Annals of Emerging Technologies in Computing最新文献_第3页

SkCanNet: A Deep Learning based Skin Cancer Classification Approach SkCanNet:基于深度学习的皮肤癌分类方法

Annals of Emerging Technologies in Computing Pub Date : 2023-10-01 DOI: 10.33166/aetic.2023.04.004

J.Andrew Onesimu, Varun Unnikrishnan Nair, Martin K. Sagayam, Jennifer Eunice, Mohd Helmy abd Wahab, Nor’Aisah Sudin

引用次数: 0

A Comparative Analysis of Design Principles for Integration in Wearable Persuasive Multimedia 可穿戴式劝导多媒体集成设计原则的比较分析

Annals of Emerging Technologies in Computing Pub Date : 2023-10-01 DOI: 10.33166/aetic.2023.04.002

Umi Hanim Mazlan, Siti Mahfuzah Sarif

{"title":"A Comparative Analysis of Design Principles for Integration in Wearable Persuasive Multimedia","authors":"Umi Hanim Mazlan, Siti Mahfuzah Sarif","doi":"10.33166/aetic.2023.04.002","DOIUrl":"https://doi.org/10.33166/aetic.2023.04.002","url":null,"abstract":"Many studies, to varying degrees, have confirmed the importance of persuasive approaches in wearable technology. Meanwhile, there are also a growing number of studies in persuasive multimedia, particularly in promoting awareness. Also, many studies reported on wearable multimedia, especially in game-based and VR/AR applications. Given the increasing emergence of these technologies, there is a need to integrate existing diverse research endeavours and consolidate them for improved planned effects on human attitude and behaviour, including one's awareness. However, a similar attempt to incorporate a triad of persuasive, multimedia and wearable design principles toward improved controllability awareness lacks empirical evidence. Here, this study explores the design principles of persuasive, multimedia and wearable technologies that can be leveraged into an integrated design model, especially in promoting controllability awareness of mental health issues. Moreover, this study believes exploring the potential integration of the design principles would significantly impact the application's effectiveness. Therefore, this study conducted a comparative analysis which involved 20 relevant studies pertinent to wearable design principles, persuasive design principles, and multimedia design principles. Furthermore, all identified studies were reviewed regarding the domain, the technology used, target outcomes, and utilisation of the design principles. As a result, this study discovered that many studies were on integrating persuasive and multimedia design principles and persuasive and wearable technologies. Therefore, the outcome of this study could be leveraged to incorporate all three design principles (i.e., wearable, persuasive technology, multimedia) into a conceptual model. The conceptual model is expected to produce a more effective result, especially in enhancing controllability awareness in the mental health domain.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135369261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Leading but Simple Classification Method for Remote Sensing Images 一种领先但简单的遥感图像分类方法

Annals of Emerging Technologies in Computing Pub Date : 2023-07-01 DOI: 10.33166/aetic.2023.03.001

Huaxiang Song

{"title":"A Leading but Simple Classification Method for Remote Sensing Images","authors":"Huaxiang Song","doi":"10.33166/aetic.2023.03.001","DOIUrl":"https://doi.org/10.33166/aetic.2023.03.001","url":null,"abstract":"Recently, researchers have proposed a lot of deep convolutional neural network (CNN) approaches with obvious flaws to tackle the difficult semantic classification (SC) task of remote sensing images (RSI). In this paper, the author proposes a simple method that aims to provide a leading but efficient solution by using a lightweight EfficientNet-B0. First, this paper concluded the drawbacks with an analysis of mathematical theory and then proposed a qualitative conclusion on the previous methods’ theoretical performance based on theoretical derivation and experiments. Following that, the paper designs a novel method named LS-EfficientNet, consisting only of a single CNN and a concise training algorithm called SC-CNN. Far different from previous complex and hardware-extensive ones, the proposed method mainly focuses on tackling the long-neglected problems, including overfitting, data distribution shift by DA, improper use of training tricks, and other incorrect operations on a pre-trained CNN. Compared to previous studies, the proposed method is easy to reproduce because all the models, training tricks, and hyperparameter settings are open-sourced. Extensive experiments on two benchmark datasets show that the proposed method can easily surpass all the previous state-of-the-art ones, with an outstanding accuracy lead of 0.5% to 1.2% and a remarkable parameter decrease of 78% if compared to the best prior one in 2022. In addition, ablation test results also prove that the proposed effective combination of training tricks, including OLS and CutMix, can clearly boost a CNN's performance for RSI-SC, with an increase in accuracy of 1.0%. All the results reveal that a single lightweight CNN can well tackle the routine task of classifying RSI.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49512635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Handwritten Bengali Alphabets, Compound Characters and Numerals Recognition Using CNN-based Approach 基于CNN的孟加拉语手写字母、复合字符和数字识别方法

Annals of Emerging Technologies in Computing Pub Date : 2023-07-01 DOI: 10.33166/aetic.2023.03.003

Md Asraful, Md Anwar Hossain, Ebrahim Hossen

{"title":"Handwritten Bengali Alphabets, Compound Characters and Numerals Recognition Using CNN-based Approach","authors":"Md Asraful, Md Anwar Hossain, Ebrahim Hossen","doi":"10.33166/aetic.2023.03.003","DOIUrl":"https://doi.org/10.33166/aetic.2023.03.003","url":null,"abstract":"Accurately classifying user-independent handwritten Bengali characters and numerals presents a formidable challenge in their recognition. This task becomes more complicated due to the inclusion of numerous complex-shaped compound characters and the fact that different authors employ diverse writing styles. Researchers have recently conducted significant researches using individual approaches to recognize handwritten Bangla digits, alphabets, and slightly compound characters. To address this, we propose a straightforward and lightweight convolutional neural network (CNN) framework to accurately categorize handwritten Bangla simple characters, compound characters, and numerals. The suggested approach exhibits outperformance in terms of performance when compared too many previously developed procedures, with faster execution times and requiring fewer epochs. Furthermore, this model applies to more than three datasets. Our proposed CNN-based model has achieved impressive validation accuracies on three datasets. Specifically, for the BanglaLekha isolated dataset, which includes 84-character classes, the validation accuracy was 92.48%. On the Ekush dataset, which includes 60-character classes, the model achieved a validation accuracy of 97.24%, while on the customized dataset, which includes 50-character classes, the validation accuracy was 97.03%. Our model has demonstrated high accuracy and outperformed several prominent existing frameworks.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45821077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Enhancing the Efficiency of Diabetes Prediction through Training and Classification using PCA and LR Model 通过PCA和LR模型的训练和分类提高糖尿病预测的效率

Annals of Emerging Technologies in Computing Pub Date : 2023-07-01 DOI: 10.33166/aetic.2023.03.004

M. R. Belgaum, Telugu Harsha Charitha, Munurathi Harini, B. Anusha, Ala Jayasri Sai, Undralla Chandana Yadav, Z. Alansari

{"title":"Enhancing the Efficiency of Diabetes Prediction through Training and Classification using PCA and LR Model","authors":"M. R. Belgaum, Telugu Harsha Charitha, Munurathi Harini, B. Anusha, Ala Jayasri Sai, Undralla Chandana Yadav, Z. Alansari","doi":"10.33166/aetic.2023.03.004","DOIUrl":"https://doi.org/10.33166/aetic.2023.03.004","url":null,"abstract":"In this paper, we introduce a new approach for predicting the risk of diabetes using a combination of Principal Component Analysis (PCA) and Logistic Regression (LR). Our method offers a unique solution that could lead to more accurate and efficient predictions of diabetes risk. To develop an effective model for predicting diabetes, it is important to consider various clinical and demographic factors contributing to the disease's development. This approach typically involves training the model on a large dataset that includes these factors. By doing so, we can better understand how different characteristics can impact the development of diabetes and create more accurate predictions for individuals at risk. The PCA method is employed to reduce the dataset's dimensions and augment the model's computational efficacy. The LR model then classifies patients into diabetic or non-diabetic groups. Accuracy, precision, recall, the F1-score, and the area under the ROC curve (AUC) are only a few of the indicators used to evaluate the performance of the proposed model. Pima Indian Diabetes Data (PIDD) is used to evaluate the model, and the results demonstrate a significant improvement over the state-of-the-art methods. The proposed model presents an efficient and effective method for predicting diabetes risk that may have significant implications for improving healthcare outcomes and reducing healthcare costs. The proposed PCA-LR model outperforms other algorithms, such as SVM and RF, especially in terms of accuracy, while optimizing computational complexity. This approach can potentially provide a practical and efficient solution for large-scale diabetes screening programs.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48344743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GreenPy: Evaluating Application-Level Energy Efficiency in Python for Green Computing GreenPy:在Python中评估绿色计算的应用级能源效率

Annals of Emerging Technologies in Computing Pub Date : 2023-07-01 DOI: 10.33166/aetic.2023.03.005

Nurzihan Fatema Reya, Abtahi Ahmed, T. Zaman, Md. Motaharul Islam

{"title":"GreenPy: Evaluating Application-Level Energy Efficiency in Python for Green Computing","authors":"Nurzihan Fatema Reya, Abtahi Ahmed, T. Zaman, Md. Motaharul Islam","doi":"10.33166/aetic.2023.03.005","DOIUrl":"https://doi.org/10.33166/aetic.2023.03.005","url":null,"abstract":"The increased use of software applications has resulted in a surge in energy demand, particularly in data centers and IT infrastructures. As global energy consumption is projected to surpass supply by 2030, the need to optimize energy consumption in programming has become imperative. Our study explores the energy efficiency of various coding patterns and techniques in Python, with the objective of guiding programmers to a more informed and energy-conscious coding practices. The research investigates the energy consumption of a comprehensive range of topics, including data initialization, access patterns, structures, string formatting, sorting algorithms, dynamic programming and performance comparisons between NumPy and Pandas, and personal computers versus cloud computing. The major findings of our research include the advantages of using efficient data structures, the benefits of dynamic programming in certain scenarios that saves up to 0.128J of energy, and the energy efficiency of NumPy over Pandas for numerical calculations. Additionally, the study also shows that assignment operator, sequential read, sequential write and string concatenation are 2.2 times, 1.05 times, 1.3 times and 1.01 times more energy-efficient choices, respectively, compared to their alternatives for data initialization, data access patterns, and string formatting. Our findings offer guidance for developers to optimize code for energy efficiency and inspire sustainable software development practices, contributing to a greener computing industry.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41504655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Monte Carlo Simulation of Cone X-ray Beam and Dose Scoring on Voxel Phantom with Open Source Software EGSnrcmp 基于开源软件EGSnrcmp的锥形x射线束蒙特卡罗模拟及体素幻影剂量评分

Annals of Emerging Technologies in Computing Pub Date : 2023-04-01 DOI: 10.33166/aetic.2023.02.003

N. Chatzisavvas, D. Nikolopoulos, G. Priniotakis, I. Valais, T. Koustas, Georgios Karpetas

{"title":"Monte Carlo Simulation of Cone X-ray Beam and Dose Scoring on Voxel Phantom with Open Source Software EGSnrcmp","authors":"N. Chatzisavvas, D. Nikolopoulos, G. Priniotakis, I. Valais, T. Koustas, Georgios Karpetas","doi":"10.33166/aetic.2023.02.003","DOIUrl":"https://doi.org/10.33166/aetic.2023.02.003","url":null,"abstract":"Radiation is used nowadays for inspection, therapy, food safety, and diagnostic purposes. Our daily lives include the use of devices like airport scanners, projectional radiographers, CT scanners, treatment heads, cargo inspection systems, etc. However, these systems are extremely complicated and cost a significant amount of money to study, maintain and conduct research with. Monte Carlo is the ideal method for simulating such systems successfully and achieving findings that are remarkably comparable to experimental methods. Simulation software, however, is not always free, open source, and accessible to everyone. Open source software has gained popularity in the technological age that best represents the period we are living in, and practically all significant software sectors now use open source software tools. With the aid of an open-source, thoroughly validated software, called EGSnrcmp we were able to describe an abstract model-geometry of a cone-beam computed tomography X-rays source, produce patient-specific phantoms and score dosage values based on characteristics of the cone beam source. We outline the necessary methods and provide useful details about how to conduct such studies inside the software's ecosystem. Our study focuses on the relationship between the cone-beam source's field of view (FOV) and its impact on patient dosage, by emulating a CBCT examination. To characterize our cbct source, we employed stainless steel material to build the collimator and tungsten (W) material to build the anode. The most frequent energy at which these tests are conducted is 100 keV, which is the energy of the electrons we utilize. We were able to score absorbed dosage within a phantom produced from dicom images of a real patient, demonstrate the relationship between the FOV of the beam and the absorbed dosage and verify the cbct source using theoretical values.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47808452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Study of Prediction Accuracy of English Test Performance Using Data Mining and Analysis 基于数据挖掘与分析的英语考试成绩预测准确性研究

Annals of Emerging Technologies in Computing Pub Date : 2023-04-01 DOI: 10.33166/aetic.2023.02.001

Yujie Duan

引用次数: 0

Weighted Sum Metrics – Based Load Balancing RPL Objective Function for IoT 基于加权和度量的物联网负载平衡RPL目标函数

Annals of Emerging Technologies in Computing Pub Date : 2023-04-01 DOI: 10.33166/aetic.2023.02.004

Poorana Senthilkumar Subramani, Subramani Bojan

{"title":"Weighted Sum Metrics – Based Load Balancing RPL Objective Function for IoT","authors":"Poorana Senthilkumar Subramani, Subramani Bojan","doi":"10.33166/aetic.2023.02.004","DOIUrl":"https://doi.org/10.33166/aetic.2023.02.004","url":null,"abstract":"The technological development of Internet of Things (IoT) applications is emerging and attracting the attention of the real world in the automated industry, agriculture, environment, and scientific community. In most scenarios, extending the network lifetime of an IoT network is highly challenging because of constrained nodes. The wireless sensor network (WSN) is the core component of IoT applications. In addition, the WSN nodes are required for the network processes, particularly routing, energy maintenance, load balance, congestion control, packet delivery, quick response, and more. The failure of any of the above network processes will affect the entire network operation. IPv6 Routing Protocol for Low-power and Lossy network (RPL) provides high routing solutions to IoT applications requirements. The load balance, congestion control, traffic load, and bottleneck problems are still open issues in the RPL. To resolve the load balance issue, we propose a weighted sum method objective function (WSM-OF), which provides the ability to select the alternative parent in routing by RPL metrics. WSM-OF adopts congestion control and load balancing to avoid heavy traffic and extend the network's node lifetime. The network parameters of control overhead, jitter, packet delivery ratio, parent switching, energy consumption, latency, and network lifetime are implemented and analyzed through the COOJA simulator. The result shows that the WSM-OF improves the network performance and significantly enhances the network lifetime by up to 7.8%.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45237511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Feature Extraction Technique Through Spatial Deep Learning Model for Facial Emotion Detection 基于空间深度学习模型的人脸情感检测特征提取技术的改进

Annals of Emerging Technologies in Computing Pub Date : 2023-04-01 DOI: 10.33166/aetic.2023.02.002

Nizamuddin Khan, A. Singh, Rajeev Agrawal

{"title":"Enhancing Feature Extraction Technique Through Spatial Deep Learning Model for Facial Emotion Detection","authors":"Nizamuddin Khan, A. Singh, Rajeev Agrawal","doi":"10.33166/aetic.2023.02.002","DOIUrl":"https://doi.org/10.33166/aetic.2023.02.002","url":null,"abstract":"Automatic facial expression analysis is a fascinating and difficult subject that has implications in a wide range of fields, including human–computer interaction and data-driven approaches. Based on face traits, a variety of techniques are employed to identify emotions. This article examines various recent explorations into automatic data-driven approaches and handcrafted approaches for recognising face emotions. These approaches offer computationally complex solutions that provide good accuracy when training and testing are conducted on the same datasets, but they perform less well on the most difficult realistic dataset, FER-2013. The article's goal is to present a robust model with lower computational complexity that can predict emotion classes more accurately than current methods and aid society in finding a realistic, all-encompassing solution for the facial expression system. A crucial step in good facial expression identification is extracting appropriate features from the face images. In this paper, we examine how well-known deep learning techniques perform when it comes to facial expression recognition and propose a convolutional neural network-based enhanced version of a spatial deep learning model for the most relevant feature extraction with less computational complexity. That gives a significant improvement on the most challenging dataset, FER-2013, which has the problems of occlusions, scale, and illumination variations, resulting in the best feature extraction and classification and maximizing the accuracy, i.e., 74.92%. It also maximizes the correct prediction of emotions at 99.47%, and 98.5% for a large number of samples on the CK+ and FERG datasets, respectively. It is capable of focusing on the major features of the face and achieving greater accuracy over previous fashions.","PeriodicalId":36440,"journal":{"name":"Annals of Emerging Technologies in Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45744918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3