Pub Date : 2023-01-01DOI: 10.12720/jait.14.5.1003-1011
Chetana Hemant Nemade, Uma Pujeri
—Vehicular Adhoc Networks (VANET) have grown in popularity recently. Several analytical challenges must address to build VANETs that improve driver assistance, safety, and traffic management. Another big problem is the development of expandable route findings that can assess fast topography variations and numerous network detachments brought on through excellent vehicle quality. This paper will first discuss extensive technological investigations comprising and defects of the current progressive routing algorithms. Then, author suggests an entirely original routing theme called Emergency Data Transmission using ACO (EDTA). Design this protocol to use any freeway the ambulance driver has access to or any less-traveled paths with the least amount of communication overhead and delay and the highest amount of communication throughput. The patients received treatment more promptly since the driver was alerted earlier. Author developed a novel fitness function for the Ant Colony Optimization (ACO) that concentrates on two crucial vehicle parameters: current travel speed and data/network congestion. The ACO is used to optimize to identify a more stable and reliable channel that enables rapid communication between vehicles. The performance of this protocol will compare to that of a state-of-the-art protocol in conclusion with “average throughput”, “packet delivery ratio”, “communication overhead”
{"title":"Emergency Automobile Data Transmission with Ant Colony Optimization (ACO)","authors":"Chetana Hemant Nemade, Uma Pujeri","doi":"10.12720/jait.14.5.1003-1011","DOIUrl":"https://doi.org/10.12720/jait.14.5.1003-1011","url":null,"abstract":"—Vehicular Adhoc Networks (VANET) have grown in popularity recently. Several analytical challenges must address to build VANETs that improve driver assistance, safety, and traffic management. Another big problem is the development of expandable route findings that can assess fast topography variations and numerous network detachments brought on through excellent vehicle quality. This paper will first discuss extensive technological investigations comprising and defects of the current progressive routing algorithms. Then, author suggests an entirely original routing theme called Emergency Data Transmission using ACO (EDTA). Design this protocol to use any freeway the ambulance driver has access to or any less-traveled paths with the least amount of communication overhead and delay and the highest amount of communication throughput. The patients received treatment more promptly since the driver was alerted earlier. Author developed a novel fitness function for the Ant Colony Optimization (ACO) that concentrates on two crucial vehicle parameters: current travel speed and data/network congestion. The ACO is used to optimize to identify a more stable and reliable channel that enables rapid communication between vehicles. The performance of this protocol will compare to that of a state-of-the-art protocol in conclusion with “average throughput”, “packet delivery ratio”, “communication overhead”","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136202333","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 : 2023-01-01DOI: 10.12720/jait.14.1.46-55
T. M. Tshilongamulenzhe, Topside E. Mathonsi, D. D. Plessis, M. Mphahlele
Wireless Sensor Networks (WSNs) is an area that has attracted a lot of attention currently worldwide. WSNs are implemented to monitor temperature, humidity, and pressure, among others within the agricultural environment. This paper addresses the traffic congestion that occurs within WSNs in the agricultural environment during packet transmission that is normally caused by head-of-line blocking. As a result, packet loss, packet delay, and network performance impairment occurred during packet distribution in the network. This paper proposed an Intelligence Traffic Routing (ITR) algorithm to manage packet flow to avoid traffic congestion in WSNs within the agricultural environment while improving Quality of Service (QoS). The LBRM (Load Balancing Routing Management) and MLCC (Machine Learning Congestion Control) algorithms were integrated to develop the proposed ITR algorithm. Network Simulator 2 (NS-2) was used to test the effectiveness of the proposed ITR algorithm. The simulation results showed that the proposed ITR algorithm reduced packet loss by 27.3%, packet delay by 43.4%, and improved network throughput by 98.4% when compared with LBRM and MLCC algorithms.
{"title":"Intelligent Traffic Routing Algorithm for Wireless Sensor Networks in Agricultural Environment","authors":"T. M. Tshilongamulenzhe, Topside E. Mathonsi, D. D. Plessis, M. Mphahlele","doi":"10.12720/jait.14.1.46-55","DOIUrl":"https://doi.org/10.12720/jait.14.1.46-55","url":null,"abstract":"Wireless Sensor Networks (WSNs) is an area that has attracted a lot of attention currently worldwide. WSNs are implemented to monitor temperature, humidity, and pressure, among others within the agricultural environment. This paper addresses the traffic congestion that occurs within WSNs in the agricultural environment during packet transmission that is normally caused by head-of-line blocking. As a result, packet loss, packet delay, and network performance impairment occurred during packet distribution in the network. This paper proposed an Intelligence Traffic Routing (ITR) algorithm to manage packet flow to avoid traffic congestion in WSNs within the agricultural environment while improving Quality of Service (QoS). The LBRM (Load Balancing Routing Management) and MLCC (Machine Learning Congestion Control) algorithms were integrated to develop the proposed ITR algorithm. Network Simulator 2 (NS-2) was used to test the effectiveness of the proposed ITR algorithm. The simulation results showed that the proposed ITR algorithm reduced packet loss by 27.3%, packet delay by 43.4%, and improved network throughput by 98.4% when compared with LBRM and MLCC algorithms.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66330051","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}
— There are two techniques long-established in image watermarking area, namely the k-means and genetic algorithms. The first one is commonly used to allocate an image’s pixels into distinct clusters. However, the allocation of these pixels is not optimal in all cases. The second technique is usually employed to produce an optimal watermarking solution. In this paper, a hybrid methodology is presented for coloured image watermarking that integrates both genetic algorithm and k-means clustering activity to attain the optimized cluster centroids. These centroids are utilized to optimally distribute the pixels of the cover and watermark images into suitable clusters. This will help decrease the perceptible changes in the watermarked image with the naked eye. For concealment, the Least Significant Bits method is adopted. Typically, the pixels of every watermark cluster are concealed in its closest cover’s cluster; wherein every two successive pixels hide the bits of a single cover image’s pixel. The experimental results demonstrate that the proposed methodology satisfies a sufficient imperceptibility that yields and boosts resistance against common attacks.
{"title":"A Coloured Image Watermarking Based on Genetic K-Means Clustering Methodology","authors":"Zainab Falah Hassan, Farah Al-Shareefi, Hadeel Qasem Gheni","doi":"10.12720/jait.14.2.242-249","DOIUrl":"https://doi.org/10.12720/jait.14.2.242-249","url":null,"abstract":"— There are two techniques long-established in image watermarking area, namely the k-means and genetic algorithms. The first one is commonly used to allocate an image’s pixels into distinct clusters. However, the allocation of these pixels is not optimal in all cases. The second technique is usually employed to produce an optimal watermarking solution. In this paper, a hybrid methodology is presented for coloured image watermarking that integrates both genetic algorithm and k-means clustering activity to attain the optimized cluster centroids. These centroids are utilized to optimally distribute the pixels of the cover and watermark images into suitable clusters. This will help decrease the perceptible changes in the watermarked image with the naked eye. For concealment, the Least Significant Bits method is adopted. Typically, the pixels of every watermark cluster are concealed in its closest cover’s cluster; wherein every two successive pixels hide the bits of a single cover image’s pixel. The experimental results demonstrate that the proposed methodology satisfies a sufficient imperceptibility that yields and boosts resistance against common attacks.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66330144","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 : 2023-01-01DOI: 10.12720/jait.14.2.328-341
Thanh-Nghi Doan
— One of the main issues with agricultural production is insect attack, which leads to poor crop quality. Farmers, however, have a complicated and time-consuming task in detecting and categorizing insects. Therefore, research on an effective system for image-based automated insect classification is crucial. The conventional “softmax” function is utilized to determine the category for new image occurrences and minimize “cross-entropy” loss in the bulk of current research, which focuses on employing deep convolutional neural networks to categorize insect images. This paper presents a novel method for large-scale insect pest image classification by combining fine-tuning EfficientNets and Power Mean Support Vector Machine (SVM). First, EfficientNet models are fine-tuned and re-trained on new insect pest image datasets. The retrieved features from EfficientNet models are then utilized to create a machine learning classifier. In the network’s classification stage, the traditional “softmax” function is substituted with a non-linear classifier, Power Mean SVM. As a result, rather than “cross-entropy loss,” the training process focuses on reducing “margin-based loss.” Several benchmark insect image datasets were used to evaluate our proposed method. According to the numerical results, our method outperforms other cutting-edge methods for large-scale insect pest image categorization. With fine-tuning EfficientNets and Power Mean SVM, the classification accuracy of the proposed method for the Xie24, D0, and IP102 large insect pest datasets is 99%, 99%, and 72.31%, respectively. To our knowledge, these are the best performing image classification results for these datasets.
-农业生产的主要问题之一是虫害，这导致作物质量差。然而，农民在检测和分类昆虫方面有一项复杂而耗时的任务。因此，研究一种有效的基于图像的昆虫自动分类系统至关重要。在目前的大部分研究中，传统的“softmax”函数被用来确定新图像出现的类别，并最小化“交叉熵”损失，这些研究主要是利用深度卷积神经网络对昆虫图像进行分类。本文提出了一种结合精细化效率网络和功率平均支持向量机(Power Mean Support Vector Machine, SVM)的大规模害虫图像分类方法。首先，在新的害虫图像数据集上对EfficientNet模型进行微调和重新训练。然后利用从EfficientNet模型中检索到的特征来创建机器学习分类器。在网络的分类阶段，将传统的softmax函数替换为非线性分类器Power Mean SVM。因此，训练过程侧重于减少“基于边际的损失”，而不是“交叉熵损失”。使用几个基准昆虫图像数据集对我们提出的方法进行了评估。数值结果表明，该方法优于其他先进的大规模害虫图像分类方法。通过对EfficientNets和Power Mean SVM进行微调，该方法对Xie24、D0和IP102大型害虫数据集的分类准确率分别为99%、99%和72.31%。据我们所知，这些是这些数据集表现最好的图像分类结果。
{"title":"Large-Scale Insect Pest Image Classification","authors":"Thanh-Nghi Doan","doi":"10.12720/jait.14.2.328-341","DOIUrl":"https://doi.org/10.12720/jait.14.2.328-341","url":null,"abstract":"— One of the main issues with agricultural production is insect attack, which leads to poor crop quality. Farmers, however, have a complicated and time-consuming task in detecting and categorizing insects. Therefore, research on an effective system for image-based automated insect classification is crucial. The conventional “softmax” function is utilized to determine the category for new image occurrences and minimize “cross-entropy” loss in the bulk of current research, which focuses on employing deep convolutional neural networks to categorize insect images. This paper presents a novel method for large-scale insect pest image classification by combining fine-tuning EfficientNets and Power Mean Support Vector Machine (SVM). First, EfficientNet models are fine-tuned and re-trained on new insect pest image datasets. The retrieved features from EfficientNet models are then utilized to create a machine learning classifier. In the network’s classification stage, the traditional “softmax” function is substituted with a non-linear classifier, Power Mean SVM. As a result, rather than “cross-entropy loss,” the training process focuses on reducing “margin-based loss.” Several benchmark insect image datasets were used to evaluate our proposed method. According to the numerical results, our method outperforms other cutting-edge methods for large-scale insect pest image categorization. With fine-tuning EfficientNets and Power Mean SVM, the classification accuracy of the proposed method for the Xie24, D0, and IP102 large insect pest datasets is 99%, 99%, and 72.31%, respectively. To our knowledge, these are the best performing image classification results for these datasets.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66330364","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 : 2023-01-01DOI: 10.12720/jait.14.2.185-192
Farha Fatina Wahid, R. G., S. M. Joseph, Debabrata Swain, Om Prakash Das, Biswaranjan Acharya
F.F.W
F.F.W
{"title":"A Novel Fuzzy-Based Thresholding Approach for Blood Vessel Segmentation from Fundus Image","authors":"Farha Fatina Wahid, R. G., S. M. Joseph, Debabrata Swain, Om Prakash Das, Biswaranjan Acharya","doi":"10.12720/jait.14.2.185-192","DOIUrl":"https://doi.org/10.12720/jait.14.2.185-192","url":null,"abstract":"F.F.W","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66330525","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 : 2023-01-01DOI: 10.12720/jait.14.2.302-310
Kshitij A. Kakade, Kshitish Ghate, Rajat K Jaiswal, R. Jaiswal
—This study proposes to use a hybrid ensemble learning approach to improve the prediction efficiency of crude oil prices. It combines the Long Short-Term Memory (LSTM) with factors that influence the price of crude oil. The information from fundamental and technical indicators is considered along with statistical model predictions like autoregressive integrated moving average (ARIMA)to make one-step-ahead crude oil price predictions. A Principal Component Analysis (PCA) approach is employed to transform the explanatory variables. This study combines the LSTM with PCA, jointly known as the LP model wherein PCA transforms of the fundamental and technical indicators are used as inputs to improve LSTM predictions. Further, it attempts to improve these predictions by introducing the LSTM+PCA+ARIMA (LPA) model, which uses an ensemble learning approach to utilize the forecast from the ARIMA model, as an additional input. Among LP and LPA models, the LSTM model is used as a benchmark to evaluate the performance of the hybrid models. Based on the result, a significant improvement is seen in the LP model over the chosen window sizes and error metrics. On the other hand, the LPA model performs better across all dimensions with an average improvement of 41% over the LSTM model in terms of forecasting accuracy. Moreover, the equivalence of forecasting accuracy is tested using the Diebold-Mariano and Wilcoxon signed-rank tests
{"title":"A Novel Approach to Forecast Crude Oil Prices Using Machine Learning and Technical Indicators","authors":"Kshitij A. Kakade, Kshitish Ghate, Rajat K Jaiswal, R. Jaiswal","doi":"10.12720/jait.14.2.302-310","DOIUrl":"https://doi.org/10.12720/jait.14.2.302-310","url":null,"abstract":"—This study proposes to use a hybrid ensemble learning approach to improve the prediction efficiency of crude oil prices. It combines the Long Short-Term Memory (LSTM) with factors that influence the price of crude oil. The information from fundamental and technical indicators is considered along with statistical model predictions like autoregressive integrated moving average (ARIMA)to make one-step-ahead crude oil price predictions. A Principal Component Analysis (PCA) approach is employed to transform the explanatory variables. This study combines the LSTM with PCA, jointly known as the LP model wherein PCA transforms of the fundamental and technical indicators are used as inputs to improve LSTM predictions. Further, it attempts to improve these predictions by introducing the LSTM+PCA+ARIMA (LPA) model, which uses an ensemble learning approach to utilize the forecast from the ARIMA model, as an additional input. Among LP and LPA models, the LSTM model is used as a benchmark to evaluate the performance of the hybrid models. Based on the result, a significant improvement is seen in the LP model over the chosen window sizes and error metrics. On the other hand, the LPA model performs better across all dimensions with an average improvement of 41% over the LSTM model in terms of forecasting accuracy. Moreover, the equivalence of forecasting accuracy is tested using the Diebold-Mariano and Wilcoxon signed-rank tests","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66330629","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 : 2023-01-01DOI: 10.12720/jait.14.2.355-362
Kyosuke Kageyama, Sota Arai, Hajime Hamano, Xiangbo Kong, T. Kumaki, T. Koide
—Recently, it has become possible to execute various digital multimedia applications, such as image compression, video compression, and audio processing, on mobile devices — as long as the processing core in the mobile device has the required high levels of performance, versatility, and programmability. Generally speaking, multimedia applications operate by performing repeated arithmetic and table-lookup coding operations. Therefore, to make it easier to achieve those required high levels of performance, versatility, and programmability, we propose an accelerator for mobile Central Processing Units (CPUs) known as a Content Addressable Memory-based massive-parallel Single Instruction Multiple Data (SIMD) Matrix Core (CAMX) that improves the processing speeds of both arithmetic and table-lookup coding operations. Our proposed CAMX, which is equipped with two CAM modules, has highly parallel processing capabilities that facilitate fast table-lookup coding operations. In fact, the results of Advanced Encryption Standard (AES) encryption simulations conducted in this study show that its AES encryption total clock cycles are 1,362,699. Additionally, a detailed breakdown of the number of clock cycles shows 1,312,160 for SubBytes, a combined total of 17,161 for ShiftRows and MixColumns, and 2519 for AddRoundKey. This paper also confirmed that CAMX could process AES encryptions at a rate of 83.17 clock cycles/byte. Also, the performance of CAMX, related works, and existing mobile processors are compared. The related works do not have a dedicated circuit for AES processing. From the comparison results, CAMX provides a performance improvement of approximately 4.4-and 3569.1-times over the related works. The existing mobile processors are Texas Instruments (TI) DM3730 and a TI OMAP3530. From the comparison results, CAMX provides a performance improvement of approximately 2.1 times over TI DM3730 and TI OMAP3530.
{"title":"Parallel Software Encryption of AES Algorithm by Using CAM-Based Massive-Parallel SIMD Matrix Core for Mobile Accelerator","authors":"Kyosuke Kageyama, Sota Arai, Hajime Hamano, Xiangbo Kong, T. Kumaki, T. Koide","doi":"10.12720/jait.14.2.355-362","DOIUrl":"https://doi.org/10.12720/jait.14.2.355-362","url":null,"abstract":"—Recently, it has become possible to execute various digital multimedia applications, such as image compression, video compression, and audio processing, on mobile devices — as long as the processing core in the mobile device has the required high levels of performance, versatility, and programmability. Generally speaking, multimedia applications operate by performing repeated arithmetic and table-lookup coding operations. Therefore, to make it easier to achieve those required high levels of performance, versatility, and programmability, we propose an accelerator for mobile Central Processing Units (CPUs) known as a Content Addressable Memory-based massive-parallel Single Instruction Multiple Data (SIMD) Matrix Core (CAMX) that improves the processing speeds of both arithmetic and table-lookup coding operations. Our proposed CAMX, which is equipped with two CAM modules, has highly parallel processing capabilities that facilitate fast table-lookup coding operations. In fact, the results of Advanced Encryption Standard (AES) encryption simulations conducted in this study show that its AES encryption total clock cycles are 1,362,699. Additionally, a detailed breakdown of the number of clock cycles shows 1,312,160 for SubBytes, a combined total of 17,161 for ShiftRows and MixColumns, and 2519 for AddRoundKey. This paper also confirmed that CAMX could process AES encryptions at a rate of 83.17 clock cycles/byte. Also, the performance of CAMX, related works, and existing mobile processors are compared. The related works do not have a dedicated circuit for AES processing. From the comparison results, CAMX provides a performance improvement of approximately 4.4-and 3569.1-times over the related works. The existing mobile processors are Texas Instruments (TI) DM3730 and a TI OMAP3530. From the comparison results, CAMX provides a performance improvement of approximately 2.1 times over TI DM3730 and TI OMAP3530.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66330890","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 : 2023-01-01DOI: 10.12720/jait.14.2.363-372
Tina Elizabeth Mathew
—Breast cancer is considered the most problematic of all cancers affecting women. With high incidence and mortality rates, it is ranked as the primary and most significant health hazard for women globally. Early detection of the disease is the key to ensure the survival of the patient. Several medical techniques comprising of Mammography, Magnetic Resonance Imaging, Thermography and many more are available to detect the disease. But these techniques create much stress and pain, besides employing harmful rays for detection, to the patient undergoing them. Hence for early detection other categories of techniques can be implemented. Machine-learning assisted detection and classification is one such alternative. In this paper a hyper parameter optimized extreme gradient boosting model implemented along with F-Score feature selection is proposed and the model is used for classification of the breast tumor as either malignant or benign on the Wisconsin Breast Cancer dataset. The implementation of feature importance is investigated using F-Score and this is used for selecting the most relevant features that influence the target variable and classification is based on this. Experimentation is done using different training-testing partitions and the best performance of 99.27% accuracy score was shown by the 80−20 partition by the proposed XGBoost and F-Score Model.
{"title":"Breast Cancer Classification Using an Extreme Gradient Boosting Model with F-Score Feature Selection Technique","authors":"Tina Elizabeth Mathew","doi":"10.12720/jait.14.2.363-372","DOIUrl":"https://doi.org/10.12720/jait.14.2.363-372","url":null,"abstract":"—Breast cancer is considered the most problematic of all cancers affecting women. With high incidence and mortality rates, it is ranked as the primary and most significant health hazard for women globally. Early detection of the disease is the key to ensure the survival of the patient. Several medical techniques comprising of Mammography, Magnetic Resonance Imaging, Thermography and many more are available to detect the disease. But these techniques create much stress and pain, besides employing harmful rays for detection, to the patient undergoing them. Hence for early detection other categories of techniques can be implemented. Machine-learning assisted detection and classification is one such alternative. In this paper a hyper parameter optimized extreme gradient boosting model implemented along with F-Score feature selection is proposed and the model is used for classification of the breast tumor as either malignant or benign on the Wisconsin Breast Cancer dataset. The implementation of feature importance is investigated using F-Score and this is used for selecting the most relevant features that influence the target variable and classification is based on this. Experimentation is done using different training-testing partitions and the best performance of 99.27% accuracy score was shown by the 80−20 partition by the proposed XGBoost and F-Score Model.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66331041","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 : 2023-01-01DOI: 10.12720/jait.14.3.426-430
E. Blancaflor, Sasky A. Samonte
—Starting an e-commerce website has been one of the most successful business ideas in recent years. Managing an e-commerce website used to be challenging, but thanks to advances in technology, it is now feasible to successfully manage an e-commerce website by choosing the right e-commerce platform. Almost every company nowadays has a website, particularly those that cater to digital or internet-based clientele. Starting a modest online store is straightforward, but as the company expands, the expectations get more specialized, and they are not met. Unfortunately, “ready to go” solutions are typically resistive to acceptance, meaning that all individual changes are not warmly welcomed. This study analyzed and compared the two types of software used in building e-commerce websites in the Philippines’ popular websites and detected the current web technologies and conducted an online survey using qualitative approach with the participation of experts and familiar with e-commerce system. It also to identified what are the things need to consider when choosing software. As results from the surveys on e-commerce software, the most significant variables to consider when choosing an e-commerce software, whether proprietary or open source, are security and performance, followed by time and budget when establishing an e-commerce website.
{"title":"An Analysis and Comparison of Proprietary and Open-Source Software for Building E-commerce Website: A Case Study","authors":"E. Blancaflor, Sasky A. Samonte","doi":"10.12720/jait.14.3.426-430","DOIUrl":"https://doi.org/10.12720/jait.14.3.426-430","url":null,"abstract":"—Starting an e-commerce website has been one of the most successful business ideas in recent years. Managing an e-commerce website used to be challenging, but thanks to advances in technology, it is now feasible to successfully manage an e-commerce website by choosing the right e-commerce platform. Almost every company nowadays has a website, particularly those that cater to digital or internet-based clientele. Starting a modest online store is straightforward, but as the company expands, the expectations get more specialized, and they are not met. Unfortunately, “ready to go” solutions are typically resistive to acceptance, meaning that all individual changes are not warmly welcomed. This study analyzed and compared the two types of software used in building e-commerce websites in the Philippines’ popular websites and detected the current web technologies and conducted an online survey using qualitative approach with the participation of experts and familiar with e-commerce system. It also to identified what are the things need to consider when choosing software. As results from the surveys on e-commerce software, the most significant variables to consider when choosing an e-commerce software, whether proprietary or open source, are security and performance, followed by time and budget when establishing an e-commerce website.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66331298","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 : 2023-01-01DOI: 10.12720/jait.14.3.479-487
C. Pham-Quoc, T. N. Thinh
—In recent years, accelerating convolutional neural networks on Field Programmable Gate Array (FPGA) to improve the performance of the inference phase of artificial intelligent edge computing applications is a promising approach. This paper presents our proposed architecture for building a convolution neural network acceleration core on FPGA. The proposed FPGA-based core targets edge computing platforms where hardware resources and power efficiency are essential requirements. We use the MobileNet neural network model for image classification as a case study to evaluate our proposed system. We compare our work with a quad-core ARM Cortex processor at 1.2GHz and achieve speed-ups by up to 14.77 × convolution operators. Although our system is worse than a 6-core Intel Core i7 processor, it is more energy-efficiency than the Intel processor. Our proposed system is the best fit for edge computing.
{"title":"Efficient FPGA-Based Convolutional Neural Network Implementation for Edge Computing","authors":"C. Pham-Quoc, T. N. Thinh","doi":"10.12720/jait.14.3.479-487","DOIUrl":"https://doi.org/10.12720/jait.14.3.479-487","url":null,"abstract":"—In recent years, accelerating convolutional neural networks on Field Programmable Gate Array (FPGA) to improve the performance of the inference phase of artificial intelligent edge computing applications is a promising approach. This paper presents our proposed architecture for building a convolution neural network acceleration core on FPGA. The proposed FPGA-based core targets edge computing platforms where hardware resources and power efficiency are essential requirements. We use the MobileNet neural network model for image classification as a case study to evaluate our proposed system. We compare our work with a quad-core ARM Cortex processor at 1.2GHz and achieve speed-ups by up to 14.77 × convolution operators. Although our system is worse than a 6-core Intel Core i7 processor, it is more energy-efficiency than the Intel processor. Our proposed system is the best fit for edge computing.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66331353","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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