Journal of Computer Science最新文献

{"title":"Discrete Shearlet Transform and Lempel-Ziv Welch Coding for Lossless Fingerprint Image Compression","authors":"N. A. Kadim, S. Guirguis, H. Elsayed","doi":"10.3844/jcssp.2024.564.573","DOIUrl":"https://doi.org/10.3844/jcssp.2024.564.573","url":null,"abstract":": Image compression is a crucial task in image processing and in the process of sending and receiving files. There is a need for effective techniques for image compression as the raw images require large amounts of disk space to defect during transportation and storage operations. The most important objective of image compression is to decrease the redundancy of the image which helps in increasing the storage capacity and then efficient transmission. This study introduces a system for lossless image compression that is built to work on fingerprint image compression. It uses lossless compression to take care of the first image during processing. However, there is a serious problem which is the low ratio of compression. In order to make the ratio higher, there are five lossless compression techniques used in this study which are Elias Gamma Coding (EGC), Huffman Coding (HC), Arithmetic Coding (AC), Run-Length Encoding (RLE) and Lempel Ziv Welch (LZW). With these techniques, there are three types of transforms are used; they are Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Discrete Shearlet Transform (DST). The results conclude that discrete shearlet transform with the Lempel-Ziv Welch coding technique outperforms the other lossless compression techniques and its Compression Ratio (CR) is 3.678023.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141055551","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":"COVID-19 in the Era of Artificial Intelligence: Existing Technologies and A Strategic Model for Mitigating Future Pandemics","authors":"Bandar M. Alshammari","doi":"10.3844/jcssp.2024.465.486","DOIUrl":"https://doi.org/10.3844/jcssp.2024.465.486","url":null,"abstract":": Pandemics have existed since the existence of life and will continue as life continues. Throughout many of the previous pandemics, what played a major role in decreasing their severity is how we mitigated and controlled them. The main reason for this is the time it takes for treatments and vaccinations to be developed, which usually takes a long time. Therefore, the techniques used to control a pandemic rapidly change over the course of the pandemic until a cure or vaccine comes to light. At present, advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), fifth generation networks, and big data can without a doubt play major roles in controlling upcoming pandemics including COVID-19. This paper provides a comprehensive survey of current technologies that use AI and big data analytics to take part in the fight against the current pandemic (COVID-19), including their objectives, strengths, weaknesses, and challenges. This paper also studies existing telemedicine technologies and contact tracing tools used in various countries, which governments have adapted to fight against the current COVID-19 pandemic. This work concludes by suggesting a novel strategic model for controlling and mitigating pandemic crises (e.g., COVID-19). This model represents a guided solution for identifying pandemics and for controlling them using advanced digital solutions from the early stages.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141057235","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":"CMMI V2.0 Maturity Level 2 and Scrum Applicability in Jordanian Agile Companies Based on Expert Review","authors":"Moath Husni","doi":"10.3844/jcssp.2024.400.407","DOIUrl":"https://doi.org/10.3844/jcssp.2024.400.407","url":null,"abstract":"","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140354911","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":"Advances in Forest Fire Detection, Prediction and Behavior: A Comprehensive Survey","authors":"Ahmad A. A. Alkhatib, Khalid Jaber","doi":"10.3844/jcssp.2024.408.418","DOIUrl":"https://doi.org/10.3844/jcssp.2024.408.418","url":null,"abstract":"","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140356997","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":"Measuring Customers’ Satisfaction Using Sentiment Analysis: Model and Tool","authors":"Ahmed Alqurafi, Tawfeeq Alsanoosy","doi":"10.3844/jcssp.2024.419.430","DOIUrl":"https://doi.org/10.3844/jcssp.2024.419.430","url":null,"abstract":"","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140357519","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 Robust Medical Image Fusion Based on Synthetic Focusing Degree Criterion and Special Kernel Set for Clinical Diagnosis","authors":"D. C. Lepcha, Bhawna Goyal, Ayush Dogra, Ahmed Alkhayyat, Sanjeev Kumar Shah, Vinay Kukreja","doi":"10.3844/jcssp.2024.389.399","DOIUrl":"https://doi.org/10.3844/jcssp.2024.389.399","url":null,"abstract":"","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353310","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":"Classification of Thoracic X-Ray Images of COVID-19 Patients Using the Convolutional Neutral Network (CNN) Method","authors":"Ramacos Fardela, Dian Milvita, Mawanda Almuhayar, Dedi Mardiansyah, Latifah Aulia Rasyada, L. Hakim","doi":"10.3844/jcssp.2024.357.364","DOIUrl":"https://doi.org/10.3844/jcssp.2024.357.364","url":null,"abstract":": Recently, radiology modalities have been widely used to detect COVID-19. Thoracic X-rays and CT scans are the primary radiological tools utilized in the diagnosis and treatment of individuals with COVID-19. In addition, chest CT scans are more accurate and sensitive in early COVID-19 identification. A new problem arises in diagnosing the results of CT scan images of COVID-19 by radiologists or radiology specialists where COVID-19 is difficult to distinguish from pneumonia caused by other viruses and bacteria, so misdiagnosis can occur. Many researchers worldwide have developed computer-aided detection or diagnosis schemes based on medical image processing and machine learning to overcome this challenge. This research focuses on the development of previous studies, where the use of the Convolutional Neural Network (CNN) method to classify Thoracic X-ray Images of COVID-19 Patients is compared with the model developed by Roboflow. Image manipulation techniques applied to this study are pseudo color and the program is Python. This study employs the pseudo color image manipulation technique of the program in Python. This study uses data on patients with confirmed COVID-19 at Andalas University Hospital in 2022. Based on the study's results, a very good CNN Specificity score of 93% was obtained and the perfect Sensitivity score value was produced by the detection method using the Roboflow model, which was 100%. However, the Kappa score for both methods is below the expected threshold of 36-38%. Based on the ROC value, the CNN and Roboflow methods are good for calculating chest X-ray images of COVID-19 and normal patients.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140355375","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":"Human Activity Prediction Studies Using Wearable Sensors and Machine Learning","authors":"Divya Sharma, U. Chauhan","doi":"10.3844/jcssp.2024.431.441","DOIUrl":"https://doi.org/10.3844/jcssp.2024.431.441","url":null,"abstract":"","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353802","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":"Development of Big Data Classifier for Biomedicine Early Diagnosis: An Experimental Approach Using Machine Learning Methods","authors":"Ma Beth Solas Concepcion, Bobby D. Gerardo, Frank Elijorde, Joel Traifalgar De Castro, Nerilou Bermudez Dela Cruz","doi":"10.3844/jcssp.2024.379.388","DOIUrl":"https://doi.org/10.3844/jcssp.2024.379.388","url":null,"abstract":": In the fast-phase world, data availability is abundant due to a rapid adaptation increase of big data technologies. Large amounts of data have been generated and collected at an unprecedented speed and scale, introducing a revolution in medical research practices for biomedicine informatics. Thus, there is an immense demand for statistically rigorous approaches, especially in the medical diagnosis discipline. Therefore, this study utilized the Bayesian Belief Network (BBN) for feature selection, which identifies relevant features from a larger set of attributes and employs a stratification for the Stochastic Gradient Descent (SGD) classifier in the classifying of breast cancer on the publicly available machine learning repository at the University of California, Irvine (UCI) such, breast cancer Wisconsin and Coimbra breast cancer datasets. The experimental approach of using BBN as feature selection achieved 0.95% coincidence. Thus, a stratified Stochastic Gradient Descent (SGD) was employed to build a classification model to validate the coincidence. Our proposed modeling classifier approach reached novelty 98%, which improved by 7% compared to the previous works. Furthermore, this study presents a web-based application, a prototype type, to employ the proposed classifier model for breast cancer diagnosis. This study expects to provide a source of confidence and satisfaction for medical physicians to use decision-support tools.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140353893","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":"Generating IoT Specific Anomaly Datasets Using Cooja Simulator (Contiki-OS) and Performance Evaluation of Deep Learning Model Coupled with Aquila Optimizer","authors":"Vandana Choudhary, Sarvesh Tanwar, Tanupriya Choudhury","doi":"10.3844/jcssp.2020.365.378","DOIUrl":"https://doi.org/10.3844/jcssp.2020.365.378","url":null,"abstract":": In recent times, the massive expansion of the Internet of Things (IoT) has transformed various facets of everyday life and industries. The compelling cause behind the widespread adoption of IoT is the increasing availability of affordable, compact, and energy-efficient computing devices. While these devices offer significant benefits, they also raise substantial security and privacy challenges. Consequently, safeguarding IoT networks and devices is imperative. To raise a robust security system for IoT networks, it is crucial to have an efficient anomaly-based intrusion detection system. In this study, we introduce a meticulous methodology to create IoT-specific datasets. Utilizing the Contiki-OS Cooja simulator, we generate datasets representative of real-world IoT security threats, including sinkholes, version numbers, and flooding attacks. We then evaluate the performance of a Convolutional Neural Network paired with an Aquila Optimizer (CNN-AO) using these self-generated datasets, by employing metrics such as accuracy, precision, recall, F1-score, sensitivity, specificity, and false alarm rate. Additionally, we compare the effectiveness of CNN and LSTM models in distinguishing between benign and malicious traffic. Our findings demonstrate that the CNN-AO model surpasses other models in accurately classifying normal and malicious traffic with an accuracy of 99.22, 99.77, and 99.55% for our self-generated malicious datasets based on sinkhole attack, version number attack, and flooding attack respectively. This novel approach not only establishes a solid foundation for future investigations in this domain but also provides valuable insights into enhancing IoT system security. In this study, we contribute to the field by introducing a robust methodology for IoT-specific dataset generation and evaluating a cutting-edge CNN-AO model for intrusion detection. Furthermore, it is crucial to note that this research was conducted with utmost ethical consideration. Ethical guidelines and data privacy concerns were meticulously addressed during the generation of IoT datasets and the simulation of real-world attack scenarios, ensuring the responsible conduct of our study.","PeriodicalId":40005,"journal":{"name":"Journal of Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140354343","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}