Journal of Electrical Systems and Information Technology最新文献_第3页

Houseplant leaf classification system based on deep learning algorithms 基于深度学习算法的植物叶片分类系统

Journal of Electrical Systems and Information Technology Pub Date : 2024-04-07 DOI: 10.1186/s43067-024-00141-5

Hersh M. Hama, Taib Sh. Abdulsamad, Saman M. Omer

{"title":"Houseplant leaf classification system based on deep learning algorithms","authors":"Hersh M. Hama, Taib Sh. Abdulsamad, Saman M. Omer","doi":"10.1186/s43067-024-00141-5","DOIUrl":"https://doi.org/10.1186/s43067-024-00141-5","url":null,"abstract":"Botanical experts are typically relied upon to classify houseplants since even subtle differences in characteristics such as leaves can distinguish one species from another. Therefore, an automated system for recognizing houseplant leaves with accuracy and reliability becomes a valuable asset for the identification of indoor plant species. In this paper, a houseplant leaf classification system utilizing deep learning algorithms is proposed, which has been improved to effectively classify and identify a variety of houseplant leaf types. The system uses the ResNet-50 architecture based on convolutional neural network to analyze features of the leaf images and extract relevant information for classification. In addition, this work presents a newly constructed local dataset consisting of 2500 images to classify species of houseplant leaves. The dataset includes ten types of houseplant leaves that are suitable for cultivation in various climates at home. The dataset was augmented using data augmentation algorithms to increase its size and reduce overfitting. The developed system was training and testing using a local dataset. To evaluate the improved model, comparative experiments were conducted utilizing pre-trained models (original ResNet-50 and MobileNet_v2). The improved model revealed recognition accuracy of 99% with the augmented dataset and 98.60% without the augmentation, affirming its effectiveness. The improved model could potentially be used in various fields, including horticulture, plant pathology, and environmental monitoring to identify plant species.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"204 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Early prediction of chronic kidney disease based on ensemble of deep learning models and optimizers 基于深度学习模型和优化器集合的慢性肾病早期预测

Journal of Electrical Systems and Information Technology Pub Date : 2024-04-03 DOI: 10.1186/s43067-024-00142-4

Dina Saif, Amany M. Sarhan, Nada M. Elshennawy

{"title":"Early prediction of chronic kidney disease based on ensemble of deep learning models and optimizers","authors":"Dina Saif, Amany M. Sarhan, Nada M. Elshennawy","doi":"10.1186/s43067-024-00142-4","DOIUrl":"https://doi.org/10.1186/s43067-024-00142-4","url":null,"abstract":"Recent studies have proven that data analytics may assist in predicting events before they occur, which may impact the outcome of current situations. In the medical sector, it has been utilized for predicting the likelihood of getting a health condition such as chronic kidney disease (CKD). This paper aims at developing a CKD prediction framework, which forecasts CKD occurrence over a specific time using deep learning and deep ensemble learning approaches. While a great deal of research focuses on disease detection, few studies contribute to disease prediction before it may occur. However, the performance of previous work was not competitive. This paper tackles the under-explored area of early CKD prediction through a high-performing deep learning and ensemble framework. We bridge the gap between existing detection methods and preventive interventions by: developing and comparing deep learning models like CNN, LSTM, and LSTM-BLSTM for 6–12 month CKD prediction; addressing data imbalance, feature selection, and optimizer optimization; and building an ensemble model combining the best individual models (CNN-Adamax, LSTM-Adam, and LSTM-BLSTM-Adamax). Our framework achieves significantly higher accuracy (98% and 97% for 6 and 12 months) than previous work, paving the way for earlier diagnosis and improved patient outcomes.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140569267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced resource allocation strategies to improve the spectral efficiency in massive MIMO systems 提高大规模多输入多输出系统频谱效率的增强型资源分配策略

Journal of Electrical Systems and Information Technology Pub Date : 2024-03-08 DOI: 10.1186/s43067-023-00132-y

Angelina Misso

{"title":"Enhanced resource allocation strategies to improve the spectral efficiency in massive MIMO systems","authors":"Angelina Misso","doi":"10.1186/s43067-023-00132-y","DOIUrl":"https://doi.org/10.1186/s43067-023-00132-y","url":null,"abstract":"The accuracy of the channel state information is important for correct channel estimation. However, when conducting channel estimation, more resources are allocated to pilots for estimation compared to data transmission. Furthermore, when the number of users increases, the number of pilots for estimation increases. Subsequently, there is an increase in the transmission overhead and hence reduces the spectral efficiency. Therefore, the advantage of obtaining channel state information is significantly reduced. To improve the performance of massive MIMO systems, the study analyses the tradeoff between the number of resources required to correctly estimate the channel using pilots to avoid interference while maintaining optimum spectral efficiency in massive MIMO antennas. Therefore, this study proposes an algorithm to address the challenge of optimum resource allocation in a massive MIMO. Pilot Frequency reuse, max–min fairness algorithm, and Zadoff–Chu sequences were adopted to achieve optimal allocation of resources and reduce interference for users in different cells using the same frequencies. The results reveal improved performance in terms of spectral efficiency with the adoption of the resource optimization approach. The study contributes to the performance improvement of massive MIMO antennas for 5 G communications.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"105 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140070696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and development of an IoT-based intelligent water quality management system for aquaculture 设计和开发基于物联网的水产养殖智能水质管理系统

Journal of Electrical Systems and Information Technology Pub Date : 2024-03-06 DOI: 10.1186/s43067-024-00139-z

Olumide Oluseye Olanubi, Theddeus Tochukwu Akano, Olumuyiwa Sunday Asaolu

引用次数: 0

Design and implementation of domestic dual-SIM telesecurity alarm system using voice code recognition 利用声码识别设计和实施国内双 SIM 卡远程安全警报系统

Journal of Electrical Systems and Information Technology Pub Date : 2024-03-05 DOI: 10.1186/s43067-024-00140-6

Johnpaul Uzozie Okafor, Akinyinka Olukunle Akande, Cosmas Kemdirim Agubor

{"title":"Design and implementation of domestic dual-SIM telesecurity alarm system using voice code recognition","authors":"Johnpaul Uzozie Okafor, Akinyinka Olukunle Akande, Cosmas Kemdirim Agubor","doi":"10.1186/s43067-024-00140-6","DOIUrl":"https://doi.org/10.1186/s43067-024-00140-6","url":null,"abstract":"Violent crime cases which include, robbery, rape, and homicide are terribly on the rise, and the role of security in combating this menace cannot be overemphasized. This research presents a security device that aims at fighting violent crimes using voice recognition technology. The work also tends to solve the issue of network downtime when the user is out of reach for help in time of attack. In this work, a voice processing unit which comprises the condenser microphone, an amplifier, a shift register, and a timer was designed. The processing unit circuit was incorporated into microcontrollers which create Human-Device interaction and the GSM communication unit which is made up of two GSM modules. The two microcontrollers used in the design are PIC18F4520 and PIC16F873A. The microcontrollers were programmed with C++ using the MPLAB IDE software and the circuit simulation was done using Proteus Design Suite version 8. The result shows that the appropriate authority receives SMS whenever the pre-recorded code is mentioned. The result also shows that during network downtime, the second GSM module sends an SMS to the appropriate authority. Evaluating the performance of this work, it was observed that the device works best in a calm area compared to a noisy area. This work is designed to work in domestic areas like homes, offices, malls, and mainly areas free from so much noise. Therefore, this work has successfully reduced the crime rate in emergencies.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140032878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sensor placement algorithm for faults detection in electrical secondary distribution network using dynamic programming method: focusing on dynamic change and expansion of the network configurations 使用动态编程方法的二次配电网络故障检测传感器安置算法：关注网络配置的动态变化和扩展

Journal of Electrical Systems and Information Technology Pub Date : 2024-02-23 DOI: 10.1186/s43067-024-00135-3

Daudi Charles Mnyanghwalo, Shamte Juma Kawambwa

{"title":"Sensor placement algorithm for faults detection in electrical secondary distribution network using dynamic programming method: focusing on dynamic change and expansion of the network configurations","authors":"Daudi Charles Mnyanghwalo, Shamte Juma Kawambwa","doi":"10.1186/s43067-024-00135-3","DOIUrl":"https://doi.org/10.1186/s43067-024-00135-3","url":null,"abstract":"Modern power grids are developing toward smartness through the use of sensors in gathering data for situation awareness, visibility, and fault detection. In most developing countries, fault detection in the electrical secondary distribution network (SDN) is very challenging due to the lack of automated systems for network monitoring. Systems for monitoring faults require sensor placement on each node, which is not economically feasible. Hence, optimal placement algorithms are required to ensure that the network is observable with few sensors possible. The existing sensor placement methods based on mathematical and heuristic approaches are efficient for transmission and primary distribution networks which are mostly static in size and layout. Such methods may not be efficient in SDN which is dynamic in size and have a relatively large number of nodes. This study proposes an enhanced dynamic programming method for sensor placement to enhance fault detection in SDN. The proposed algorithm employs the depth search concepts and the parent–children relationship between nodes to determine sensor types and locations considering the optimal cost. The proposed algorithm was compared with other methods including particle swarm optimization, genetic algorithm, and chaotic crow search algorithm using different network configurations. The results revealed that the proposed algorithm suggested the minimum number of sensors and shortest convergence time of 1.27 min. The results also revealed that, on network expansion, maintaining the location of the existing sensors is more cost-effective by 20% than reallocating the existing sensors. Furthermore, the results revealed that an average of 30% of nodes, need sensors to observe the entire network, hence cost optimization.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139952075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparing pre-trained models for efficient leaf disease detection: a study on custom CNN 比较预训练模型以实现高效叶病检测：对定制 CNN 的研究

Journal of Electrical Systems and Information Technology Pub Date : 2024-02-23 DOI: 10.1186/s43067-024-00137-1

Touhidul Seyam Alam, Chandni Barua Jowthi, Abhijit Pathak

{"title":"Comparing pre-trained models for efficient leaf disease detection: a study on custom CNN","authors":"Touhidul Seyam Alam, Chandni Barua Jowthi, Abhijit Pathak","doi":"10.1186/s43067-024-00137-1","DOIUrl":"https://doi.org/10.1186/s43067-024-00137-1","url":null,"abstract":"Leaf disease detection is a crucial task in modern agriculture, aiding in early diagnosis and prevention of crop infections. In this research paper, authors present a comprehensive study comparing nine widely used pre-trained models, namely DenseNet201, EfficientNetB3, EfficientNetB4, InceptionResNetV2, MobileNetV2, ResNet50, ResNet152, VGG16, and Xception, with our newly developed custom CNN (Convolutional Neural Network) for leaf disease detection. The objective is to determine if our custom CNN can match the performance of these established pre-trained models while maintaining superior efficiency. The authors trained and fine-tuned each pre-trained model and our custom CNN on a large dataset of labeled leaf images, covering various diseases and healthy states. Subsequently, the authors evaluated the models using standard metrics, including accuracy, precision, recall, and F1-score, to gauge their overall performance. Additionally, the authors analyzed computational efficiency regarding training time and memory consumption. Surprisingly, our results indicate that the custom CNN performs comparable to the pre-trained models, despite their sophisticated architectures and extensive pre-training on massive datasets. Moreover, our custom CNN demonstrates superior efficiency, outperforming the pre-trained models regarding training speed and memory requirements. These findings highlight the potential of custom CNN architectures for leaf disease detection tasks, offering a compelling alternative to the commonly used pre-trained models. The efficiency gains achieved by our custom CNN can be beneficial in resource-constrained environments, enabling faster inference and deployment of leaf disease detection systems. Overall, our research contributes to the advancement of agricultural technology by presenting a robust and efficient solution for the early detection of leaf diseases, thereby aiding in crop protection and yield enhancement.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139956387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CNN-optimized text recognition with binary embeddings for Arabic expiry date recognition 采用二进制嵌入的 CNN 优化文本识别，用于阿拉伯文有效期识别

Journal of Electrical Systems and Information Technology Pub Date : 2024-02-21 DOI: 10.1186/s43067-024-00136-2

Mohamed Lotfy, Ghada Soliman

{"title":"CNN-optimized text recognition with binary embeddings for Arabic expiry date recognition","authors":"Mohamed Lotfy, Ghada Soliman","doi":"10.1186/s43067-024-00136-2","DOIUrl":"https://doi.org/10.1186/s43067-024-00136-2","url":null,"abstract":"Recognizing Arabic dot-matrix digits is a challenging problem due to the unique characteristics of dot-matrix fonts, such as irregular dot spacing and varying dot sizes. This paper presents an approach for recognizing Arabic digits printed in dot matrix format. The proposed model is based on convolutional neural networks (CNN) that take the dot matrix as input and generate embeddings that are rounded to generate binary representations of the digits. The binary embeddings are then used to perform Optical Character Recognition (OCR) on the date images. To overcome the challenge of the limited availability of dotted Arabic expiration date images, we developed a True Type Font (TTF) for generating synthetic images of Arabic dot-matrix characters. The model was trained on a synthetic dataset of 3287 images and 658 synthetic images for testing, representing realistic expiration dates from 2019 to 2027 in the format of yyyy/mm/dd and yy/mm/dd. Our model achieved an accuracy of 98.94% on the expiry date recognition with Arabic dot matrix format using fewer parameters and less computational resources than traditional CNN-based models. By investigating and presenting our findings comprehensively, we aim to contribute substantially to the field of OCR and pave the way for advancements in Arabic dot-matrix character recognition. Our proposed approach is not limited to Arabic dot matrix digit recognition but can be also extended to text recognition tasks, such as text classification and sentiment analysis.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"164 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139925143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved-RSSI-based indoor localization by using pseudo-linear solution with machine learning algorithms 利用机器学习算法的伪线性解决方案改进基于 RSSI 的室内定位功能

Journal of Electrical Systems and Information Technology Pub Date : 2024-02-17 DOI: 10.1186/s43067-024-00138-0

M. W. P. Maduranga, Valmik Tilwari, Ruvan Abeysekera

{"title":"Improved-RSSI-based indoor localization by using pseudo-linear solution with machine learning algorithms","authors":"M. W. P. Maduranga, Valmik Tilwari, Ruvan Abeysekera","doi":"10.1186/s43067-024-00138-0","DOIUrl":"https://doi.org/10.1186/s43067-024-00138-0","url":null,"abstract":"With the rapid advancement of the Internet of Things and the popularization of mobile Internet-based applications, the location-based service (LBS) has attracted much attention from commercial developers and researchers. Received signal strength indicator (RSSI)-based indoor localization technology has irreplaceable advantages for many LBS applications. However, due to multipath fading, noise, and the limited dynamic range of the RSSI measurements, precise localization based on a path-loss model and multiliterate becomes highly challenging. Therefore, this study proposes a machine learning (ML)-based improved RSSI-based indoor localization approach in which RSSI data is first augmented and then classified using ML algorithms. In addition, we implement an experimental testbed to collect the RSSI value based on Wi-Fi using various reference and target nodes. The received RSSI measurements undergo pre-processing using pseudo-linear solution techniques for closed-form solutions, approximating the original system of nonlinear RSSI measurement equations with a system of linear equations. Finally, the RSSI measurement are trained using ML models such as linear regression, polynomial regression, support vector regression, random forest regression, and decision tree regression. Consequently, the experimental results express in terms of root mean square error and coefficient of determinant compared with various machine learning models with hyper-parameter tuning.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139761601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generalized antenna sequence spatial modulation (GASSM): a novel framework for enhanced PHY layer security and energy efficiency 广义天线序列空间调制（GASSM）：增强物理层安全性和能效的新型框架

Journal of Electrical Systems and Information Technology Pub Date : 2024-01-29 DOI: 10.1186/s43067-023-00133-x

Joseph A. Obadha, Peter O. Akuon, Vitalis K. Oduol

{"title":"Generalized antenna sequence spatial modulation (GASSM): a novel framework for enhanced PHY layer security and energy efficiency","authors":"Joseph A. Obadha, Peter O. Akuon, Vitalis K. Oduol","doi":"10.1186/s43067-023-00133-x","DOIUrl":"https://doi.org/10.1186/s43067-023-00133-x","url":null,"abstract":"In antenna sequence spatial modulation (ASSM), the spatial and symbol domains are both used to encode information. The sequencing of the antennas’ transmissions within given time slots and the symbol carried in those time slots represent information in the spatial and symbol domains, respectively. The receiver’s task is to decode this sequence of transmission and to detect the symbol carried in the time slots, equal to the number of antennas. For better energy efficiency and greater security, we propose a generalized ASSM, GASSM. In the GASSM, while the information is contained in both the sequence and the symbols transmitted, different symbols can be transmitted within the time slots forming the frame. This expands the code domain since the symbol and the spatial information represent a longer code in the mapper table. As a special case, we present the case of three transmit antennas, transmitting within three time slots. A combination of the antennas’ sequence and the symbol carried in the time slots is used as a code for a bit sequence. Results obtained from simulation and analysis of the bit error rate performance and secrecy capacity are contrasted and presented. Comparing the GASSM to the standard spatial modulation (SM) and the ASSM, we deduce that the GASSM has a higher secrecy capacity and higher energy efficiency per bit and unlike conventional SM allows utilization of odd number of antennas.","PeriodicalId":100777,"journal":{"name":"Journal of Electrical Systems and Information Technology","volume":"106 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139583617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0