Wirelessly based security applications have exploded as a result of modern technology. To build and/or implement security access control systems, many types of wireless communication technologies have been deployed. quick response (QR code) is a contactless technology that is extensively utilised in a variety of sectors, including access control, library book tracking, supply chains, and tollgate systems, among others. This paper combines QR code technology with Arduino and Python to construct an automated QR code-based access management system. After detecting a QR code, the QR scanner at the entry collects and compares the user's unique identifier (UID) with the UID recorded in the system. The results show that this system is capable of granting or denying access to a protected environment in a timely, effective, and reliable way. Security systems can protect physical and intellectual property by preventing unauthorized persons from entering the area. Many door locks, such as mechanical and electrical locks, were created to meet basic security needs but it also helps to create a data files structure of the authorized persons.
{"title":"Smart door access control system based on QR code","authors":"Agrim Jain, Abhinav Panwar, Mohd. Azam, Ruqaiya Khanam","doi":"10.11591/ijict.v12i2.pp171-179","DOIUrl":"https://doi.org/10.11591/ijict.v12i2.pp171-179","url":null,"abstract":"Wirelessly based security applications have exploded as a result of modern technology. To build and/or implement security access control systems, many types of wireless communication technologies have been deployed. quick response (QR code) is a contactless technology that is extensively utilised in a variety of sectors, including access control, library book tracking, supply chains, and tollgate systems, among others. This paper combines QR code technology with Arduino and Python to construct an automated QR code-based access management system. After detecting a QR code, the QR scanner at the entry collects and compares the user's unique identifier (UID) with the UID recorded in the system. The results show that this system is capable of granting or denying access to a protected environment in a timely, effective, and reliable way. Security systems can protect physical and intellectual property by preventing unauthorized persons from entering the area. Many door locks, such as mechanical and electrical locks, were created to meet basic security needs but it also helps to create a data files structure of the authorized persons.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125253504","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}
Pub Date : 1900-01-01DOI: 10.11591/ijict.v12i3.pp225-235
Prateek Agarwal, Tanuj Kumar Garg, Adesh Kumar
The router is a network device that is used to connect subnetwork and packet-switched networking by directing the data packets to the intended IP addresses. It succeeds the traffic between different systems and allows several devices to share the internet connection. The router is applicable for the effective commutation in system on chip (SoC) modules for network on chip (NoC) communication. The research paper emphasizes the design of the two dimensional (2D) router hardware chip in the Xilinx integrated system environment (ISE) 14.7 software and further logic verification using the data packets transmitted from all input/output ports. The design evaluation is done based on the pre-synthesis device utilization summary relating to different field programmable gate array (FPGA) boards such as Spartan-3E (XC3S500E), Spartan-6 (XC6SLX45), Virtex-4 (XC4VFX12), Virtex-5 (XC5VSX50T), and Virtex-7 (XC7VX550T). The 64-bit data logic is verified on the different ports of the router configuration in the Xilinx and Modelsim waveform simulator. The Virtex-7 has proven the fast-switching speed and optimal hardware parameters in comparison to other FPGAs.
{"title":"2D router chip design, analysis, and simulation for effective communication","authors":"Prateek Agarwal, Tanuj Kumar Garg, Adesh Kumar","doi":"10.11591/ijict.v12i3.pp225-235","DOIUrl":"https://doi.org/10.11591/ijict.v12i3.pp225-235","url":null,"abstract":"The router is a network device that is used to connect subnetwork and packet-switched networking by directing the data packets to the intended IP addresses. It succeeds the traffic between different systems and allows several devices to share the internet connection. The router is applicable for the effective commutation in system on chip (SoC) modules for network on chip (NoC) communication. The research paper emphasizes the design of the two dimensional (2D) router hardware chip in the Xilinx integrated system environment (ISE) 14.7 software and further logic verification using the data packets transmitted from all input/output ports. The design evaluation is done based on the pre-synthesis device utilization summary relating to different field programmable gate array (FPGA) boards such as Spartan-3E (XC3S500E), Spartan-6 (XC6SLX45), Virtex-4 (XC4VFX12), Virtex-5 (XC5VSX50T), and Virtex-7 (XC7VX550T). The 64-bit data logic is verified on the different ports of the router configuration in the Xilinx and Modelsim waveform simulator. The Virtex-7 has proven the fast-switching speed and optimal hardware parameters in comparison to other FPGAs.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"239 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133989832","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}
Pub Date : 1900-01-01DOI: 10.11591/ijict.v12i2.pp115-126
Neha Sandotra, P. Mahajan, P. Abrol, P. Lehana
Convolutional neural networks (CNN) trained using deep learning (DL) have advanced dramatically in recent years. Researchers from a variety of fields have been motivated by the success of CNNs in computer vision to develop better CNN models for use in other visually-rich settings. Successes in image classification and research have been achieved in a wide variety of domains throughout the past year. Among the many popularized image classification techniques, the detection of plant leaf diseases has received extensive research. As a result of the nature of the procedure, image quality is often degraded and distortions are introduced during the capturing of the image. In this study, we look into how various CNN models are affected by distortions. Corn-maze leaf photos from the 4,188-image corn or maize leaf Dataset (split into four categories) are under consideration. To evaluate how well they handle noise and blur, researchers have deployed pre-trained deep CNN models like visual geometry group (VGG), InceptionV3, ResNet50, and EfficientNetB0. Classification accuracy and metrics like as recall and f1-score are used to evaluate CNN performance.
{"title":"Analyzing performance of deep learning models under the presence of distortions in identifying plant leaf disease","authors":"Neha Sandotra, P. Mahajan, P. Abrol, P. Lehana","doi":"10.11591/ijict.v12i2.pp115-126","DOIUrl":"https://doi.org/10.11591/ijict.v12i2.pp115-126","url":null,"abstract":"Convolutional neural networks (CNN) trained using deep learning (DL) have advanced dramatically in recent years. Researchers from a variety of fields have been motivated by the success of CNNs in computer vision to develop better CNN models for use in other visually-rich settings. Successes in image classification and research have been achieved in a wide variety of domains throughout the past year. Among the many popularized image classification techniques, the detection of plant leaf diseases has received extensive research. As a result of the nature of the procedure, image quality is often degraded and distortions are introduced during the capturing of the image. In this study, we look into how various CNN models are affected by distortions. Corn-maze leaf photos from the 4,188-image corn or maize leaf Dataset (split into four categories) are under consideration. To evaluate how well they handle noise and blur, researchers have deployed pre-trained deep CNN models like visual geometry group (VGG), InceptionV3, ResNet50, and EfficientNetB0. Classification accuracy and metrics like as recall and f1-score are used to evaluate CNN performance.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124567670","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}
Pub Date : 1900-01-01DOI: 10.11591/ijict.v12i2.pp92-102
Nnebe Scholastica Ukamaka, Odeh Isaac Ochim, Okafor Chinenye Sunday, Ugbe Oluchi Christiana
The study evaluated interference in a dense heterogeneous network using third-generation universal mobile telecommunication systems (UMTS) and fourth-generation long term evolution (LTE) networks LTE. The UMTs/LTE heterogeneous network determines the level of interference when the two communication systems coexist and how to improve the network by migrating from UMTs to LTE, which has a faster download speed and larger capacity. Techno lite 8 on third generation (3G) and Infinix Pro 6 on fourth generation (4G) were used to measure network the received signal strength (RSS) during site investigation. UE interference was detected and traced using a spectrum analyzer. UMTS and LTE path loss exponents are 2.6 and 3.2. Shannon's capacity theorem calculated LTE and UMTS capacity. When signal to interference and noise ratio (SINR) was used as a quality of service (QoS) indicator, MATLAB channel capacity plots did not match Shannon's due to neighboring interference. UMTS had an R2 of 0.54 and LTE 0.57 for the Shannon channel capacity equation. Adjacent channel interference (ACI) user devices reduce network capacity, lowering QoS for other customers.
该研究评估了使用第三代通用移动通信系统(UMTS)和第四代长期演进(LTE)网络的密集异构网络中的干扰。umt /LTE异构网络决定了两种通信系统共存时的干扰程度，以及如何通过从umt迁移到下载速度更快、容量更大的LTE来改善网络。在现场调查过程中，采用Techno lite 8第三代(3G)和Infinix Pro 6第四代(4G)测量网络接收信号强度(RSS)。使用频谱分析仪检测和跟踪UE干扰。UMTS和LTE的路径损耗指数分别为2.6和3.2。Shannon容量定理计算LTE和UMTS的容量。当信噪比(SINR)作为服务质量(QoS)指标时，由于相邻干扰，MATLAB的信道容量图与Shannon的信道容量图不匹配。对于香农信道容量方程，UMTS的R2为0.54,LTE的R2为0.57。相邻信道干扰(ACI)用户设备减少了网络容量，降低了其他客户的QoS。
{"title":"Evaluating the level of inteference in UMTS/LTE heterogeneous network system","authors":"Nnebe Scholastica Ukamaka, Odeh Isaac Ochim, Okafor Chinenye Sunday, Ugbe Oluchi Christiana","doi":"10.11591/ijict.v12i2.pp92-102","DOIUrl":"https://doi.org/10.11591/ijict.v12i2.pp92-102","url":null,"abstract":"The study evaluated interference in a dense heterogeneous network using third-generation universal mobile telecommunication systems (UMTS) and fourth-generation long term evolution (LTE) networks LTE. The UMTs/LTE heterogeneous network determines the level of interference when the two communication systems coexist and how to improve the network by migrating from UMTs to LTE, which has a faster download speed and larger capacity. Techno lite 8 on third generation (3G) and Infinix Pro 6 on fourth generation (4G) were used to measure network the received signal strength (RSS) during site investigation. UE interference was detected and traced using a spectrum analyzer. UMTS and LTE path loss exponents are 2.6 and 3.2. Shannon's capacity theorem calculated LTE and UMTS capacity. When signal to interference and noise ratio (SINR) was used as a quality of service (QoS) indicator, MATLAB channel capacity plots did not match Shannon's due to neighboring interference. UMTS had an R2 of 0.54 and LTE 0.57 for the Shannon channel capacity equation. Adjacent channel interference (ACI) user devices reduce network capacity, lowering QoS for other customers.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"40 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120851299","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}
Pub Date : 1900-01-01DOI: 10.11591/ijict.v12i3.pp272-283
M. Mohseni
In this study, machine learning is examined in relation to commercial machine learning's resilience to the COVID-19 pandemic-related crisis. Two approaches are used to assess the pandemic's impact on machine learning risk, as well as a method to prioritize sectors according to the crisis's potential negative consequences. I conducted the study to determine Santander machine learning's resilience. The data mining area offers prospects for COVID-19's future. A total of 13 machine learning demos were selected for its organization. The Hellweg strategy and the technique for order preference by similarity to ideal solution (TOPSIS) technique were utilized as direct request strategies. Parametric assessment of machine learning versatility in business was based on capital sufficiency, liquidity proportion, market benefits, and share in an arrangement of openings with a perceived disability, and affectability of machine learning's credit portfolio to monetary hazard. As a result of the COVID-19 pandemic, these enterprises were ranked according to their threat. Based on the findings of the research, machine learning worked the best for the pandemic. Meanwhile, machine learning suffered the most during the downturn. It can be seen, for example, in conversations about the impact of the pandemic on developing business sector soundness and managing financial framework solidity risk.
在本研究中，研究了机器学习与商业机器学习对COVID-19大流行相关危机的复原力的关系。有两种方法用于评估大流行对机器学习风险的影响，以及根据危机的潜在负面后果确定部门优先次序的方法。我进行这项研究是为了确定桑坦德银行机器学习的弹性。数据挖掘领域为COVID-19的未来提供了前景。该组织共选择了13个机器学习演示。直接请求策略采用Hellweg策略和TOPSIS (order preference by similarity to ideal solution)方法。对机器学习在商业中的通用性的参数化评估是基于资本充足率、流动性比例、市场收益、在具有感知残疾的开放安排中的份额，以及机器学习的信贷组合对货币风险的影响。受COVID-19大流行影响，这些企业根据其威胁程度进行排名。根据研究结果，机器学习对大流行的效果最好。与此同时，机器学习在经济衰退期间受到的影响最大。例如，在关于大流行对发展商业部门稳健性和管理金融框架稳健性风险的影响的对话中可以看到这一点。
{"title":"Evaluating the impact of COVID-19 on the monetary crisis by machine learning","authors":"M. Mohseni","doi":"10.11591/ijict.v12i3.pp272-283","DOIUrl":"https://doi.org/10.11591/ijict.v12i3.pp272-283","url":null,"abstract":"In this study, machine learning is examined in relation to commercial machine learning's resilience to the COVID-19 pandemic-related crisis. Two approaches are used to assess the pandemic's impact on machine learning risk, as well as a method to prioritize sectors according to the crisis's potential negative consequences. I conducted the study to determine Santander machine learning's resilience. The data mining area offers prospects for COVID-19's future. A total of 13 machine learning demos were selected for its organization. The Hellweg strategy and the technique for order preference by similarity to ideal solution (TOPSIS) technique were utilized as direct request strategies. Parametric assessment of machine learning versatility in business was based on capital sufficiency, liquidity proportion, market benefits, and share in an arrangement of openings with a perceived disability, and affectability of machine learning's credit portfolio to monetary hazard. As a result of the COVID-19 pandemic, these enterprises were ranked according to their threat. Based on the findings of the research, machine learning worked the best for the pandemic. Meanwhile, machine learning suffered the most during the downturn. It can be seen, for example, in conversations about the impact of the pandemic on developing business sector soundness and managing financial framework solidity risk.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121665754","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}
Pub Date : 1900-01-01DOI: 10.11591/ijict.v12i3.pp214-224
Meriam Dhouibi, A. K. Ben Salem, Afef Saidi, S. Ben Saoud
Diabetic retinopathy (DR) is one of the most common causes of blindness. The necessity for a robust and automated DR screening system for regular examination has long been recognized in order to identify DR at an early stage. In this paper, an embedded DR diagnosis system based on convolutional neural networks (CNNs) has been proposed to assess the proper stage of DR. We coupled the power of CNN with transfer learning to design our model based on state-of-the-art architecture. We preprocessed the input data, which is color fundus photography, to reduce undesirable noise in the image. After training many models on the dataset, we chose the adopted ResNet50 because it produced the best results, with a 92.90% accuracy. Extensive experiments and comparisons with other research work show that the proposed method is effective. Furthermore, the CNN model has been implemented on an embedded target to be a part of a medical instrument diagnostic system. We have accelerated our model inference on a field programmable gate array (FPGA) using Xilinx tools. Results have confirmed that a customized FPGA system on chip (SoC) with hardware accelerators is a promising target for our DR detection model with high performance and low power consumption.
{"title":"Acceleration of convolutional neural network based diabetic retinopathy diagnosis system on field programmable gate array","authors":"Meriam Dhouibi, A. K. Ben Salem, Afef Saidi, S. Ben Saoud","doi":"10.11591/ijict.v12i3.pp214-224","DOIUrl":"https://doi.org/10.11591/ijict.v12i3.pp214-224","url":null,"abstract":"Diabetic retinopathy (DR) is one of the most common causes of blindness. The necessity for a robust and automated DR screening system for regular examination has long been recognized in order to identify DR at an early stage. In this paper, an embedded DR diagnosis system based on convolutional neural networks (CNNs) has been proposed to assess the proper stage of DR. We coupled the power of CNN with transfer learning to design our model based on state-of-the-art architecture. We preprocessed the input data, which is color fundus photography, to reduce undesirable noise in the image. After training many models on the dataset, we chose the adopted ResNet50 because it produced the best results, with a 92.90% accuracy. Extensive experiments and comparisons with other research work show that the proposed method is effective. Furthermore, the CNN model has been implemented on an embedded target to be a part of a medical instrument diagnostic system. We have accelerated our model inference on a field programmable gate array (FPGA) using Xilinx tools. Results have confirmed that a customized FPGA system on chip (SoC) with hardware accelerators is a promising target for our DR detection model with high performance and low power consumption.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127987383","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}
Pub Date : 1900-01-01DOI: 10.11591/ijict.v12i2.pp150-161
Andri Reimondo Tamba, Krista Lumbantoruan, A. Pakpahan, S. Situmeang
The course activity log is where a learning management system (LMS) like Moodle keeps track of the various learning activities. In order to conduct a quicker and more in-depth examination of the students' behaviors, the instructor may either directly examine the log or make use of more complex methodologies such as data mining. The majority of the proposed methods for analyzing this log data center mostly on predictive analysis. In this research, cluster analysis and association analysis, two separate data mining functions, are investigated in order to analyze the log. The students' activities are used in the cluster analysis performed with K-Means++, and the association analysis performed with Apriori is used to investigate the connections between the students' various activities. A dashboard presentation of the findings is provided in order to facilitate clearer comprehension. Based on the findings of the analysis, it can be concluded that the structure of the student cluster is medium, whereas the association between the activities undertaken by students is positively correlated and well-balanced. The subjective review of the dashboard reveals that the visualization is already sufficient, but there are some recommendations for making it even better.
{"title":"A cluster and association analysis visualization using Moodle activity log data","authors":"Andri Reimondo Tamba, Krista Lumbantoruan, A. Pakpahan, S. Situmeang","doi":"10.11591/ijict.v12i2.pp150-161","DOIUrl":"https://doi.org/10.11591/ijict.v12i2.pp150-161","url":null,"abstract":"The course activity log is where a learning management system (LMS) like Moodle keeps track of the various learning activities. In order to conduct a quicker and more in-depth examination of the students' behaviors, the instructor may either directly examine the log or make use of more complex methodologies such as data mining. The majority of the proposed methods for analyzing this log data center mostly on predictive analysis. In this research, cluster analysis and association analysis, two separate data mining functions, are investigated in order to analyze the log. The students' activities are used in the cluster analysis performed with K-Means++, and the association analysis performed with Apriori is used to investigate the connections between the students' various activities. A dashboard presentation of the findings is provided in order to facilitate clearer comprehension. Based on the findings of the analysis, it can be concluded that the structure of the student cluster is medium, whereas the association between the activities undertaken by students is positively correlated and well-balanced. The subjective review of the dashboard reveals that the visualization is already sufficient, but there are some recommendations for making it even better.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116057384","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}
Pub Date : 1900-01-01DOI: 10.11591/ijict.v12i2.pp140-149
Oumaima Liouane, S. Femmam, T. Bakir, Abdessalem Ben Abdelali
Localization is a critical concern in many wireless sensor network (WSN) applications. Furthermore, correct information regarding the geographic placements of nodes (sensors) is critical for making the collected data valuable and relevant. Because of their benefits, such as simplicity and acceptable accuracy, the based connectivity algorithms attempt to localize multi-hop WSN. However, due to environmental factors, the precision of localisation may be rather low. This publication describes an Extreme Learning Machine (ELM) technique for minimizing localization error in range-free WSN. In this paper, we propose a Cascade Extreme Learning Machine (Cascade-ELM) to increase localization accuracy in Range-Free WSNs. We tested the proposed approaches in a variety of multi-hop WSN scenarios. Our research focused on an isotropic and irregular environment. The simulation results show that the proposed Cascade-ELM algorithm considerably improves localization accuracy when compared to previous algorithms derived from smart computing approaches. When compared to previous work, isotropic environments show improved localization results.
{"title":"Novel DV-hop algorithm-based machines learning technics for node localization in rang-free wireless sensor networks","authors":"Oumaima Liouane, S. Femmam, T. Bakir, Abdessalem Ben Abdelali","doi":"10.11591/ijict.v12i2.pp140-149","DOIUrl":"https://doi.org/10.11591/ijict.v12i2.pp140-149","url":null,"abstract":"Localization is a critical concern in many wireless sensor network (WSN) applications. Furthermore, correct information regarding the geographic placements of nodes (sensors) is critical for making the collected data valuable and relevant. Because of their benefits, such as simplicity and acceptable accuracy, the based connectivity algorithms attempt to localize multi-hop WSN. However, due to environmental factors, the precision of localisation may be rather low. This publication describes an Extreme Learning Machine (ELM) technique for minimizing localization error in range-free WSN. In this paper, we propose a Cascade Extreme Learning Machine (Cascade-ELM) to increase localization accuracy in Range-Free WSNs. We tested the proposed approaches in a variety of multi-hop WSN scenarios. Our research focused on an isotropic and irregular environment. The simulation results show that the proposed Cascade-ELM algorithm considerably improves localization accuracy when compared to previous algorithms derived from smart computing approaches. When compared to previous work, isotropic environments show improved localization results.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126157943","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}
Pub Date : 1900-01-01DOI: 10.11591/ijict.v12i2.pp127-139
Amatullah Fatwimah Humairaa Mahomodally, Geerish Suddul, S. Armoogum
Diseases in edible and industrial plants remains a major concern, affecting producers and consumers. The problem is further exacerbated as there are different species of plants with a wide variety of diseases that reduce the effectiveness of certain pesticides while increasing our risk of illness. A timely, accurate and automated detection of diseases can be beneficial. Our work focuses on evaluating deep learning (DL) approaches using transfer learning to automatically detect diseases in plants. To enhance the capabilities of our approach, we compiled a novel image dataset containing 87,570 records encompassing 32 different plants and 74 types of diseases. The dataset consists of leaf images from both laboratory setups and cultivation fields, making it more representative. To the best of our knowledge, no such datasets have been used for DL models. Four pre[1]trained computer vision models, namely VGG-16, VGG-19, ResNet-50, and ResNet-101 were evaluated on our dataset. Our experiments demonstrate that both VGG-16 and VGG-19 models proved more efficient, yielding an accuracy of approximately 86% and a f1-score of 87%, as compared to ResNet-50 and ResNet-101. ResNet-50 attains an accuracy and a f1-score of 46.9% and 45.6%, respectively, while ResNet-101 reaches an accuracy of 40.7% and a f1-score of 26.9%.
{"title":"Machine learning techniques for plant disease detection: an evaluation with a customized dataset","authors":"Amatullah Fatwimah Humairaa Mahomodally, Geerish Suddul, S. Armoogum","doi":"10.11591/ijict.v12i2.pp127-139","DOIUrl":"https://doi.org/10.11591/ijict.v12i2.pp127-139","url":null,"abstract":"Diseases in edible and industrial plants remains a major concern, affecting producers and consumers. The problem is further exacerbated as there are different species of plants with a wide variety of diseases that reduce the effectiveness of certain pesticides while increasing our risk of illness. A timely, accurate and automated detection of diseases can be beneficial. Our work focuses on evaluating deep learning (DL) approaches using transfer learning to automatically detect diseases in plants. To enhance the capabilities of our approach, we compiled a novel image dataset containing 87,570 records encompassing 32 different plants and 74 types of diseases. The dataset consists of leaf images from both laboratory setups and cultivation fields, making it more representative. To the best of our knowledge, no such datasets have been used for DL models. Four pre[1]trained computer vision models, namely VGG-16, VGG-19, ResNet-50, and ResNet-101 were evaluated on our dataset. Our experiments demonstrate that both VGG-16 and VGG-19 models proved more efficient, yielding an accuracy of approximately 86% and a f1-score of 87%, as compared to ResNet-50 and ResNet-101. ResNet-50 attains an accuracy and a f1-score of 46.9% and 45.6%, respectively, while ResNet-101 reaches an accuracy of 40.7% and a f1-score of 26.9%.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114459513","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}
Pub Date : 1900-01-01DOI: 10.11591/ijict.v12i3.pp261-271
H. Baghdadi, K. Rhofir, M. Lamhamdi
In this work, an internet of things (IoT) sensing and monitoring box has been developed. The proposed low-cost system is a portable device for smart buildings to measure vibrations, monitor, and control noise caused by the industrial machines. We will present an instrument and a method to measure the vibration and tilt of a mechanical system (air conditioner). The primary goal is to create a signal acquisition and monitoring system that is both user-friendly and affordable, while also delivering exceptional precision. The key concept is centered around acquiring and processing signals through the Raspberry Pi. We will use for the first time as an application, which does not exist before, a conversion method to control and monitor remotely the noise generated by the machines. Once the noise reaches a high value or the air conditioner is too much tilted, the system sends an alert in the form of an email. We will use the Python language to acquire and process the signal and send the alerts. The proposed approach is straightforward to implement, and the obtained results demonstrate a high level of accuracy that is consistent with the existing literature.
{"title":"Smart portable system for monitoring vibration based on the Raspberry Pi microcomputer and the MEMS accelerometer","authors":"H. Baghdadi, K. Rhofir, M. Lamhamdi","doi":"10.11591/ijict.v12i3.pp261-271","DOIUrl":"https://doi.org/10.11591/ijict.v12i3.pp261-271","url":null,"abstract":"In this work, an internet of things (IoT) sensing and monitoring box has been developed. The proposed low-cost system is a portable device for smart buildings to measure vibrations, monitor, and control noise caused by the industrial machines. We will present an instrument and a method to measure the vibration and tilt of a mechanical system (air conditioner). The primary goal is to create a signal acquisition and monitoring system that is both user-friendly and affordable, while also delivering exceptional precision. The key concept is centered around acquiring and processing signals through the Raspberry Pi. We will use for the first time as an application, which does not exist before, a conversion method to control and monitor remotely the noise generated by the machines. Once the noise reaches a high value or the air conditioner is too much tilted, the system sends an alert in the form of an email. We will use the Python language to acquire and process the signal and send the alerts. The proposed approach is straightforward to implement, and the obtained results demonstrate a high level of accuracy that is consistent with the existing literature.","PeriodicalId":245958,"journal":{"name":"International Journal of Informatics and Communication Technology (IJ-ICT)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123715549","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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