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BUSINESS DEVELOPMENT IN THE DIGITAL AGE 数字时代的业务发展
Pub Date : 2024-01-17 DOI: 10.47679/ijasca.v3i2.55
Helisia Margahana
In today's digital era, old systems and processes must be rethought, and new technologies must be implemented to keep businesses competitive and growing. High global competition provides its own demands for business people to continue to improve product innovation by utilising existing technology to face this global challenge. Data collection techniques on business development in the digital era are conducted through online data analysis, literature studies obtained from Google Scholar, online surveys and social media monitoring to collect information on digital businesses. The use of technologies such as big data and sentiment analysis can also help in understanding the changes that occur in the digital business ecosystem. Technology and the internet have opened up new opportunities for businesses to reach a wider market, improve operational efficiency, and accelerate business growth. High global competition puts its own demands on businesses to continuously improve product innovation by utilising existing technology to face these global challenges. Businesses that succeed in the digital era are those that can adapt quickly and remain responsive to changes in the market and technology. Businesses need to adapt to technological developments and utilise them to improve business quality and expand market reach.
在当今的数字化时代,必须对旧的系统和流程进行反思,并采用新技术来保持企业的竞争力和发展。激烈的全球竞争对企业人员提出了自身的要求,即利用现有技术不断改进产品创新,以应对这一全球性挑战。有关数字时代企业发展的数据收集技术是通过在线数据分析、从谷歌学术中获得的文献研究、在线调查和社交媒体监测来收集有关数字企业的信息。大数据和情感分析等技术的使用也有助于了解数字商业生态系统发生的变化。技术和互联网为企业进入更广阔的市场、提高运营效率和加快业务增长带来了新的机遇。激烈的全球竞争要求企业利用现有技术不断提高产品创新能力,以应对这些全球性挑战。能够在数字时代取得成功的企业,都是那些能够快速适应并对市场和技术变化保持快速反应的企业。企业需要适应技术发展,并利用技术发展来提高业务质量和扩大市场范围。
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引用次数: 0
Convolutional Arabic handwriting recognition system based BLSTM-CTC using WBS decoder 使用 WBS 解码器的基于 BLSTM-CTC 的卷积阿拉伯语手写识别系统
Pub Date : 2024-01-16 DOI: 10.47679/ijasca.v3i2.52
M. Rabi
Arabic handwriting recognition (AHR) poses major challenges for pattern recognition due to the cursive script and visual similarity of Arabic characters. While deep learning demonstrates promise, architectural enhancements may further improve performance. This study presents an offline AHR approach using a convolutional neural network (CNN) with bidirectional long short-term memory (BLSTM) and connectionist temporal classification (CTC). By enhancing temporal modeling and context representations without segmentation requirements, this BLSTM-CTC-CNN framework with an integrated Word Beam Search (WBS) decoder achieved 94.58% accuracy on the IFN/ENIT database. Results highlight improved efficiency over prior works. This demonstrates continued advancement in sophisticated deep learning techniques for accurate AHR through specialized modeling of Arabic script cursive properties and decoding constraints. This research represents an advancement in the continuous development of progressively intricate and precise systems for handwriting recognition.
阿拉伯语手写识别(AHR)因其草书字体和阿拉伯字符的视觉相似性,给模式识别带来了重大挑战。虽然深度学习前景广阔,但架构上的改进可能会进一步提高性能。本研究提出了一种离线 AHR 方法,该方法使用了具有双向长短期记忆(BLSTM)和联结时态分类(CTC)的卷积神经网络(CNN)。通过增强时间建模和上下文表示而无需分段要求,这种带有集成词束搜索(WBS)解码器的 BLSTM-CTC-CNN 框架在 IFN/ENIT 数据库上实现了 94.58% 的准确率。与之前的研究相比,结果凸显了效率的提高。这表明,通过对阿拉伯文草书特性和解码约束进行专门建模,在实现精确 AHR 的复杂深度学习技术方面取得了持续进步。这项研究标志着手写识别系统在逐步复杂化和精确化方面的不断发展。
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引用次数: 0
ANALYSIS OF FACTORS INFLUENCING SATISFACTION WITH USING THE MOBILE LAPOR APPLICATION IN INDONESIA 影响印度尼西亚移动电话应用满意度的因素分析
Pub Date : 2024-01-09 DOI: 10.47679/ijasca.v3i2.51
Theresa Karyn Wijaya
The rapid digital growth in Indonesia has prompted the government to leverage digital-based services to address the needs and concerns of its citizens. In this context, the LAPOR (People's Online Aspiration and Complaints Service) mobile application and website have been established as a means for Indonesians to voice complaints, aspirations, and requests for information to government agencies. However, user satisfaction with the LAPOR application has been suboptimal, as evidenced by user ratings and comments on platforms such as Playstore and AppStore. This research aims to identify the factors influencing user satisfaction with the LAPOR mobile application in Indonesia. The study focuses on variables including ease of use, security privacy, system quality, speed of platform response, and attitude toward use. The research methodology involves primary data collection through the distribution of questionnaires via Google Form to Indonesian users residing in specific regions. The validity and reliability of the research are ensured through rigorous testing of dependent, independent, and intervening variables. The findings of the research highlight the significant influence of security privacy on system quality, ease of use on user satisfaction, and system quality on ease of use. These results provide valuable insights for the government of Indonesia to enhance the effectiveness of the LAPOR system in addressing public complaints and aspirations. By addressing these factors, the government can improve user satisfaction and engagement with the LAPOR mobile application, ultimately leading to more effective public service delivery and citizen engagement. Keywords: Mobile Application, User Satisfaction, Technology Acceptance Model (TAM), Public Complaints, Indonesia Public Service Delivery
印度尼西亚数字技术的快速发展促使政府利用数字服务来满足公民的需求和关切。在此背景下,印尼政府建立了 LAPOR(人民在线愿望和投诉服务)移动应用程序和网站,作为印尼人向政府机构提出投诉、愿望和信息请求的途径。然而,从 Playstore 和 AppStore 等平台上的用户评分和评论来看,用户对 LAPOR 应用程序的满意度并不理想。本研究旨在确定影响印度尼西亚用户对 LAPOR 移动应用程序满意度的因素。研究重点关注易用性、安全隐私、系统质量、平台响应速度和使用态度等变量。研究方法包括通过谷歌表格向居住在特定地区的印尼用户发放问卷,收集原始数据。通过对因变量、自变量和干预变量的严格测试,确保了研究的有效性和可靠性。研究结果凸显了安全隐私对系统质量、易用性对用户满意度以及系统质量对易用性的重要影响。这些结果为印尼政府提高 LAPOR 系统在解决公众投诉和愿望方面的有效性提供了有价值的见解。通过解决这些因素,政府可以提高用户对 LAPOR 移动应用程序的满意度和参与度,最终实现更有效的公共服务提供和公民参与。关键词移动应用、用户满意度、技术接受模型(TAM)、公众投诉、印度尼西亚公共服务提供
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引用次数: 0
Utilizing a pseudo-random Linear Congruential Generator (LCG) S-Box for encoding color images through genetic crossover 利用伪随机线性公有生成器 (LCG) S-Box 通过遗传交叉对彩色图像进行编码
Pub Date : 2023-12-26 DOI: 10.47679/ijasca.v3i2.41
A. Jarjar
This article introduces a novel algorithm crafted for encrypting color images. The algorithm leverages chaotic principles and harnesses fresh substitution tables derived from independent linear congruence generators. These generators are dynamically sized based on pseudo-random vectors used in this technology. The proposed method initiates with the original image, vectorization, employing selected chaotic maps. The primary goal revolves around implementing a genetic operator tailored for image encryption, who integrates an enhanced Vigenère technique, incorporating novel confusion and diffusion functions derived from the previously established substitution tables. To gauge the effectiveness of this approach, numerous color images of varying dimensions and formats underwent testing using our algorithm. The yielded outcomes are both promising and gratifying, furnishing heightened security against recognized attacks.
本文介绍了一种用于加密彩色图像的新型算法。该算法利用混沌原理,利用从独立线性同调发生器中衍生出的新鲜替换表。这些生成器的大小是根据这项技术中使用的伪随机向量动态确定的。建议的方法从原始图像开始,利用选定的混沌图进行矢量化。其主要目标是为图像加密量身定制一个遗传算子,该算子集成了增强型维尼哲技术,并结合了从先前建立的替换表中提取的新型混淆和扩散函数。为了衡量这种方法的有效性,使用我们的算法对大量不同尺寸和格式的彩色图像进行了测试。测试结果令人满意,提高了识别攻击的安全性。
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引用次数: 0
A Comparative Study of Embedded Learning Models IoT-based for real time Mask Detection 基于嵌入式学习模型的物联网实时掩码检测比较研究
Pub Date : 2023-12-26 DOI: 10.47679/ijasca.v3i2.49
Mohamed Amine Meddaoui, M. Erritali, Françoise Sailhan
Following the outbreak of the coronavirus, many preventive measures are implemented to slow down the transmission of the virus. Amongst others, facemask detection is a key innovative technology that allows the identificationof the number of individuals wearing face masks. In this regard, this paperprovides a comparative study of several machine learning and deep learningalgorithms (e.g., SVM, RNN, Mask-RCNN, LSTM, CNN, Auto-Encoder,GAN, U-Net GAN) that support mask detection.
冠状病毒爆发后,为减缓病毒传播,采取了许多预防措施。其中,口罩检测是一项关键的创新技术,它可以识别佩戴口罩的人数。为此,本文对支持口罩检测的几种机器学习和深度学习算法(如 SVM、RNN、Mask-RCNN、LSTM、CNN、Auto-Encoder、GAN、U-Net GAN)进行了比较研究。
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引用次数: 0
Client-server Architecture, a Review 客户端-服务器架构回顾
Pub Date : 2023-12-13 DOI: 10.47679/ijasca.v3i1.48
Geofrey Nyabuto
Client-server architecture is a software model through which resources and requests are serviced over a network. The client requests a resource over a network, and the server receives the request, processes it, and responds appropriately. With this model, multiple users can simultaneously access and use resources. This paper provides an overview of the architecture, outlining its characteristics, advantages, disadvantages, different implementations of the architecture as well as the current and future of this architecture.
客户端-服务器架构是一种通过网络为资源和请求提供服务的软件模型。客户端通过网络请求资源,服务器接收请求、处理请求并做出适当响应。通过这种模式,多个用户可以同时访问和使用资源。本文概述了该架构,概述了其特点、优缺点、架构的不同实现方式以及该架构的现状和未来。
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引用次数: 0
Characteristic behavior of soil using bacterial biogrouting with LISA FEA V.8. 利用 LISA FEA V.8 进行细菌生物路由的土壤特征行为。
Pub Date : 2023-11-17 DOI: 10.47679/ijasca.v3i1.47
A. W. Efendi
The increasingly widespread use of micro-bacteria in research to improve soil characteris-tics in the world of construction and also provide a significant increase in soil carrying ca-pacity so that this kind of research makes a very good contribution where the material and mixing object are natural materials, namely in the form of bacteria. This study used the finite element method with the help of LISA V.8 FEA (Li-cense), a finite element method software package, to obtain the stress arising from existing soil ma-terial with soil material that has been mixed with mycobac-teria or the biogrouting method. According from the results of the analysis using numerical analysis using the finite el-ement method LISA V.8 FEA program, it can be seen that there is a reduc-tion in the oc-currence of settlement after adding the two bacteria which have been aged for 30 days with a reduction in soil settlement of 2.9-2.27 mm de-spite an increase in stress. ranging from 58.3 to 69.4 kN/m2.
微生物细菌在研究中的应用日益广泛,不仅可以改善建筑领域的土壤特性,还能显著提高土壤的承载能力,因此,当材料和混合对象都是天然材料(即细菌形式)时,这种研究会做出很好的贡献。本研究利用有限元法软件包 LISA V.8 FEA (Li-cense),采用有限元法获得了现有土壤材料与混合了真菌的土壤材料或生物布道法产生的应力。根据使用有限元分析法 LISA V.8 FEA 程序进行数值分析的结果,可以看出,在添加两种菌种 30 天后,沉降发生率有所降低,土壤沉降量减少了 2.9-2.27 毫米,尽管应力增加了 58.3 至 69.4 千牛/平方米。
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引用次数: 0
E-passport security systems and attack implications 电子护照安全系统和攻击影响
Pub Date : 2023-11-15 DOI: 10.47679/ijasca.v3i1.38
Kaznah Alshammari
Recent technological advances aim to recognise user data using biometrics such as the face, fingerprint, hand veins and iris. Currently, face prints are widely used to verify user data in e-passports. As a result, institutions face substantial difficulties in maintaining an appropriate level of security. Human error can introduce flaws that undermine security mechanisms. One potential solution to this problem is to install a facial recognition security system. Both hardware and software components make up this system, with the hardware being a camera and the software comprising face detection and identification algorithms.   The purpose of this essay is to provide a thorough understanding of the Face Recognition Security System, including its application and deployment. Furthermore, the essay investigates the various weaknesses and methods of attack that could be used to target the system. The purpose of addressing these factors is to improve the effectiveness and robustness of system security, notably e-passport security.
最近的技术进步旨在利用生物识别技术(如人脸、指纹、手脉和虹膜)识别用户数据。目前,脸部指纹被广泛用于验证电子护照中的用户数据。因此,各机构在维持适当的安全级别方面面临很大困难。人为错误会带来破坏安全机制的缺陷。解决这一问题的一个潜在办法是安装面部识别安全系统。该系统由硬件和软件两部分组成,硬件是摄像头,软件包括人脸检测和识别算法。 本文的目的是透彻了解人脸识别安全系统,包括其应用和部署。此外,本文还将研究可用于攻击该系统的各种弱点和方法。解决这些因素的目的是提高系统安全,特别是电子护照安全的有效性和稳健性。
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引用次数: 0
Unraveling COVID-19 Progression: A Comprehensive Com-parison of Advanced Deep Learning Methods for Precise Pre-dictions 解读 COVID-19 进展:全面比较高级深度学习方法,实现精确预判
Pub Date : 2023-11-13 DOI: 10.47679/ijasca.v3i1.37
Muhammad Usman Tariq, Shuhaida Binti Ismail
The COVID-19 pandemic has significantly impacted the United Arab Emirates (UAE), necessitating effective and accurate forecasting tools to inform public health policies and strategies. This study presents a comparative analysis of advanced deep-learning models for predicting COVID-19 cases in the UAE. We investigate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer perceptron, and Recurrent Neural Networks (RNN). The models are trained and evaluated using a comprehensive dataset of confirmed cases, demographic information, and relevant socio-economic indicators. The models are further optimized using a Bayesian optimizer and comparison is performed before and after the optimization of models. We have used predictive and perspective analytics on the COVID-19 dataset. Our research goal is to identify the most accurate and reliable model for forecasting COVID-19 cases in the region. The results demonstrate the effectiveness of these deep learning techniques in predicting COVID-19 cases, with each model exhibiting varying levels of accuracy and precision. A thorough and rigorous evaluation of the models' performances reveals the most suitable architecture for the UAE's specific context. This study contributes to the ongoing efforts to combat the pandemic by providing valuable insights into the application of advanced deep-learning models for accurate and timely COVID-19 case predictions. It was found that the RNN model performed the best without any optimization. The findings have significant implications for public health decision-making, enabling authorities to develop targeted and data-driven interventions to curb the spread of the virus and mitigate its impact on the UAE's population. This demonstrates the potential of deep learning algorithms in handling complex datasets and making accurate predictions which is a valuable capability to enhance accuracy in professional and healthcare environments.
COVID-19 大流行对阿拉伯联合酋长国(UAE)产生了重大影响,因此需要有效、准确的预测工具来为公共卫生政策和战略提供信息。本研究对用于预测阿联酋 COVID-19 病例的高级深度学习模型进行了比较分析。我们研究了长短期记忆(LSTM)、双向 LSTM、卷积神经网络(CNN)、CNN-LSTM、多层感知器和循环神经网络(RNN)的性能。使用包含确诊病例、人口信息和相关社会经济指标的综合数据集对这些模型进行了训练和评估。使用贝叶斯优化器进一步优化模型,并在模型优化前后进行比较。我们在 COVID-19 数据集上使用了预测和透视分析技术。我们的研究目标是找出预测该地区 COVID-19 病例的最准确、最可靠的模型。 研究结果证明了这些深度学习技术在预测 COVID-19 病例方面的有效性,每个模型都表现出了不同程度的准确性和精确性。对模型性能的全面、严格评估揭示了最适合阿联酋具体情况的架构。本研究为应用先进的深度学习模型准确、及时地预测 COVID-19 病例提供了宝贵的见解,为抗击大流行病的持续努力做出了贡献。研究发现,RNN 模型在未进行任何优化的情况下表现最佳。这些发现对公共卫生决策具有重要意义,使当局能够制定有针对性的数据驱动干预措施,以遏制病毒传播并减轻其对阿联酋人口的影响。这证明了深度学习算法在处理复杂数据集和进行准确预测方面的潜力,而这正是提高专业和医疗环境准确性的宝贵能力。
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引用次数: 0
IoT-based intelligent system For Alzheimer's Disease Detection & Monitoring 基于物联网的阿尔茨海默病检测与监控智能系统
Pub Date : 2023-11-12 DOI: 10.47679/ijasca.v3i1.39
Mohamed Riad
This research is based on two areas related to Alzheimer's disease, the first is the early detection and diagnosis of Alzheimer's disease using deep learning techniques and its various algorithms, and the second relates to how to monitor and follow up on Alzheimer's disease using the Internet of Things (IOT). In this paper, a new diagnosis based on deep machine learning and monitoring of diseases similar to Alzheimer's is proposed. Diagnosis of Alzheimer's-like diseases is achieved through deep learning magnetic resonance imaging (MRI) analysis followed by an activity tracking framework to monitor people's activities in daily life using wearable inertial sensors. Activity monitoring provides a framework for assistance in activities of daily living and assessment of patient deterioration based on activity level. The results of Alzheimer's diagnosis show an improvement of up to 86.34% with respect to current known techniques. Furthermore, greater than 95% accuracy was achieved for classifying activities of daily living, which is very encouraging in looking at the subject's activity profile.
这项研究基于与阿尔茨海默病相关的两个领域,一是利用深度学习技术及其各种算法对阿尔茨海默病进行早期检测和诊断,二是如何利用物联网(IOT)对阿尔茨海默病进行监测和跟踪。 本文提出了一种基于深度机器学习的新型诊断方法,并对类似阿尔茨海默氏症的疾病进行监测。通过深度学习磁共振成像(MRI)分析实现阿尔茨海默氏症类似疾病的诊断,然后利用活动跟踪框架,使用可穿戴惯性传感器监测人们在日常生活中的活动。活动监测为日常生活活动提供了一个帮助框架,并根据活动水平评估患者的病情恶化情况。 与目前已知的技术相比,阿尔茨海默氏症的诊断结果表明,诊断准确率提高了 86.34%。此外,对日常生活活动进行分类的准确率超过了 95%,这对了解受试者的活动情况非常有帮助。
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引用次数: 0
期刊
International Journal of Advanced Science and Computer Applications
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