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Enhancing Energy Efficiency in Cluster Based WSN using Grey Wolf Optimization 利用灰狼优化提高聚类WSN的能效
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-11-01 DOI: 10.14201/adcaij.30632
Ashok Kumar Rai, Lalit Kumar Tyagi, Anoop Kumar, Swapnita Srivastava, Naushen Fatima
Wireless sensor networks (WSNs) are typically made up of small, low-power sensor nodes (SNs) equipped with capability for wireless communication, processing, and sensing. These nodes collaborate with each other to form a self-organizing network. They can collect data from their surrounding environment, such as temperature, humidity, light intensity, or motion, and transmit it to a central base station (BS) or gateway for additional processing and analysis. LEACH and TSEP are examples of cluster-based protocols developed for WSNs. These protocols require careful design and optimization of CH selection algorithms, considering factors such as energy consumption, network scalability, data aggregation, load balancing, fault tolerance, and adaptability to dynamic network conditions. Various research efforts have been made to develop efficient CH selection algorithms in WSNs, considering these challenges and trade-offs. In this paper, the Grey Wolf Optimization (GWO) algorithm is employed to address the problem of selecting CHs (CHs) in WSNs. The proposed approach takes into account two parameters: Residual Energy (RE) and the distance of node (DS)s from the BS. By visualizing and analyzing the GWO algorithm under variable parameters in WSNs, this research identifies the most appropriate node from all normal nodes for CH selection. The experimental results demonstrate that the proposed model, utilizing GWO, outperforms other approaches in terms of performance.
无线传感器网络(wsn)通常由具有无线通信、处理和传感能力的小、低功耗传感器节点(SNs)组成。这些节点相互协作,形成一个自组织网络。它们可以从周围环境中收集数据,如温度、湿度、光照强度或运动,并将其传输到中央基站(BS)或网关,以进行额外的处理和分析。LEACH和TSEP是为无线传感器网络开发的基于集群的协议的例子。这些协议需要仔细设计和优化CH选择算法,考虑能耗、网络可扩展性、数据聚合、负载均衡、容错和对动态网络条件的适应性等因素。考虑到这些挑战和权衡,已经进行了各种研究工作,以开发有效的无线传感器网络中的CH选择算法。本文采用灰狼优化(GWO)算法来解决无线传感器网络中CHs的选择问题。该方法考虑了两个参数:剩余能量(RE)和节点到极点的距离(DS)s。通过可视化和分析WSNs变参数下的GWO算法,从所有正常节点中识别出最适合CH选择的节点。实验结果表明,利用GWO的模型在性能上优于其他方法。
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引用次数: 0
Comparison of Pre-trained vs Custom-trained Word Embedding Models for Word Sense Disambiguation 语义消歧的预训练词嵌入模型与自定义词嵌入模型的比较
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-11-01 DOI: 10.14201/adcaij.31084
Muhammad Farhat Ullah, Ali Saeed, Naveed Hussain
The prime objective of word sense disambiguation (WSD) is to develop such machines that can automatically recognize the actual meaning (sense) of ambiguous words in a sentence. WSD can improve various NLP and HCI challenges. Researchers explored a wide variety of methods to resolve this issue of sense ambiguity. However, majorly, their focus was on English and some other well-reputed languages. Urdu with more than 300 million users and a large amount of electronic text available on the web is still unexplored. In recent years, for a variety of Natural Language Processing tasks, word embedding methods have proven extremely successful. This study evaluates, compares, and applies a variety of word embedding approaches to Urdu Word embedding (both Lexical Sample and All-Words), including pre-trained (Word2Vec, Glove, and FastText) as well as custom-trained (Word2Vec, Glove, and FastText trained on the Ur-Mono corpus). Two benchmark corpora are used for the evaluation in this study: (1) the UAW-WSD-18 corpus and (2) the ULS-WSD-18 corpus. For Urdu All-Words WSD tasks, top results have been achieved (Accuracy=60.07 and F1=0.45) using pre-trained FastText. For the Lexical Sample, WSD has been achieved (Accuracy=70.93 and F1=0.60) using custom-trained GloVe word embedding method.
词义消歧(WSD)的主要目标是开发能够自动识别句子中歧义词的实际意义(意义)的机器。水务署可以改善各种NLP和HCI挑战。研究者们探索了各种各样的方法来解决这一问题。然而,他们主要关注的是英语和其他一些著名的语言。乌尔都语有超过3亿的用户,网络上有大量的电子文本,但乌尔都语仍未开发。近年来,对于各种自然语言处理任务,词嵌入方法已经被证明是非常成功的。本研究评估、比较并应用了多种乌尔都语词嵌入方法(包括Lexical Sample和All-Words),包括预训练(Word2Vec、Glove和FastText)和自定义训练(在Ur-Mono语料库上训练的Word2Vec、Glove和FastText)。本研究使用两个基准语料库进行评价:(1)UAW-WSD-18语料库和(2)ULS-WSD-18语料库。对于乌尔都语全词WSD任务,使用预训练的FastText获得了最佳结果(准确率=60.07,F1=0.45)。对于Lexical Sample,使用定制训练的GloVe词嵌入方法实现了WSD(准确率=70.93,F1=0.60)。
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引用次数: 0
Healthcare Data Collection Using Internet of Things and Blockchain Based Decentralized Data Storage 使用物联网和基于区块链的分散数据存储的医疗保健数据收集
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-10-06 DOI: 10.14201/adcaij.28612
M. Sumathi, S. P. Raja, N. Vijayaraj, M. Rajkamal
With the increase in usage of Internet of Things devices (IoT), IoT is used in different sectors such as manufacturing, electric vehicles, home automation and healthcare. The IoT devices collected large volumes of data on different parameters at regular intervals. Storing a massive amount volume of IoT data securely is a complicated task. Presently, the majority of IoT devices use cloud storage to store the data, however, cloud servers require large storage and high computation. Due to third party cloud service provider (CSP) interaction, the management of IoT data security fully depends on the CSP. To manage these problems, a decentralized blockchain based secure storage is proposed in this work. In the proposed scheme, instead of CSP storage location, the patient health information is stored in the blockchain technique and the blockchain miners verify the transactions with the help of Elliptic Curve Cryptography (ECC). The miner verification process dynamically avoids adversary access. Similarly, the certificateless access is used in the proposed system to avoid certificate based issues. The blocks in the blockchain is going to be stored patient details in a decentralized storage location to avoid unauthorized access and ensure the authenticity of data. The use of blockchain eliminates the need for third party public auditing process through immutable storage. This work illustrates secure communication and immutable data storage without the intervention of CSP. The communication overhead reduced by nearly 10 to 40% and authentication improved by 10 to 20% while confidentiality increased by 5% in comparison to existing techniques. Through this technique, data confidentiality, integrity and availability is ensured.
随着物联网设备(IoT)使用的增加,物联网被用于制造业、电动汽车、家庭自动化和医疗保健等不同领域。物联网设备定期收集大量不同参数的数据。安全存储大量物联网数据是一项复杂的任务。目前,大多数物联网设备使用云存储来存储数据,但云服务器需要大容量存储和高计算能力。由于第三方云服务提供商(CSP)的交互,物联网数据的安全管理完全依赖于CSP。为了解决这些问题,本文提出了一种基于分散区块链的安全存储方法。在该方案中,患者健康信息存储在区块链技术中,而不是CSP存储位置,区块链矿工借助椭圆曲线加密(ECC)验证交易。矿工验证过程动态避免对手访问。类似地,在建议的系统中使用无证书访问来避免基于证书的问题。区块链中的区块将把患者的详细信息存储在一个分散的存储位置,以避免未经授权的访问,确保数据的真实性。区块链的使用通过不可变存储消除了对第三方公共审计过程的需求。这项工作说明了在没有CSP干预的情况下安全通信和不可变数据存储。与现有技术相比,通信开销减少了近10%到40%,身份验证提高了10%到20%,机密性提高了5%。通过该技术,保证了数据的保密性、完整性和可用性。
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引用次数: 0
Development of an Extended Medical Diagnostic System for Typhoid and Malaria Fever 伤寒和疟疾扩展医疗诊断系统的开发
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-09-26 DOI: 10.30564/aia.v5i1.5505
Temitope Apanisile, Joshua Ayobami Ayeni
In developing countries like Nigeria, malaria and typhoid fever are major health challenges in society today. The symptoms vary and can lead to other illnesses in the body which include prolonged fever, fatigue, nausea, headaches, and the risk of contracting infection occurring concurrently if not properly diagnosed and treated. There is a strong need for cost-effective technologies to manage disease processes and reduce morbidity and mortality in developing countries. Some of the challenging issues confronting healthcare are lack of proper processing of data and delay in the dissemination of health information, which often causes delays in the provision of results and poor quality of service delivery. This paper addressed the weaknesses of the existing system through the development of an Artificial Intelligence (AI) driven extended diagnostic system (EDS). The dataset was obtained from patients’ historical records from the Lagos University Teaching Hospital (LUTH) and contained two-hundred and fifty (250) records with five (5) attributes such as risk level, gender, symptom 1, symptom 2, and ailment type. The malaria and typhoid dataset was pre-processed and cleansed to remove unwanted data and information. The EDS was developed using the Naive Bayes technique and implemented using software development tools. The performance of the system was evaluated using the following known metrics: accuracies of true positive (TP), true negative (TN), false positive (FP), and false negative (FN). The performance of the EDS was substantially significant for both malaria and typhoid fevers.
在尼日利亚等发展中国家,疟疾和伤寒是当今社会面临的主要健康挑战。症状各不相同,可导致身体出现其他疾病,包括长期发烧、疲劳、恶心、头痛,如果诊断和治疗不当,还可能同时发生感染。发展中国家迫切需要具有成本效益的技术来管理疾病进程并降低发病率和死亡率。卫生保健面临的一些具有挑战性的问题是缺乏适当的数据处理和传播卫生信息的延迟,这往往导致提供结果的延迟和提供服务的质量差。本文通过开发人工智能(AI)驱动的扩展诊断系统(EDS)来解决现有系统的弱点。该数据集来自拉各斯大学教学医院(LUTH)的患者历史记录,包含250条记录,有5个属性,如风险水平、性别、症状1、症状2和疾病类型。对疟疾和伤寒数据集进行了预处理和清理,以删除不需要的数据和信息。EDS使用朴素贝叶斯技术开发,并使用软件开发工具实现。使用以下已知指标评估系统的性能:真阳性(TP)、真阴性(TN)、假阳性(FP)和假阴性(FN)的准确性。EDS对疟疾和伤寒的疗效显著。
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引用次数: 0
Energy Efficient 4-2 and 5-2 Compressor for Low Power Computing 节能4-2和5-2压缩机低功耗计算
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-09-19 DOI: 10.14201/adcaij.30381
Rahul Mani Upadhyay, R.K. Chauhan, Manish Kumar
As the use of multimedia devices is rising, power management is becoming a major challenge. Various types of compressors have been designed in this study. Compressor circuits are designed using several circuits of XOR-XNOR gates and multiplexers. XOR-XNOR gate combinations and multiplexer circuits have been used to construct the suggested compressor design. The performance of the proposed compressor circuits using these low-power XOR-XNOR gates and multiplexer blocks has been found to be economical in terms of space and power. This study proposes low-power and high-speed 3-2, 4-2, and 5-2 compressors for digital signal processing applications. A new compressor has also been proposed that is faster and uses less energy than the traditional compressor. The full adder circuit, constructed using various combinations of XOR-XNOR gates, has been used to develop the proposed compressor. The proposed 3-2 compressor shows average power dissipation 571.7 nW and average delay 2.41 nS, 4-2 compressor shows average power dissipation 1235 nW and average delay 2.7 nS while 5-2 compressor shows average power dissipation 2973.50 nW and average delay 3.75 nS.
随着多媒体设备使用的增加,电源管理成为一个主要的挑战。本研究设计了各种类型的压缩机。压缩电路采用多个异或异或门电路和多路复用器进行设计。XOR-XNOR门组合和多路复用电路被用于构建建议的压缩机设计。使用这些低功耗XOR-XNOR门和多路复用器模块的拟议压缩电路的性能在空间和功率方面已被发现是经济的。本研究提出用于数字信号处理应用的低功耗和高速3-2、4-2和5-2压缩机。还提出了一种比传统压缩机速度更快、能耗更低的新型压缩机。完整的加法器电路,使用各种异或异或门组合构建,已用于开发所提出的压缩机。3-2压缩机平均功耗571.7 nW,平均时延2.41 nS; 4-2压缩机平均功耗1235 nW,平均时延2.7 nS; 5-2压缩机平均功耗2973.50 nW,平均时延3.75 nS。
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引用次数: 0
Comparison of Swarm-based Metaheuristic and Gradient Descent-based Algorithms in Artificial Neural Network Training 基于群的元启发式算法与基于梯度下降算法在人工神经网络训练中的比较
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-09-19 DOI: 10.14201/adcaij.29969
Erdal Eker, Murat Kayri, Serdar Ekinci, Davut İzci
This paper aims to compare the gradient descent-based algorithms under classical training model and swarm-based metaheuristic algorithms in feed forward backpropagation artificial neural network training. Batch weight and bias rule, Bayesian regularization, cyclical weight and bias rule and Levenberg-Marquardt algorithms are used as the classical gradient descent-based algorithms. In terms of the swarm-based metaheuristic algorithms, hunger games search, gray wolf optimizer, Archimedes optimization, and the Aquila optimizer are adopted. The Iris data set is used in this paper for the training. Mean square error, mean absolute error and determination coefficient are used as statistical measurement techniques to determine the effect of the network architecture and the adopted training algorithm. The metaheuristic algorithms are shown to have superior capability over the gradient descent-based algorithms in terms of artificial neural network training. In addition to their success in error rates, the classification capabilities of the metaheuristic algorithms are also observed to be in the range of 94%-97%. The hunger games search algorithm is also observed for its specific advantages amongst the metaheuristic algorithms as it maintains good performance in terms of classification ability and other statistical measurements.
本文旨在比较经典训练模型下基于梯度下降的算法和基于群的元启发式算法在前馈反向传播人工神经网络训练中的应用。基于梯度下降的经典算法采用了批加权和偏置规则、贝叶斯正则化、周期加权和偏置规则和Levenberg-Marquardt算法。在基于群体的元启发式算法中,采用了饥饿游戏搜索、灰狼优化器、阿基米德优化器和Aquila优化器。本文使用Iris数据集进行训练。采用均方误差、平均绝对误差和确定系数作为统计度量技术来确定网络结构和所采用的训练算法的效果。在人工神经网络训练方面,元启发式算法比基于梯度下降的算法具有更强的能力。除了在错误率上取得成功外,元启发式算法的分类能力也在94%-97%的范围内。饥饿游戏搜索算法在元启发式算法中也有其独特的优势,因为它在分类能力和其他统计测量方面保持了良好的性能。
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引用次数: 0
A Framework for Improving the Performance of QKDN using Machine Learning Approach 使用机器学习方法提高QKDN性能的框架
Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-09-19 DOI: 10.14201/adcaij.30240
R Arthi, A Saravanan, J S Nayana, Chandresh MuthuKumaran
A reliable secure communication can be given between two remote parties by key sharing, quantum key distribution (QKD) is widely concentrated as the information in QKD is safeguarded by the laws of quantum physics. There are many techniques that deal with quantum key distribution network (QKDN), however, only few of them use machine learning (ML) and soft computing techniques to improve QKDN. ML can analyze data and improve itself through model training without having to be programmed manually. There has been a lot of progress in both the hardware and software of ML technologies. Given ML’s advantageous features, it can help improve and resolve issues in QKDN, facilitating its commercialization. The proposed work provides a detailed understanding of role of each layer of QKDN, addressing the limitations of each layer, and suggesting a framework to improve the performance metrics for various applications of QKDN by applying machine learning techniques, such as support vector machine and decision tree algorithms.
量子密钥分发(quantum key distribution, QKD)是一种广泛集中的通信方式,由于量子密钥分发中的信息受到量子物理定律的保护。处理量子密钥分发网络(QKDN)的技术有很多,但利用机器学习(ML)和软计算技术来改进QKDN的技术很少。机器学习可以通过模型训练来分析数据并改进自身,而无需手动编程。机器学习技术在硬件和软件方面都取得了很大的进步。鉴于ML的优势特性,它可以帮助改进和解决QKDN中的问题,促进其商业化。提出的工作提供了对QKDN每层作用的详细理解,解决了每层的局限性,并提出了一个框架,通过应用机器学习技术,如支持向量机和决策树算法,来提高QKDN各种应用的性能指标。
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引用次数: 0
Restricted Computations and Parameters in Type-Theory of Acyclic Recursion 非循环递归类型论中的限制计算和参数
IF 1.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-07-20 DOI: 10.14201/adcaij.29081
Roussanka Loukanova
The paper extends the formal language and the reduction calculus of Moschovakis type-theory of recursion, by adding a restrictor operator on terms with predicative restrictions. Terms with restrictions over memory variables formalise inductive algorithms with generalised, restricted parameters. The extended type-theory of restricted recursion (TTRR) provides computations for algorithmic semantics of mathematical expressions and definite descriptors, in formal and natural languages.The reduction calculi of TTRR provides a mathematical foundation of the work of compilers for reducing recursive programs to iterative ones. The type-theory of acyclic recursion (TTAR) has a special importance to syntax-semantics interfaces in computational grammars.
本文通过在带有谓词限制的项上添加一个限制算子,扩展了Moschovakis递归类型论的形式语言和约简演算。对内存变量有限制的术语用一般化的、受限的参数形式化归纳算法。限制递归的扩展类型理论(TTRR)为数学表达式和确定描述符的算法语义提供了形式语言和自然语言的计算。TTRR的约简演算为编译器将递归程序约简为迭代程序提供了数学基础。在计算语法中,无循环递归的类型论对于语法-语义接口具有特殊的重要性。
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引用次数: 0
AI Assists Operation and Maintenance of Future Cities 人工智能助力未来城市运维
IF 1.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-06-19 DOI: 10.30564/aia.v5i1.5780
Hanxi Zhao
*CORRESPONDING AUTHOR: Han-Wei Zhao, State Key Laboratory of Safety, Durability and Healthy Operation of Long Span Bridges, Southeast University, Nanjing, Jiangsu, 211189, China; Key Laboratory of Concrete and Pre-stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing, Jiangsu, 210096, China; Teaching and Research Section of Intelligent Construction, Southeast University, Nanjing, Jiangsu, 211189, China; Email: civil_hwzhao@seu.edu.cn
*通讯作者:赵汉伟,大跨度桥梁安全耐久性与健康运行国家重点实验室,东南大学,江苏南京,211189;东南大学混凝土及预应力混凝土结构教育部重点实验室,江苏南京210096;东南大学智能建筑教研室,江苏南京211189;电子邮件:civil_hwzhao@seu.edu.cn
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引用次数: 0
A Novel Framework for Ancient Text Translation Using Artificial Intelligence 基于人工智能的古文翻译新框架
IF 1.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-06-05 DOI: 10.14201/adcaij.28380
Shikha Verma, Neha Gupta, Anil B C, Rosey Chauhan
Ancient script has been a repository of knowledge, culture and civilization history. In order to have a greater access to the valuable information present in the ancient scripts, an appropriate translation system needs to be developed keeping complexity and very less knowledge of the script available in consideration. In this study, a translation and prediction system has been implemented using Artificial Intelligence. The training has been developed using Sunda-Dataset and self-generated dataset, whereas the translation from ancient script viz. Sundanese script to English text is done using two layers Recurrent Neural Network. The technique used is compared with an existing translator called IM Translator. The results shows that the BLEU score  is increased by 8% in comparison to IM Translator further WER is decreased  by 10% in contrast to IM Translator.  Furthermore, the N-Gram analysis results indicate 3% to 4% increase in 100% contrast value. 
古代文字一直是知识、文化和文明史的宝库。为了更好地获取古代文字中存在的有价值的信息,需要开发一个适当的翻译系统,同时考虑到复杂性和对文字的了解非常少。本研究采用人工智能技术实现了一个翻译预测系统。使用sunda数据集和自生成数据集进行训练,而从古代文字(即Sundanese文字)到英语文本的翻译则使用两层递归神经网络完成。所使用的技术与现有的称为IM translator的翻译器进行了比较。结果表明,BLEU分数比IM Translator提高了8%,而WER分数比IM Translator降低了10%。此外,N-Gram分析结果表明100%对比度值增加3%至4%。
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引用次数: 0
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ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal
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