress of 40 mm and application of biological fertilizers in the form of mycorrhiza application with an average of 3307.1 kg/ha, which was about 70% more than 100 mm evaporation stress and no application of biological fertilizer. The use of ascorbic acid under drought stress conditions improved by 10%, the essential oil using ascorbic acid evaporated under drought stress conditions of 100 mm. As a general conclusion, the use of ascorbic acid and Mycorrhiza + Azospirillium biological fertilizer improved the quantitative and qualitative characteristics of basil under drought stress.
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Affiliation(s)
- M Hamidi
- Islamic Azad University, Varamin-Pishva Branch, College of Agriculture, Department of Agronomy, Varamin, Iran
| | - H Tohidi Moghadam
- Islamic Azad University, Varamin-Pishva Branch, College of Agriculture, Department of Agronomy, Varamin, Iran
| | - M Nasri
- Islamic Azad University, Varamin-Pishva Branch, College of Agriculture, Department of Agronomy, Varamin, Iran
| | - P Kasraie
- Islamic Azad University, Varamin-Pishva Branch, College of Agriculture, Department of Agronomy, Varamin, Iran
| | - H Larijani
- Islamic Azad University, Varamin-Pishva Branch, College of Agriculture, Department of Agronomy, Varamin, Iran
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Shah SY, Larijani H, Gibson RM, Liarokapis D. Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals. Sensors (Basel) 2022; 22:s22072466. [PMID: 35408080 PMCID: PMC9002775 DOI: 10.3390/s22072466] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/14/2022] [Accepted: 03/17/2022] [Indexed: 06/12/2023]
Abstract
Epileptic seizures are caused by abnormal electrical activity in the brain that manifests itself in a variety of ways, including confusion and loss of awareness. Correct identification of epileptic seizures is critical in the treatment and management of patients with epileptic disorders. One in four patients present resistance against seizures episodes and are in dire need of detecting these critical events through continuous treatment in order to manage the specific disease. Epileptic seizures can be identified by reliably and accurately monitoring the patients' neuro and muscle activities, cardiac activity, and oxygen saturation level using state-of-the-art sensing techniques including electroencephalograms (EEGs), electromyography (EMG), electrocardiograms (ECGs), and motion or audio/video recording that focuses on the human head and body. EEG analysis provides a prominent solution to distinguish between the signals associated with epileptic episodes and normal signals; therefore, this work aims to leverage on the latest EEG dataset using cutting-edge deep learning algorithms such as random neural network (RNN), convolutional neural network (CNN), extremely random tree (ERT), and residual neural network (ResNet) to classify multiple variants of epileptic seizures from non-seizures. The results obtained highlighted that RNN outperformed all other algorithms used and provided an overall accuracy of 97%, which was slightly improved after cross validation.
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Affiliation(s)
- Syed Yaseen Shah
- School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK; (R.M.G.); (D.L.)
| | - Hadi Larijani
- SMART Technology Research Centre, Glasgow Caledonian University, Cowcaddens Road, Glasgow G4 0BA, UK
| | - Ryan M. Gibson
- School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK; (R.M.G.); (D.L.)
| | - Dimitrios Liarokapis
- School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK; (R.M.G.); (D.L.)
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Dashtipour K, Gogate M, Adeel A, Larijani H, Hussain A. Sentiment Analysis of Persian Movie Reviews Using Deep Learning. Entropy (Basel) 2021; 23:596. [PMID: 34066133 PMCID: PMC8151596 DOI: 10.3390/e23050596] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 05/03/2021] [Accepted: 05/04/2021] [Indexed: 02/07/2023]
Abstract
Sentiment analysis aims to automatically classify the subject's sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, news, etc. Deep learning has recently emerged as a powerful machine learning technique to tackle the growing demand for accurate sentiment analysis. However, the majority of research efforts are devoted to English-language only, while information of great importance is also available in other languages. This paper presents a novel, context-aware, deep-learning-driven, Persian sentiment analysis approach. Specifically, the proposed deep-learning-driven automated feature-engineering approach classifies Persian movie reviews as having positive or negative sentiments. Two deep learning algorithms, convolutional neural networks (CNN) and long-short-term memory (LSTM), are applied and compared with our previously proposed manual-feature-engineering-driven, SVM-based approach. Simulation results demonstrate that LSTM obtained a better performance as compared to multilayer perceptron (MLP), autoencoder, support vector machine (SVM), logistic regression and CNN algorithms.
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Affiliation(s)
- Kia Dashtipour
- Department of Computing Science and Mathematics, University of Stirling, Stirling FK9 4LA, UK
| | - Mandar Gogate
- School of Computing, Edinburgh Napier University, Edinburgh EH11 4BN, UK; (M.G.); (A.H.)
| | - Ahsan Adeel
- School of Mathematics and Computer Science, University of Wolverhampton, Wolverhampton WV1 1LY, UK;
| | - Hadi Larijani
- School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK;
| | - Amir Hussain
- School of Computing, Edinburgh Napier University, Edinburgh EH11 4BN, UK; (M.G.); (A.H.)
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Ahmad J, Larijani H, Emmanuel R, Mannion M, Javed A. Occupancy detection in non-residential buildings – A survey and novel privacy preserved occupancy monitoring solution. ACI 2020. [DOI: 10.1016/j.aci.2018.12.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Buildings use approximately 40% of global energy and are responsible for almost a third of the worldwide greenhouse gas emissions. They also utilise about 60% of the world’s electricity. In the last decade, stringent building regulations have led to significant improvements in the quality of the thermal characteristics of many building envelopes. However, similar considerations have not been paid to the number and activities of occupants in a building, which play an increasingly important role in energy consumption, optimisation processes, and indoor air quality. More than 50% of the energy consumption could be saved in Demand Controlled Ventilation (DCV) if accurate information about the number of occupants is readily available (Mysen et al., 2005). But due to privacy concerns, designing a precise occupancy sensing/counting system is a highly challenging task. While several studies count the number of occupants in rooms/zones for the optimisation of energy consumption, insufficient information is available on the comparison, analysis and pros and cons of these occupancy estimation techniques. This paper provides a review of occupancy measurement techniques and also discusses research trends and challenges. Additionally, a novel privacy preserved occupancy monitoring solution is also proposed in this paper. Security analyses of the proposed scheme reveal that the new occupancy monitoring system is privacy preserved compared to other traditional schemes.
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Affiliation(s)
- Jawad Ahmad
- School of Computing, Edinburgh Napier University, Edinburgh, United Kingdom
| | - Ahsen Tahir
- Department of Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan
| | - Hadi Larijani
- School of Computing Engineering and Built Environment, Glasgow Caledonian University, Glasgow, United Kingdom
| | - Fawad Ahmed
- Department of Electrical Engineering, HITEC University Taxila, Pakistan
| | - Syed Aziz Shah
- School of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK
| | - Adam James Hall
- School of Computing, Edinburgh Napier University, Edinburgh, United Kingdom
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Javed A, Larijani H, Ahmadinia A, Emmanuel R, Gibson D, Clark C. Experimental testing of a random neural network smart controller using a single zone test chamber. IET Networks 2015. [DOI: 10.1049/iet-net.2015.0020] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Affiliation(s)
- Abbas Javed
- School of Engineering and Built EnvironmentGlasgow Caledonian UniversityGlasgowUK
| | - Hadi Larijani
- School of Engineering and Built EnvironmentGlasgow Caledonian UniversityGlasgowUK
| | - Ali Ahmadinia
- School of Engineering and Built EnvironmentGlasgow Caledonian UniversityGlasgowUK
| | - Rohinton Emmanuel
- School of Engineering and Built EnvironmentGlasgow Caledonian UniversityGlasgowUK
| | - Des Gibson
- Gas Sensing Solutions Ltd60 Grayshill Road, Westfield North CourtyardGlasgowG68 9HQUK
- Institute of Thin Films, Sensors and Imaging, Scottish Universities Physics Alliance, University of the West of ScotlandPaisleyPA12BEUK
| | - Caspar Clark
- Gas Sensing Solutions Ltd60 Grayshill Road, Westfield North CourtyardGlasgowG68 9HQUK
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