International Journal on Recent and Innovation Trends in Computing and Communication
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Determine the Classification of COVID-19 by Combining the Encoding of Amino Acids with Machine-Learning Models
Full text linkIn the ongoing battle against COVID-19, a novel approach integrating the encoding of amino acids with advanced machine-learning models offers a promising avenue for enhancing the classification accuracy of the virus strains. The relentless evolution of the virus necessitates robust and adaptable diagnostic tools capable of capturing the genetic intricacies that underpin the disease's transmission and virulence. This study addresses the critical need for refined classification techniques, pinpointing a significant gap in existing methodologies that often overlook the potential of amino acid sequences as predictive biomarkers. Employing a sophisticated feature selection mechanism, this research harnesses the power of Information Gain (IG) and Analysis of Variance (ANOVA) to distill essential features from the amino acid sequences. This process not only illuminates the sequences' predictive capacity but also reduces computational complexity, paving the way for more efficient model training and validation. The dataset, derived from the National Genomics Data Center (NGDC), encompasses a comprehensive array of amino acid sequences associated with various COVID-19 strains, providing a fertile ground for model evaluation through 10-fold cross-validation. The study meticulously evaluates the performance of two machine-learning classifiers: Decision Trees (DT) and Random Forest (RF). Utilizing IG, the RF classifier demonstrated exceptional proficiency, achieving an accuracy of 98.69%, with similarly high metrics across sensitivity, specificity, and precision. This starkly contrasts with the DT classifier, which, while respectable, lagged behind with an overall accuracy of 89.23%. A parallel examination using ANOVA echoed these findings, with RF maintaining superior performance, albeit with a narrower margin of distinction between the two classifiers. This comparative analysis underscores the RF classifier's robustness, attributable to its ensemble nature, which aggregates insights from multiple decision trees to mitigate overfitting and enhance predictive accuracy. The integration of amino acid encoding with RF, informed by targeted feature selection through IG and ANOVA, presents a potent methodology for COVID-19 strain classification
Fast Flux Domain Detection Using DNS Traffic
Full text linkThere are many attacks possible that affect the services of DNS server, one such type of attack is Distributed Denial of Service (DDoS). So to avoid such attacks, DNS servers use various types of techniques like load balancing, Round Robin DNS, Content Distribution Networks, etc. But cybercriminals use these techniques to hide their actual and network location from the outside world. One such type of technique is Fast-Flux Service Networks, which is like proxies to the cybercriminals that makes them untraceable. FFSN is a major threat to internet security and used in many illegal scams like phishing websites, malware delivery, illegal adult content, and etc. Fast flux service networks have some limitation as attackers do not have control over the compromised PC’s physically.
For the detection of FFSN, broadly two approaches have been proposed, namely, (i) Using passive network traffic, and (ii) Using active network traffic. The problem of detection with active network traffic is that they predict CDN domain as FFSN domain because initially, FFSN looks like CDN. Further, there are many machine learning algorithms have been used to detect FFSN. In this research, we emphasize on two problems, namely, (i) Features used for detecting the FFSN which helps us to distinguish FFSN from the other network efficiently, and (ii) Find the best classifier for detection of FFSN.
This work shows how relevant features extracted from the network traffic help us to distinguish FFSN from benign domains. Further, we try to propose the best threshold values for each feature that efficiently detect FFSN while distinguishing it from other benign domains. In this work, we have used five different machine learning algorithms, namely, Decision Tree, Random Forest, SVM, KNN, and Boosted Tree. Then, we compare the performance of these five machine learning algorithms to find out which is the best one to detect fast flux domain from passive DNS network traffic
Machine Learning Applications for Predictive Maintenance in Mechanical Systems: Case Studies, Algorithms, and Performance Evaluation
Full text linkPredictive maintenance is a critical aspect of ensuring the reliability and efficiency of mechanical systems in various industries. Machine learning (ML) techniques have emerged as powerful tools for predictive maintenance, enabling early detection of equipment failures and facilitating timely interventions to prevent costly downtime and repairs. This paper provides an overview of machine learning applications for predictive maintenance in mechanical systems, presenting case studies, algorithms, and performance evaluation metrics. We discuss the significance of predictive maintenance in enhancing operational efficiency, reducing maintenance costs, and minimizing unplanned downtime. Furthermore, we review various machine learning algorithms commonly employed for predictive maintenance, including supervised and unsupervised learning techniques, deep learning models, and ensemble methods. Additionally, we delve into real-world case studies that highlight the successful implementation of machine learning for predictive maintenance across different industries, such as manufacturing, automotive, aerospace, and energy. Finally, we discuss performance evaluation metrics and methodologies used to assess the effectiveness and reliability of predictive maintenance models, considering factors such as accuracy, precision, recall, and F1-score. Through this comprehensive exploration, this paper aims to provide insights into the practical application of machine learning for predictive maintenance and its potential impact on optimizing the performance and longevity of mechanical systems
Develop an Alternative Novel Service tool for Cloud Computing
Full text linkNowadays, as cloud computing technology is so widely used and is developing so quickly, many companies utilize cloud services to build their business systems or applications. The cloud computing environment is made up of a number of involved entities with varying goals and expectations, such as end users, cloud consumers, cloud service brokers, and cloud service providers. Choosing a reliable cloud service provider is a difficult issue. Furthermore, while evaluating cloud service Quality of service (QoS), decision makers are more based on linguistic descriptions. This paper presents Develop an Alternative Novel Service Tool for Cloud Computing. Providing security both at the Cloud service provider (CSP) and domain levels, the Security Framework and Cloud Security Protocol have been recommended. Through essentially verifying the information and its owner on the server, the Data storage protocol (DSP) was developed to provide increased security to data stored in a cloud environment. This analysis additionally provides useful suggestions for enhancing cloud security control. The results of the experimental investigations demonstrate that described system operates with good high accuracy, efficiency, security, and short execution time
Mobile Application for Rainfall-Landslide Early Warning System (RLEWS) using Global Precipitation Measurement (GPM)
Full text linkMalaysia has tropical climates near the equatorial zone that deliver widespread rainfall each year. Due to this, Malaysia is susceptible to landslide incidents as one of the main factors that can induce a landslide is rainfall. Landslides have a significant impact on many environmental and socioeconomic issues, including the loss of life, damage to property and infrastructure, and psychological stress among victims. According to a study, geological circumstances are primarily responsible for slope collapse in a number of these countries. A landslide-specific early warning system must be established despite the increased sensitivity to landslides to lower the danger of landslide hazards. The objectives of this project are to develop a mobile app for a landslide early warning system to be used as a monitoring tool of landslides using estimates from the Global Precipitation Measurement (GPM) precipitation data to alert if any warning signs from potential landslides occurrences were seen in Ulu Klang. The methodology of this project is using MySQL as a database server, and PHP as a programming language with the input/data from GPM rainfall data. The result is all users are notified of the condition of the landscape and the landslide occurrence in Ulu Klang
Image Processing in Augmented Reality (AR) and Virtual Reality (VR)
Full text linkThis study investigates sophisticated image processing algorithms required for augmented reality (AR) and virtual reality (VR) settings. Image registration, feature identification, object recognition, depth estimation, 3D reconstruction, and real-time rendering are assessed for accuracy, computational complexity, real-time performance, robustness, scalability, and memory utilization. Real-time rendering is shown to be ideal, with good accuracy (95%) and real-time speed (60 fps). GPU advances and algorithmic optimizations reduce computing needs. Future directions include AI integration, benchmarking, uniform frameworks, and domain-specific apps
A Closer Look at Hybrid Agile Implementation in China
Full text linkA software project must be developed based on specific software development models or frameworks such as the Waterfall model, agile methodologies or any suitable development approaches as needed by the software development team. The need to refer to a model or framework is to ensure that the project is systematically managed, the progress is monitored, and a project meets the requirements with a good quality project that is delivered to the end-users on time. Hybrid agile is a software development lifecycle model currently widely used in a software project. Waterfall and Scrum are two examples of a well-known hybrid agile model. Some software projects even use three combinations of a model that derives a hybrid agile model called Water-Scrum-Fall. Despite many successful stories on the hybrid agile implementation and a list of benefits of the project implementing hybrid agile being reported, it is unfair to make a generic assumption that hybrid agile is acceptable by the majority of software projects. This research explores another perspective of hybrid agile implementation in one East Asian country, China. This research adopted semi-structured interviews to explore the perspective of China’s software engineering team on the hybrid agile implementation, and thematic analyses is used as the analysis approach. The finding shows that the software engineering team is inclined towards hybrid agile but acknowledges the advantages of hybrid agile
Integrating DSS in Student Courses Management System
Full text linkDecision support system is the key of every institution for a successful decision making. This study aims to integrate a decision support system in the Computer Arts and Technological College, Inc. to avoid the waste of valuable data sources due to incorrect representations of data. With the help of data warehouse the administrator have easily drawn a conclusion based on the given data sources. To come up with the project the integration of Pentaho Data Integration tools have used in order to extract, transform and load a data into the CATC-DW.
This project was also concern to lessen the job of the staff in the office of the institutions because through the used of the data warehouse they do not need any more to collect and merge manually the different data. This paper also concern to lessen the cost of building a data warehouse because the researcher prepared an open source tools for data integration. And lastly the paper focused on the importance of integrating DSS in the institutions. It explained the type of data sources and the previous way of decision support used by the institutions with and without DSS