ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal最新文献

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

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-11-01 DOI: 10.14201/adcaij.30632

Ashok Kumar Rai, Lalit Kumar Tyagi, Anoop Kumar, Swapnita Srivastava, Naushen Fatima

{"title":"Enhancing Energy Efficiency in Cluster Based WSN using Grey Wolf Optimization","authors":"Ashok Kumar Rai, Lalit Kumar Tyagi, Anoop Kumar, Swapnita Srivastava, Naushen Fatima","doi":"10.14201/adcaij.30632","DOIUrl":"https://doi.org/10.14201/adcaij.30632","url":null,"abstract":"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.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135714760","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

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

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-11-01 DOI: 10.14201/adcaij.31084

Muhammad Farhat Ullah, Ali Saeed, Naveed Hussain

{"title":"Comparison of Pre-trained vs Custom-trained Word Embedding Models for Word Sense Disambiguation","authors":"Muhammad Farhat Ullah, Ali Saeed, Naveed Hussain","doi":"10.14201/adcaij.31084","DOIUrl":"https://doi.org/10.14201/adcaij.31084","url":null,"abstract":"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.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135715035","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

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

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-10-06 DOI: 10.14201/adcaij.28612

M. Sumathi, S. P. Raja, N. Vijayaraj, M. Rajkamal

{"title":"Healthcare Data Collection Using Internet of Things and Blockchain Based Decentralized Data Storage","authors":"M. Sumathi, S. P. Raja, N. Vijayaraj, M. Rajkamal","doi":"10.14201/adcaij.28612","DOIUrl":"https://doi.org/10.14201/adcaij.28612","url":null,"abstract":"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.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135351973","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

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

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-09-26 DOI: 10.30564/aia.v5i1.5505

Temitope Apanisile, Joshua Ayobami Ayeni

{"title":"Development of an Extended Medical Diagnostic System for Typhoid and Malaria Fever","authors":"Temitope Apanisile, Joshua Ayobami Ayeni","doi":"10.30564/aia.v5i1.5505","DOIUrl":"https://doi.org/10.30564/aia.v5i1.5505","url":null,"abstract":"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.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135721046","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

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

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-09-19 DOI: 10.14201/adcaij.30381

Rahul Mani Upadhyay, R.K. Chauhan, Manish Kumar

{"title":"Energy Efficient 4-2 and 5-2 Compressor for Low Power Computing","authors":"Rahul Mani Upadhyay, R.K. Chauhan, Manish Kumar","doi":"10.14201/adcaij.30381","DOIUrl":"https://doi.org/10.14201/adcaij.30381","url":null,"abstract":"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.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135064059","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

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

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-09-19 DOI: 10.14201/adcaij.29969

Erdal Eker, Murat Kayri, Serdar Ekinci, Davut İzci

{"title":"Comparison of Swarm-based Metaheuristic and Gradient Descent-based Algorithms in Artificial Neural Network Training","authors":"Erdal Eker, Murat Kayri, Serdar Ekinci, Davut İzci","doi":"10.14201/adcaij.29969","DOIUrl":"https://doi.org/10.14201/adcaij.29969","url":null,"abstract":"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.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135014293","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 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

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-09-19 DOI: 10.14201/adcaij.30240

R Arthi, A Saravanan, J S Nayana, Chandresh MuthuKumaran

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

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-07-20 DOI: 10.14201/adcaij.29081

Roussanka Loukanova

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

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-06-19 DOI: 10.30564/aia.v5i1.5780

Hanxi Zhao

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

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-06-05 DOI: 10.14201/adcaij.28380

Shikha Verma, Neha Gupta, Anil B C, Rosey Chauhan

引用次数: 0