Parkinson’s disease (PD) is one of the reformed brain syndromes that results in unintended stiffness and difficulty with balance and dexterity. To detect PD in medical scenery, physicians commonly use experimental indicators like motorized and non-motor symptoms and the severity rating depends on the unified PD Rating Scale (UPDRS). However, these medical assessments highly rely on expertized clinicians and lead to inter-variability discrepancies. Nowadays, gait sensor data assists doctors in diagnosing PD and estimates the severity level of gait abnormalities in patients. However, the gait sensor data increases the dimensionality issues and is subjected to high non-linear complexity. Hence, this study suggests an innovative deep learning (DL) technique for accurate PD analysis using gait patterns. Initially, the gait sensor data is preprocessed by performing data cleaning, and decimal scaling normalization (DS- Norm) to enhance the data quality. The Hoehn and Yahr (H&Y) scale is a commonly used rating scale for measuring the progression of Parkinson's disease symptoms. It's typically used to assess motor symptoms like tremors, rigidity, and bradykinesia. The scale ranges from 0 to 5, with higher numbers indicating more severe symptoms and disability. The preprocessed data are then fed into the proposed Duo spatiotemporal convoluted kernel boosted ResNet (DSCK-RNet) model for classifying the PD severity rating by learning the gait spatiotemporal features. The developed method is processed and scrutinized via the Python platform and a publicly available Physio- Net dataset is utilized for the simulation process. Various assessment measures like accuracy, precision, sensitivity, specificity, PPV, FPR, and MCC are examined and compared with traditional studies. In the experimental section, the developed DSCK-RNet model achieved an accuracy of 100%, 99.6%, 99%, and 99.64% for different classes like healthy, severity-2, severity-2.5, and severity-3 respectively. Compared to the conventional techniques, our suggested approach performs better. The experimental findings demonstrate the clinical significance of the suggested approach for the impartial evaluation of gait motor impairment in PD patients.
帕金森病（Parkinson's disease，PD）是一种脑部综合征，会导致患者出现意外的僵硬、平衡和灵活性困难。为了在医疗景象中发现帕金森病，医生通常使用运动症状和非运动症状等实验指标，并根据统一的帕金森病评分量表（UPDRS）对严重程度进行评级。然而，这些医学评估高度依赖于专业的临床医生，并导致变量间的差异。如今，步态传感器数据可协助医生诊断帕金森病，并估计患者步态异常的严重程度。然而，步态传感器数据会增加维度问题，并具有较高的非线性复杂性。因此，本研究提出了一种创新的深度学习（DL）技术，利用步态模式准确分析帕金森病。首先，对步态传感器数据进行预处理，包括数据清理和十进制缩放归一化（DS- Norm），以提高数据质量。Hoehn and Yahr（H&Y）量表是测量帕金森病症状进展的常用评分量表。它通常用于评估震颤、僵直和运动迟缓等运动症状。该量表的范围从 0 到 5，数字越大，表示症状和残疾程度越严重。预处理后的数据被输入到所提出的 Duo spatiotemporal convoluted kernel boosted ResNet（DSCK-RNet）模型中，通过学习步态时空特征来对帕金森病的严重程度进行分类。所开发的方法通过 Python 平台进行处理和检查，并利用公开的 Physio- Net 数据集进行模拟。对准确度、精确度、灵敏度、特异性、PPV、FPR 和 MCC 等各种评估指标进行了检查，并与传统研究进行了比较。在实验部分，针对健康、严重程度-2、严重程度-2.5 和严重程度-3 等不同类别，所开发的 DSCK-RNet 模型的准确率分别达到了 100%、99.6%、99% 和 99.64%。与传统技术相比，我们建议的方法表现更好。实验结果表明，建议的方法对于公正评估帕金森病患者的步态运动障碍具有重要的临床意义。
{"title":"Data-driven Gait based Severity Classification for Parkinson's Disease using Duo Spatiotemporal Convoluted Kernel Boosted ResNet model","authors":"Arogia Victor Paul M, Sharmila Sankar","doi":"10.32985/ijeces.15.4.8","DOIUrl":"https://doi.org/10.32985/ijeces.15.4.8","url":null,"abstract":"Parkinson’s disease (PD) is one of the reformed brain syndromes that results in unintended stiffness and difficulty with balance and dexterity. To detect PD in medical scenery, physicians commonly use experimental indicators like motorized and non-motor symptoms and the severity rating depends on the unified PD Rating Scale (UPDRS). However, these medical assessments highly rely on expertized clinicians and lead to inter-variability discrepancies. Nowadays, gait sensor data assists doctors in diagnosing PD and estimates the severity level of gait abnormalities in patients. However, the gait sensor data increases the dimensionality issues and is subjected to high non-linear complexity. Hence, this study suggests an innovative deep learning (DL) technique for accurate PD analysis using gait patterns. Initially, the gait sensor data is preprocessed by performing data cleaning, and decimal scaling normalization (DS- Norm) to enhance the data quality. The Hoehn and Yahr (H&Y) scale is a commonly used rating scale for measuring the progression of Parkinson's disease symptoms. It's typically used to assess motor symptoms like tremors, rigidity, and bradykinesia. The scale ranges from 0 to 5, with higher numbers indicating more severe symptoms and disability. The preprocessed data are then fed into the proposed Duo spatiotemporal convoluted kernel boosted ResNet (DSCK-RNet) model for classifying the PD severity rating by learning the gait spatiotemporal features. The developed method is processed and scrutinized via the Python platform and a publicly available Physio- Net dataset is utilized for the simulation process. Various assessment measures like accuracy, precision, sensitivity, specificity, PPV, FPR, and MCC are examined and compared with traditional studies. In the experimental section, the developed DSCK-RNet model achieved an accuracy of 100%, 99.6%, 99%, and 99.64% for different classes like healthy, severity-2, severity-2.5, and severity-3 respectively. Compared to the conventional techniques, our suggested approach performs better. The experimental findings demonstrate the clinical significance of the suggested approach for the impartial evaluation of gait motor impairment in PD patients.","PeriodicalId":507791,"journal":{"name":"International journal of electrical and computer engineering systems","volume":"75 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140371444","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}
The battery's SoC is a crucial variable since it reflects its performance. An accurate estimation of SoC protects the battery, prevents overcharging or discharge, and extends its life time. Since most of the traditional methods use complex equations, ANN has been implemented to reduce the complications and provide better accuracy. In this research, Li-NMC with capacity rating of 2000mAh is used for the estimation of SoC. In this paper, Feedforward Neural Network (FNN) algorithm and Nonlinear Auto-Regressive network with exogenous inputs (NARX) have been used for designing a neural network model. Here, the performance matrixes of both neural network models have been compared and analyzed with the same dataset.
电池的 SoC 是一个关键变量，因为它反映了电池的性能。准确估算 SoC 可以保护电池，防止过度充电或放电，并延长其使用寿命。由于大多数传统方法都使用复杂的方程，因此采用了 ANN 来减少复杂性并提供更好的准确性。本研究使用额定容量为 2000mAh 的锂离子电池来估算 SoC。本文采用前馈神经网络（FNN）算法和外生输入非线性自回归网络（NARX）来设计神经网络模型。本文使用相同的数据集对两种神经网络模型的性能矩阵进行了比较和分析。
{"title":"Measurement of State of Charge of Lithium-Nickel Manganese Cobalt Battery using Artificial Neural Network and NARX Algorithm","authors":"Divya. R, K. K, R. S, Raja. S.P","doi":"10.32985/ijeces.15.4.1","DOIUrl":"https://doi.org/10.32985/ijeces.15.4.1","url":null,"abstract":"The battery's SoC is a crucial variable since it reflects its performance. An accurate estimation of SoC protects the battery, prevents overcharging or discharge, and extends its life time. Since most of the traditional methods use complex equations, ANN has been implemented to reduce the complications and provide better accuracy. In this research, Li-NMC with capacity rating of 2000mAh is used for the estimation of SoC. In this paper, Feedforward Neural Network (FNN) algorithm and Nonlinear Auto-Regressive network with exogenous inputs (NARX) have been used for designing a neural network model. Here, the performance matrixes of both neural network models have been compared and analyzed with the same dataset.","PeriodicalId":507791,"journal":{"name":"International journal of electrical and computer engineering systems","volume":"19 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140373047","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}
Siluvai M. Michael, Bokani Mtengi, S.R.S. Prabaharan, Adamu Murtala Zungeru, James Garba Ambafi
Vehicles are part of urban area transport and are subjected to variable loads as they traverse the city with varying slopes and stop-and-go traffic. Electric Vehicles (EVs) can be a good option because of their high efficiency under stop-and-go conditions and ability to gain energy from braking. However, limited battery energy makes EVs less efficient and degrades their lifetime. In contrast to a Li-Ion battery, supercapacitors work well under high power charge and discharge cycles. However, their high cost and low energy density prevent them from being viable replacements for batteries. Due to the slow charging and discharging process of batteries, they have a low power density, but a high energy density compared to the supercapacitor. In this paper, we discussed our system design consisting of both a battery and a supercapacitor. The main aim is to design and develop a scheduling algorithm to optimize energy flow between the battery, supercapacitor, and motor. We further described an analogue-based control methodology and algorithm for the supercapacitor, augmented battery-powered motoring process. This is in addition to a charge controller designed to optimize the supercapacitor bank's current-based charge-discharge profile. The system design and tests are developed on PSPICE and a hardware platform.
{"title":"Design of Regenerative Braking System and Energy Storage with Supercapacitors as Energy Buffers","authors":"Siluvai M. Michael, Bokani Mtengi, S.R.S. Prabaharan, Adamu Murtala Zungeru, James Garba Ambafi","doi":"10.32985/ijeces.15.4.3","DOIUrl":"https://doi.org/10.32985/ijeces.15.4.3","url":null,"abstract":"Vehicles are part of urban area transport and are subjected to variable loads as they traverse the city with varying slopes and stop-and-go traffic. Electric Vehicles (EVs) can be a good option because of their high efficiency under stop-and-go conditions and ability to gain energy from braking. However, limited battery energy makes EVs less efficient and degrades their lifetime. In contrast to a Li-Ion battery, supercapacitors work well under high power charge and discharge cycles. However, their high cost and low energy density prevent them from being viable replacements for batteries. Due to the slow charging and discharging process of batteries, they have a low power density, but a high energy density compared to the supercapacitor. In this paper, we discussed our system design consisting of both a battery and a supercapacitor. The main aim is to design and develop a scheduling algorithm to optimize energy flow between the battery, supercapacitor, and motor. We further described an analogue-based control methodology and algorithm for the supercapacitor, augmented battery-powered motoring process. This is in addition to a charge controller designed to optimize the supercapacitor bank's current-based charge-discharge profile. The system design and tests are developed on PSPICE and a hardware platform.","PeriodicalId":507791,"journal":{"name":"International journal of electrical and computer engineering systems","volume":"125 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140370106","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}
Reliability is very important in the world of electronic device design and production, particularly in applications where continuous and flawless performance is a necessity. This directs our attention to the boost converter, which forms the foundation of power electronics, renewable energy systems, and electric vehicles. However, as technology progresses, the choice of materials for these converters is a big challenge. For that, in this paper, the impact of using Silicon Carbide (SiC) devices, with their promising material properties, on the reliability of boost converters is presented. Because the results showed that more than 80% of boost converter failures are caused by semiconductors, the use of SiC materials is assessed by determining its reliability using MIL-HDBK-217 standard. In addition, a comparative study with the use of traditional Silicon (Si) is conducted. The results showed that the failure rate of boost converters based on SiC devices reduced from 8.335 failure/10-6h to 6.243 failure/10-6h. This notable shift in failure rates establishes SiC as a pivotal material in the evolution of boost converter technology, offering a compelling solution to address the persistent challenges associated with semiconductor-related failures.
{"title":"Boosting Reliability","authors":"Elaid Bouchetob, Bouchra Nadji","doi":"10.32985/ijeces.15.4.2","DOIUrl":"https://doi.org/10.32985/ijeces.15.4.2","url":null,"abstract":"Reliability is very important in the world of electronic device design and production, particularly in applications where continuous and flawless performance is a necessity. This directs our attention to the boost converter, which forms the foundation of power electronics, renewable energy systems, and electric vehicles. However, as technology progresses, the choice of materials for these converters is a big challenge. For that, in this paper, the impact of using Silicon Carbide (SiC) devices, with their promising material properties, on the reliability of boost converters is presented. Because the results showed that more than 80% of boost converter failures are caused by semiconductors, the use of SiC materials is assessed by determining its reliability using MIL-HDBK-217 standard. In addition, a comparative study with the use of traditional Silicon (Si) is conducted. The results showed that the failure rate of boost converters based on SiC devices reduced from 8.335 failure/10-6h to 6.243 failure/10-6h. This notable shift in failure rates establishes SiC as a pivotal material in the evolution of boost converter technology, offering a compelling solution to address the persistent challenges associated with semiconductor-related failures.","PeriodicalId":507791,"journal":{"name":"International journal of electrical and computer engineering systems","volume":"27 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140372359","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}
Ranjita Akash Asati, M. M. Raghuwanshi, K. R. Singh
In numerous clinical applications that support the diagnosis and treatment planning of a broad variety of disorders, medical image segmentation is essential. Medical picture segmentation using the Enhanced Extended Topological Active Net (EETAN) model has proven to be successful in correctly identifying structures. This study suggests a novel way to combine the best clustering techniques and parallel processing approaches to maximize the segmentation performance of the EETAN model. The Probabilistic Depth Search Optimization (PDSO) Algorithm, which makes the parallel searching technique to find the ideal contour set, is responsible for this. This work implements parallel processing and ideal clustering to improve the EETAN model's performance in medical image segmentation. Performance metrics like accuracy, precision, recall, dice similarity, and computational time are used for a comparison study. The results demonstrate the notable enhancements attained by employing parallel processing and effective clustering.
{"title":"Optimizing Enhanced Extended Topological Active Nets Model Using Parallel Processing","authors":"Ranjita Akash Asati, M. M. Raghuwanshi, K. R. Singh","doi":"10.32985/ijeces.15.4.4","DOIUrl":"https://doi.org/10.32985/ijeces.15.4.4","url":null,"abstract":"In numerous clinical applications that support the diagnosis and treatment planning of a broad variety of disorders, medical image segmentation is essential. Medical picture segmentation using the Enhanced Extended Topological Active Net (EETAN) model has proven to be successful in correctly identifying structures. This study suggests a novel way to combine the best clustering techniques and parallel processing approaches to maximize the segmentation performance of the EETAN model. The Probabilistic Depth Search Optimization (PDSO) Algorithm, which makes the parallel searching technique to find the ideal contour set, is responsible for this. This work implements parallel processing and ideal clustering to improve the EETAN model's performance in medical image segmentation. Performance metrics like accuracy, precision, recall, dice similarity, and computational time are used for a comparison study. The results demonstrate the notable enhancements attained by employing parallel processing and effective clustering.","PeriodicalId":507791,"journal":{"name":"International journal of electrical and computer engineering systems","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140373320","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}
This study presents a new method to improve the detection ability of a convolutional neural network (CNN) in pneumonia detection using chest X-ray images. Using Gray-Level Co-occurrence Matrix (GLCM) analysis, additional channels are added to the original image data provided by Guangzhou Children's Hospital in Guangzhou, China. The main goal is to design a lightweight, fully convolution network and increase its available information using GLCM. Performance analysis is performed on the new CNN model and GLCM-enhanced CNN model, and results are compared with Transfer Learning approaches. Various evaluation metrics, including accuracy, precision, recall, F1 score, and AUC-ROC, are used to evaluate the improved analysis performance of CNN. The results showed a significant increase in the ability of the model to detect pneumonia, with an accuracy of 99.57%. In addition, the study evaluates the descriptive properties of the CNN model by analyzing its decision process using Grad-CAM.
{"title":"Gray Level Co-occurrence Matrix based Fully Convolutional Neural Network Model for Pneumonia Detection","authors":"Shubhra Prakash, B. Ramamurthy","doi":"10.32985/ijeces.15.4.7","DOIUrl":"https://doi.org/10.32985/ijeces.15.4.7","url":null,"abstract":"This study presents a new method to improve the detection ability of a convolutional neural network (CNN) in pneumonia detection using chest X-ray images. Using Gray-Level Co-occurrence Matrix (GLCM) analysis, additional channels are added to the original image data provided by Guangzhou Children's Hospital in Guangzhou, China. The main goal is to design a lightweight, fully convolution network and increase its available information using GLCM. Performance analysis is performed on the new CNN model and GLCM-enhanced CNN model, and results are compared with Transfer Learning approaches. Various evaluation metrics, including accuracy, precision, recall, F1 score, and AUC-ROC, are used to evaluate the improved analysis performance of CNN. The results showed a significant increase in the ability of the model to detect pneumonia, with an accuracy of 99.57%. In addition, the study evaluates the descriptive properties of the CNN model by analyzing its decision process using Grad-CAM.","PeriodicalId":507791,"journal":{"name":"International journal of electrical and computer engineering systems","volume":"5 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140369401","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}
In recent years, there has been an increase in online education resources to help learners improve their skills. However, it is difficult to select the right course from available online education resources due to the demands and needs of learners with different knowledge domains. To solve this problem, an online course recommendation model has the important factor of enhancing learner's knowledge. Many existing recommendation systems (RS) use collaborative filtering (CF) to recommend courses to learners. The major problems with the Collaborative Filtering Recommendation System (CFRS) are the sparse preferences and the scalability of the data. According to the similarity of items, many recommendation models are proposed and developed, but none of these provide suggestions to users without their associations or preferences. We propose a deep hybrid model-online course recommendation (DHM-OCR) that uses high-level learner behavior and course objective features. We demonstrate the improvements and efficiency of the model for suggesting online e-learning courses. According to the analysis and evaluation results, it seems that our DHM-OCR outperforms the parallel research recommendation system. Experimental findings from online course data reveal that the suggested model and approach significantly improve classification accuracy and training efficiency, particularly limited available data.
{"title":"DHM-OCR","authors":"Sagar Mekala, Padma Tns, Rama Rao Tandu","doi":"10.32985/ijeces.15.4.5","DOIUrl":"https://doi.org/10.32985/ijeces.15.4.5","url":null,"abstract":"In recent years, there has been an increase in online education resources to help learners improve their skills. However, it is difficult to select the right course from available online education resources due to the demands and needs of learners with different knowledge domains. To solve this problem, an online course recommendation model has the important factor of enhancing learner's knowledge. Many existing recommendation systems (RS) use collaborative filtering (CF) to recommend courses to learners. The major problems with the Collaborative Filtering Recommendation System (CFRS) are the sparse preferences and the scalability of the data. According to the similarity of items, many recommendation models are proposed and developed, but none of these provide suggestions to users without their associations or preferences. We propose a deep hybrid model-online course recommendation (DHM-OCR) that uses high-level learner behavior and course objective features. We demonstrate the improvements and efficiency of the model for suggesting online e-learning courses. According to the analysis and evaluation results, it seems that our DHM-OCR outperforms the parallel research recommendation system. Experimental findings from online course data reveal that the suggested model and approach significantly improve classification accuracy and training efficiency, particularly limited available data.","PeriodicalId":507791,"journal":{"name":"International journal of electrical and computer engineering systems","volume":"128 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140369815","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}
Abdul Haris Rangkuti, Varyl Hasbi Athala, Sian Lun Lau, Rudi Aryanto
This study aims to compare and evaluate the performance of banana detection models utilizing deep learning techniques and the Darknet algorithm. The objective is to identify the most effective approach for accurately detecting bananas in various real- world scenarios. The analysis involves training and testing multiple models using different datasets and evaluating their performance based on precision, recall, and overall accuracy. The results provide valuable insights into the strengths and weaknesses of each approach, enabling researchers and practitioners to make informed decisions when implementing banana detection systems. To detect banana objects, several convolutional neural network (CNN) models were used, including MobileNetV2, YOLOv3-Nano, YOLO Fastest 1.1, YOLOv3-tiny-PRN, YOLOv4-tiny, YOLOv7, and DenseNet121-YOLOv3. The training process utilizes the Darknet algorithm to facilitate the identification of banana types/classes captured by a camera, resulting in an MP4 film file. In this research, various experiments were carried out using different CNN models. However, these six models achieve optimal accuracy above 80%. Among them, the YOLOv7 model shows the highest average accuracy (MAP) at 100%, followed by the small model YOLOv4 at 92%. Meanwhile, for performance measurements, the accuracy of the YOLOv4-tiny model was 87%, followed by the YOLOv7 model at 84%. In the banana fruit experiment, several models showed very good performance, such as recognition of the Ambon, Kepok, and Emas banana classes up to 100% using the YOLOv7 and YOLOv4-tiny models. The YOLOv7 model itself can recognize other banana classes up to 100% in the Barangan, Rjbulu, Uli, and Tanduk classes. Furthermore, theYOLOv4-tiny model can recognize other banana classes, up to 90% of the Barangan, Rjbulu, Rjsereh, and Uli banana types. Thus, this experiment provides very good average accuracy results on 2 CNN models, namely YOLOv7 and YOLOv4-tiny. Future research will involve grouping pictures of bananas, which produces different image shapes, so it requires a different way to recognize them. It is hoped that this research can become a basis for further research in this field.
{"title":"Comparative Analysis of Banana Detection Models","authors":"Abdul Haris Rangkuti, Varyl Hasbi Athala, Sian Lun Lau, Rudi Aryanto","doi":"10.32985/ijeces.15.4.6","DOIUrl":"https://doi.org/10.32985/ijeces.15.4.6","url":null,"abstract":"This study aims to compare and evaluate the performance of banana detection models utilizing deep learning techniques and the Darknet algorithm. The objective is to identify the most effective approach for accurately detecting bananas in various real- world scenarios. The analysis involves training and testing multiple models using different datasets and evaluating their performance based on precision, recall, and overall accuracy. The results provide valuable insights into the strengths and weaknesses of each approach, enabling researchers and practitioners to make informed decisions when implementing banana detection systems. To detect banana objects, several convolutional neural network (CNN) models were used, including MobileNetV2, YOLOv3-Nano, YOLO Fastest 1.1, YOLOv3-tiny-PRN, YOLOv4-tiny, YOLOv7, and DenseNet121-YOLOv3. The training process utilizes the Darknet algorithm to facilitate the identification of banana types/classes captured by a camera, resulting in an MP4 film file. In this research, various experiments were carried out using different CNN models. However, these six models achieve optimal accuracy above 80%. Among them, the YOLOv7 model shows the highest average accuracy (MAP) at 100%, followed by the small model YOLOv4 at 92%. Meanwhile, for performance measurements, the accuracy of the YOLOv4-tiny model was 87%, followed by the YOLOv7 model at 84%. In the banana fruit experiment, several models showed very good performance, such as recognition of the Ambon, Kepok, and Emas banana classes up to 100% using the YOLOv7 and YOLOv4-tiny models. The YOLOv7 model itself can recognize other banana classes up to 100% in the Barangan, Rjbulu, Uli, and Tanduk classes. Furthermore, theYOLOv4-tiny model can recognize other banana classes, up to 90% of the Barangan, Rjbulu, Rjsereh, and Uli banana types. Thus, this experiment provides very good average accuracy results on 2 CNN models, namely YOLOv7 and YOLOv4-tiny. Future research will involve grouping pictures of bananas, which produces different image shapes, so it requires a different way to recognize them. It is hoped that this research can become a basis for further research in this field.","PeriodicalId":507791,"journal":{"name":"International journal of electrical and computer engineering systems","volume":"124 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140369958","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}
Mobile ad hoc networks (MANETs), which are a promising method for the intelligent transportation system, include vehicular ad hoc networks (VANETs) (ITS). Developing reliable and strong cooperative collision avoidance (CCA) strategy to mitigate the growing number of road fatalities each year is one of the main difficulties facing vehicular ad hoc networks (VANETs).A proper and successful routing method aids in the successful expansion of vehicular ad hoc networks. This study explains the architecture, interface layers, safety features, and implementation of a novel priority-based direction-aware collision avoidance system (P-DVCA). It distinguishes our study in the collision area of VANETs by accounting for realistic bi-directional traffic. The scheme begins with the development of dynamic clusters, which is difficult because of the bi-directional diverse traffic and the need to avoid collisions within and between clusters. The target node is sent an early warning message that includes the safe speed and the likelihood of a collision in order to notify it of an impending danger. To determine the least expensive, shortest one with the fewest hops between the source and the endpoint. A crucial problem with VANETs is the transmission of data from a source node to the base station. Cross-layer issues must be solved for a robust and stable collision avoidance programme to function properly in a VANET communication architecture. The results of the simulation show that the suggested scheme significantly outperforms CCM and C-RACCA in terms of cluster stability, fewer collisions, low latency, and low communication overhead. According to the findings, P-DVCA offers stable clustering, minimises network congestion, and lowers communication overhead and latency.
移动特设网络（MANET）是智能交通系统中一种前景广阔的方法，其中包括车辆特设网络（VANET）（ITS）。开发可靠而强大的协同避免碰撞（CCA）策略，以减少每年不断增加的道路死亡事故，是车载 ad hoc 网络（VANET）面临的主要困难之一。本研究阐述了新型基于优先级的方向感知防撞系统（P-DVCA）的体系结构、接口层、安全功能和实现方法。通过考虑现实的双向交通，它使我们在 VANET 碰撞领域的研究与众不同。该方案从动态集群的发展开始，由于双向多样的流量以及避免集群内和集群间碰撞的需要，动态集群的发展十分困难。向目标节点发送包括安全速度和碰撞可能性在内的预警信息，以通知其危险即将来临。在源点和终点之间确定一条成本最低、最短、跳数最少的线路。VANET 的一个关键问题是从源节点向基站传输数据。必须解决跨层问题，才能在 VANET 通信架构中正常运行稳健而稳定的避免碰撞方案。仿真结果表明，建议的方案在集群稳定性、较少碰撞、低延迟和低通信开销方面明显优于 CCM 和 C-RACCA。研究结果表明，P-DVCA 可提供稳定的聚类，最大限度地减少网络拥塞，并降低通信开销和延迟。
{"title":"Increasing Efficiency and Reliability in Multicast Routing based V2V Communication for Direction-Aware Cooperative Collision Avoidance","authors":"L. V, S. Pramila. R","doi":"10.32985/ijeces.15.2.3","DOIUrl":"https://doi.org/10.32985/ijeces.15.2.3","url":null,"abstract":"Mobile ad hoc networks (MANETs), which are a promising method for the intelligent transportation system, include vehicular ad hoc networks (VANETs) (ITS). Developing reliable and strong cooperative collision avoidance (CCA) strategy to mitigate the growing number of road fatalities each year is one of the main difficulties facing vehicular ad hoc networks (VANETs).A proper and successful routing method aids in the successful expansion of vehicular ad hoc networks. This study explains the architecture, interface layers, safety features, and implementation of a novel priority-based direction-aware collision avoidance system (P-DVCA). It distinguishes our study in the collision area of VANETs by accounting for realistic bi-directional traffic. The scheme begins with the development of dynamic clusters, which is difficult because of the bi-directional diverse traffic and the need to avoid collisions within and between clusters. The target node is sent an early warning message that includes the safe speed and the likelihood of a collision in order to notify it of an impending danger. To determine the least expensive, shortest one with the fewest hops between the source and the endpoint. A crucial problem with VANETs is the transmission of data from a source node to the base station. Cross-layer issues must be solved for a robust and stable collision avoidance programme to function properly in a VANET communication architecture. The results of the simulation show that the suggested scheme significantly outperforms CCM and C-RACCA in terms of cluster stability, fewer collisions, low latency, and low communication overhead. According to the findings, P-DVCA offers stable clustering, minimises network congestion, and lowers communication overhead and latency.","PeriodicalId":507791,"journal":{"name":"International journal of electrical and computer engineering systems","volume":"8 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140436581","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}
Rajakumar S., William P., Mabel Rose R. A., Subraja Rajaretnam, Azhagu Jaisudhan Pazhani A.
Network security has grown to be a major concern in recent years due to the popularity and development of Wi-Fi networks. However, the use of Wi-Fi networks is expanding quickly, and so is the number of attacks on Wi-Fi networks. In this paper, a novel WiFi Unauthorized Access Detection System (WUADS) technique has been proposed to detect unauthorized access in the WiFi network. Initially, the Wi-Fi frames are collected from the AWID dataset. The features of the Wi-Fi frame are extracted by using Principal Component Analysis (PCA). Finally, the Deep Belief Network (DBN) is employed for classification into authorized access and unauthorized access. The efficiency of the proposed WUADS technique was evaluated based on the parameters like accuracy, F1score, detection rate, precision, and recall. The performance analysis of the proposed WUADS technique achieves an overall accuracy range of 99.52%. The proposed WUADS method has a high success rate and the quickest attack detection time compared to deep learning techniques like CNN, RNN, and ANN. The proposed WUADS improves the overall accuracy better than 1.12%, 0.1%, and 14.22% comparative analysis of the SAE (Stacked AutoEncoder), WNIDS (wireless Network Intrusion Detection System), and 3D-ID (3 Dimensional-Identification) respectively.
{"title":"An Effective Technique to Detect WIFI Unauthorized Access using Deep Belief Network","authors":"Rajakumar S., William P., Mabel Rose R. A., Subraja Rajaretnam, Azhagu Jaisudhan Pazhani A.","doi":"10.32985/ijeces.15.2.2","DOIUrl":"https://doi.org/10.32985/ijeces.15.2.2","url":null,"abstract":"Network security has grown to be a major concern in recent years due to the popularity and development of Wi-Fi networks. However, the use of Wi-Fi networks is expanding quickly, and so is the number of attacks on Wi-Fi networks. In this paper, a novel WiFi Unauthorized Access Detection System (WUADS) technique has been proposed to detect unauthorized access in the WiFi network. Initially, the Wi-Fi frames are collected from the AWID dataset. The features of the Wi-Fi frame are extracted by using Principal Component Analysis (PCA). Finally, the Deep Belief Network (DBN) is employed for classification into authorized access and unauthorized access. The efficiency of the proposed WUADS technique was evaluated based on the parameters like accuracy, F1score, detection rate, precision, and recall. The performance analysis of the proposed WUADS technique achieves an overall accuracy range of 99.52%. The proposed WUADS method has a high success rate and the quickest attack detection time compared to deep learning techniques like CNN, RNN, and ANN. The proposed WUADS improves the overall accuracy better than 1.12%, 0.1%, and 14.22% comparative analysis of the SAE (Stacked AutoEncoder), WNIDS (wireless Network Intrusion Detection System), and 3D-ID (3 Dimensional-Identification) respectively.","PeriodicalId":507791,"journal":{"name":"International journal of electrical and computer engineering systems","volume":"63 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140436732","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}
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