International Journal of Advanced Computer Science and Applications最新文献

{"title":"A Review on Machine-Learning and Nature-Inspired Algorithms for Genome Assembly","authors":"Asmae Yassine, M. E. Riffi","doi":"10.14569/ijacsa.2023.0140798","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140798","url":null,"abstract":"—Genome assembly plays a crucial role in the field of bioinformatics, as current sequencing technologies are unable to sequence an entire genome at once where the need for fragmenting into short sequences and reassembling them. The genomes often contain repetitive sequences and duplicated regions, which can lead to ambiguities during assembly. Thus, the process of reconstructing a complete genome from a set of reads necessitates the use of efficient assembly programs. Over time, as genome sequencing technology has advanced, the methods for genome assembly have also evolved, resulting in the utilization of various genome assemblers. Many artificial intelligence techniques such as machine learning and nature-inspired algorithms have been applied in genome assembly in recent years. These technologies have the potential to significantly enhance the accuracy of genome assembly, leading to functionally correct genome reconstructions. This review paper aims to provide an overview of the genome assembly, highlighting the significance of different methods used in machine learning techniques and nature-inspiring algorithms in achieving accurate and efficient genome assembly. By examining the advancements and possibilities brought about by different machine learning and metaheuristics approaches, this review paper offers insights into the future directions of genome assembly.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"14 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74954145","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}

{"title":"Patient Health Monitoring System Development using ESP8266 and Arduino with IoT Platform","authors":"J. Alsayaydeh, Mohd Faizal bin Yusof, Muhammad Zulhakim Bin Abdul Halim, M. N. S. Zainudin, S. Herawan","doi":"10.14569/ijacsa.2023.0140467","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140467","url":null,"abstract":"The Internet of Things (IoT) has emerged as a transformative technology that has revolutionized the field of healthcare. One of the most promising applications of Internet of Things (IoT) in healthcare is patient health monitoring, which allows healthcare providers to remotely monitor patients' health and provide prompt medical attention when needed. This research work focuses on developing an Internet of Things (IoT)based patient health monitoring system aimed at providing a solution for patients, particularly the elderly, who face the risk of unexpected death due to the lack of medical attention. The proposed system utilizes a heartbeat sensor and an Infrared IR temperature sensor connected to Arduino UNO and Nodemcu, respectively, to monitor the patient's vital signs. The sensors collect the data, which is then sent to an Internet of Things (IoT) web platform via a Wi-Fi connection. The Internet of Things (IoT) platform displays the real-time data of the patient's health status, including the temperature and heartbeat rate, which can be monitored by doctors and nurses. The system is designed to send alerts to healthcare providers in the event of any medical emergency, ensuring that prompt medical attention can be provided to the patient. The significance of this research work lies in its potential to revolutionize the healthcare industry by providing a more efficient and effective means of patient health monitoring. The system can be used to monitor a large number of patients simultaneously, which is particularly beneficial in hospitals with a large patient load. Moreover, it can reduce the workload of healthcare providers, allowing them to focus on other critical tasks. This innovative system has the potential to improve the overall quality of healthcare services and lead to better health outcomes for the society. Keywords—Patient health monitoring; Internet of Things (IoT); Arduino UNO; Nodemcu ESP8266; thingspeak; wearable device; temperature value; heartbeat value; remotely","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"100 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81401751","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}

{"title":"Exploring the Challenges and Impacts of Artificial Intelligence Implementation in Project Management: A Systematic Literature Review","authors":"Muhammad Irfan Hashfi, Teguh Raharjo","doi":"10.14569/ijacsa.2023.0140940","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140940","url":null,"abstract":"This paper presents a systematic literature review (SLR) investigating the challenges and impacts of implementing artificial intelligence (AI) in project management, specifically mapping them into the process groups defined in the Project Management Body of Knowledge (PMBOK). The study aims to contribute to the understanding of integrating AI in project management and provides insights into the challenges and impacts within each process group. The SLR methodology was applied, and a total of 34 scientific articles were analyzed. The results and analysis reveal the specific challenges and impacts within each process group. In the Initiating Process Group, AI tools and analysis techniques address challenges in risk assessment, cost prediction, and decision-making. The Planning process group benefits from various tools and methodologies that improve risk assessment, project selection, cost estimation, resource allocation, and decision-making. The Execution process group emphasizes the importance of advanced tools and techniques in enhancing productivity, resource utilization, cost reduction, and decision-making. The Monitoring and Controlling process group demonstrates the potential of advanced tools in achieving efficiency, cost reduction, improved quality, and informed decision-making. Lastly, the Closing process group emphasizes the importance of utilizing advanced tools to minimize waste, optimize resource utilization, reduce costs, improve quality, and project closure success. Overall, this research provides valuable insights and strategies for organizations seeking to implement AI in project management, thereby enhancing the potential for success within the PMBOK Process Group.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135956982","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}

{"title":"A Novel Deep Neural Network to Analyze and Monitoring the Physical Training Relation to Sports Activities","authors":"Bakhytzhan Omarov, Nurlan Nurmash, Bauyrzhan Doskarayev, Nagashbek Zhilisbaev, Maxat Dairabayev, Shamurat Orazov, Nurlan Omarov","doi":"10.14569/ijacsa.2023.0140977","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140977","url":null,"abstract":"In the research paper, authors meticulously detail the development, testing, and application of an innovative deep learning model aimed at monitoring physical activities of students in real-time. Drawing upon the advanced capabilities of convolutional neural networks (CNNs), the proposed system exhibits an exceptional ability to track, analyze, and evaluate the physical exercises performed by students, thereby providing an unprecedented scope for customization in physical education strategies. This piece of scholarly work bridges the gap between physical education and cutting-edge technology, highlighting the burgeoning role of artificial intelligence in health and fitness sector. With an expansive study spanning various cohorts of physical culture students, the paper provides compelling empirical evidence that underlines the superiority of the deep learning system over conventional methods in aspects of accuracy, speed, and efficiency of monitoring. The authors demonstrate the transformative potential of their system, capable of facilitating personalized and optimized physical training strategies based on real-time feedback. Moreover, the potential implications of the study extend beyond the realm of education and into wider public health applications, with the possibility of fostering improved health outcomes on a larger scale. This research paper makes a significant contribution to the burgeoning field of AI in physical education, embodying a paradigm shift in the approach towards physical fitness and health monitoring. It underscores the potential of AI-driven technology to revolutionize traditional methods in physical education, paving the way for more personalized and effective teaching and training regimes, and ultimately contributing to enhanced health and fitness outcomes among students.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136002810","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}

{"title":"A Survey of Evolving Performance Analysis Technologies, Algorithms and Models for Sports","authors":"Shamala Subramaniam, Manoj Ravi Shankar, Azyyati Adiah Zazali, Hong Siaw Swin, Zarina Muhamed, Sivakumar Rajagopal, Mohamad Zamri Napiah, Faisal Embung","doi":"10.14569/ijacsa.2023.0140916","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140916","url":null,"abstract":"org","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136004360","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}

{"title":"Suppressing Chest Radiograph Ribs for Improving Lung Nodule Visibility by using Circular Window Adaptive Median Outlier (CWAMO)","authors":"Dnyaneshwar Kanade, J. Helonde","doi":"10.14569/ijacsa.2023.0140359","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140359","url":null,"abstract":"— Chest radiograph ribs obstruct lung nodules. To see the nodule under the chest radiograph ribs, remove or suppress them. The paper describes a circular median filter approach for finding outliers in chest radiographs. The method uses 147 Japanese Society of Radiological Technology x-ray pictures (JSRT). Pixels with intensities two standard deviations above the median are median outliers. Contrast-Limited Adaptive Histogram Equalization enhances nodule visibility (CLAHE). The method is tested on modest chest radiographs and compared to the Budapest University Bone Shadow Eliminated X-Ray Dataset methodology. The initial test uses 50 modest chest radiographs (Test 1). The proposed approach is applied after active shape modelling (ASM) lung segmentation. True positive nodules are seen on 89% of chest radiographs of various subtleties. Test-2 and Test-3 used 20 subtlety-level photos. In Test-2, the peak signal-to-noise ratio (PSNR), mean-to-standard deviation ratio (MSR), and universal image quality index (IQI) are evaluated for the full image and compared to the existing algorithm. For all three parameters, the suggested technique outperforms the algorithm. Test-3 computes nodule MSR and compares it to Budapest University's Bone Shadow Eliminated Dataset and original chest radiographs. The new algorithm improved nodule area contrast by 3.83% and 23.94% compared to the original chest radiograph. This approach improves chest radiograph nodule visualization.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"27 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81278644","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}

{"title":"Knowledge Graph based Representation to Extract Value from Open Government Data","authors":"Kawtar Younsi Dahbi, D. Chiadmi, Hind Lamharhar","doi":"10.14569/ijacsa.2023.0140329","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140329","url":null,"abstract":"","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"30 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81339129","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}

{"title":"Opportunities and Challenges in Human-Swarm Interaction: Systematic Review and Research Implications","authors":"Alexandru-Ionuț Șiean, Bogdan Gradinaru, O. Gherman, M. Danubianu, L. Milici","doi":"10.14569/ijacsa.2023.0140499","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140499","url":null,"abstract":"We conducted a Systematic Literature Review on scientific papers that examined the interaction between operators and drone swarms based on the use of a command and control center. We present the results of a meta-analysis of nine scientific papers published in the ACM DL and IEEE Xplore databases. Our findings show that research on human-drone swarm interaction shows a disproportionate interest in hand gestures compared to other input modalities for drone swarm control. Furthermore, all articles reviewed exclusively explored gestures and the size of the swarm used in the studies was limited, with a median of 3.0 and an average of 3.8 drones per study. We compiled an inventory of interaction modalities, recognition techniques, and application types from the scientific literature, which is presented in this paper. On the basis of our findings, we propose four areas for future research that can guide scientific investigations and practical developments in this field. Keywords—Human swarm interactions; input modalities; swarm control","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"53 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88385874","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}

{"title":"A Study of Encryption for Multimedia Digital Audio Security","authors":"X. Zhou, C. Wei, Xiaotang Shao","doi":"10.14569/ijacsa.2023.0140269","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140269","url":null,"abstract":"—Driven by the development of multimedia, the encryption of multimedia digital audio has received more attention; however, cryptography-based encryption methods have many shortcomings in encryption of multimedia information, and new encryption methods are urgently needed. This paper briefly introduced cryptography and chaos theory, designed a chaos-based encryption algorithm that combined Logistic mapping and Sine mapping for confusion and used a Hopfield chaos neural network for diffusion, explained the encryption and decryption process of the algorithm, and tested the algorithm. It was found that the keys obtained by the proposed algorithm passed the SP800-22 test, and the correlation between the three encrypted audio and the original audio was 0.0261, -0.0536, and 0.0237, respectively, all of which were small, and the peak signal-to-noise ratio (PSNR) values were -0.348 dB, -7.645 dB, and -3.636 dB, respectively, which were significantly different from the original audio. The NSCR and UACI were also closer to the original values. The results prove that the proposed algorithm has good security and can encrypt the actual multimedia digital audio.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"8 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78716038","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}

{"title":"Motor Imagery EEG Signals Marginal Time Coherence Analysis for Brain-Computer Interface","authors":"Md. Sujan Ali, Jannatul Ferdous","doi":"10.14569/ijacsa.2023.0140888","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140888","url":null,"abstract":"—The synchronization of neural activity in the human brain has great significance for coordinating its various cognitive functions. It changes throughout time and in response to frequency. The activity is measured in terms of brain signals, like an electroencephalogram (EEG). The time-frequency (TF) synchronization among several EEG channels is measured in this research using an efficient approach. Most frequently, the windowed Fourier transforms-short-time Fourier transform (STFT), as well as wavelet transform (WT), and are used to measure the TF coherence. The information provided by these model-based methods in the TF domain is insufficient. The proposed synchro squeezing transform (SST)-based TF representation is a data-adaptive approach for resolving the problem of the traditional one. It enables more perfect estimation and better tracking of TF components. The SST generates a clearly defined TF depiction because of its data flexibility and frequency reassignment capabilities. Furthermore, a non-identical smoothing operator is used to smooth the TF coherence, which enhances the statistical consistency of neural synchronization. The experiment is run using both simulated and actual EEG data. The outcomes show that the suggested SST-dependent system performs significantly better than the previously mentioned traditional approaches. As a result, the coherences dependent on the suggested approach clearly distinguish between various forms of motor imagery movement. The TF coherence can be used to measure the interdependencies of neural activities.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"14 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89148871","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}