Volume-12 Issue-5, January 2024 – International Journal of Recent Technology and Engineering (IJRTE)

E799212050124

IJRTE Journal — Thu, 18 Jan 2024 09:28:22 +0000

2D Flow Model of Kendal River Sedimentation Effect as Identification of Bandengan Village Flood Management
A. H. B. Kuncoro¹, T. Zhafira², H. Masvika³

¹A. H. B. Kuncoro, Department of Civil Engineering, Universitas Semarang, Semarang, Indonesia.

²T. Zhafira, Department of Civil Engineering, Universitas Semarang, Semarang, Indonesia.

³H. Masvika, Department of Civil Engineering, Universitas Semarang, Semarang, Indonesia.

Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Flooding is one of the hydrometeorological disasters that often occurs on the north coast of Central Java, such as in Bandengan Village. Kendal River sedimentation occurs, which causes flooding. A two-dimensional (2D) flow model is needed due to the influence of sedimentation in the Kendal River channel, especially around the Bandengan Village area. This modelling aims to prove that sedimentation of the Kendal River is one of the causes of flooding. Secondary data on land use and rainfall are used to calculate the design of flood discharge. Primary data collection in the form of sediment samples and river contours as the basis for making Digital Elevation Model (DEM) maps for hydraulics modelling using the HEC-RAS 2D application. The Universal Soil Loss Equation (USLE) Method was analysed to determine the erosion potential formed in the Kendall Watershed. The calculation of the design flood discharge is Q2 of 45.1 m3 /s, Q5 of 62.8 m3 /s, Q10 of 74.7 m3 /s, and Q10 of 91.3 m3 /s. Hydraulic analysis with three scenarios revealed that existing conditions caused flooding, conditions without sediment also caused flooding, and finally, river widening conditions showed no flooding. Erosion analysis shows that the erosion hazard class in the Kendal watershed is low, so there are two indications, namely the transportation of sediment from irrigation canals in the upper reaches of the Kendal watershed and sedimentation accumulated over the years due to the absence of sediment control in the Kendal River. This modelling concludes that sedimentation, small river cross-sections, and the erosion of the Kendal coastal area are the causes of flooding in the area. Handling the issue by widening the river and building sediment barriers in the upstream area can reduce sedimentation and flooding of the Kendal River around Bandengan Village.

Keywords: 2D Model, DEM, Flood, River, Sedimentation
Scope of the Article: Computational Techniques in Civil Engineering

Download PDF

The post E799212050124 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

E799112050124

IJRTE Journal — Thu, 18 Jan 2024 09:22:24 +0000

Optimizing Trial Experiences in Cloud Platforms: Challenges, Strategies, and Impact on User Engagement and Conversion Rate
Prakash Somasundaram¹, K Aishwarya Pillai²

¹Prakash Somasundaram, Department of Computer Science, Northeastern University, San Francisco, California, United States of America (USA).

²K Aishwarya Pillai, Department of Computer Science, Northeastern University, San Francisco, California, United States of America (USA).

Abstract: This article delves into the significance of trial experiences in the context of cloud-based solutions, a crucial aspect of today’s digital business landscape. As cloud platforms reshape service delivery, the Freemium and Free Trial models emerge as compelling strategies for customer engagement. These models not only offer a glimpse into the service’s capabilities but also serve as a critical touchpoint for building trust and rapport with potential customers. Optimising trial experiences, however, presents a set of challenges, including balancing feature accessibility with the need to incentivise paid conversions and tailoring the trial to meet diverse user needs and expectations. This article provides an in-depth examination of optimising trial experiences within cloud platforms, including hurdles, strategic approaches, and their significant impact on user engagement and conversion rates. It highlights the delicate art of designing trial experiences that are sufficiently feature-rich to demonstrate value yet limited enough to encourage upgrade to paid versions. The article also discusses how personalization and customer feedback can be leveraged to enhance trial experiences. By analysing key industry players such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP), the article sheds light on how these frontrunners utilise trial experiences to captivate audiences and strengthen their customer base. It explores their distinct approaches in offering trials, the impact on market positioning, and how they balance the need for security and compliance with user accessibility. The insights from these industry giants provide valuable lessons for other players in the cloud computing sphere, who seek to leverage the power of trial experiences for customer acquisition and retention.

Keywords: Cloud-Based Solutions, Conversion Rates, Trial Experience Optimization, User Engagement.
Scope of the Article: Computer Science and Its Applications

Download PDF

The post E799112050124 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

E798312050124

IJRTE Journal — Thu, 18 Jan 2024 09:15:31 +0000

A Systematic Review of the Sarcasm Detection in the Twitter Dataset
K. Veena¹, V. Sasirekha²

¹K. Veena, Assistant Professor of Computer Science, J.K.K. Nataraja College of Arts & Science, Komarapalayam, Namakkal Dt.-638183, Tamil Nadu, India.

²Dr. V. Sasirekha, Assistant Professor of Computer Science, J.K.K. Nataraja College of Arts & Science, Komarapalayam, Namakkal Dt.- 638183, Tamil Nadu, India.

Abstract: Text is the most significant contributor to data generated on the Internet. Understanding a person’s opinion is a crucial aspect of natural language processing. However, people’s views can be skewed and inaccurate if they use sarcasm when posting status updates, commenting on blogs, and reviewing products and movies. Sarcasm detection has gained significant importance in social networking platforms because it can impact various applications, including sentiment analysis, opinion mining, and stance detection. Twitter is rapidly growing in volume, and its analysis presents significant challenges in detecting sarcasm. Our research focuses on various methodologies available for detecting sarcasm. Multiple papers from recent years were compiled, and a review was conducted. This paper discusses the literature on sarcasm detection, categorising datasets, preprocessing, feature extraction, feature selection, classification algorithms, and performance measures. This paper discusses the literature on sarcasm detection, categorising datasets, preprocessing, feature extraction, feature selection, classification algorithms, and performance measures. This work explores existing approaches, challenges, and future scopes for sarcasm detection in the Twitter dataset. This review highlights the analysis of sarcasm identification in Twitter data. It is intended to serve as a resource for researchers and practitioners interested in sarcasm detection and text classification.

Keywords: Sarcasm, Sentimental Analysis, Negative Connotation, Twitter, Machine Learning, Deep Learning
Scope of the Article: Computer Science and Its Applications

Download PDF

The post E798312050124 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

D79501112423

IJRTE Journal — Thu, 18 Jan 2024 09:06:28 +0000

Study of Seismic Effect on Different Types of Infill Walls
Parth Shah¹, Roshni John²

¹Parth Shah, Student, Saraswati College of Engineering, Mumbai University. Plot No.-46. Sector No.5. Kharghar. Navi Mumbai (Maharashtra), India.

²Prof. Roshni John, Professor. Saraswati College of Engineering, Mumbai University, Plot No.-46, Sector No.-5. Kharghar. Navi Mumbai (Maharashtra), India.

Abstract: The natural disaster that has occurred is an earthquake. Known to humanity for a very long time, researchers have been exploring various methods to protect buildings since they were first discovered. Infill walls are one of the most significant components of a building, as they serve to limit the damage caused by them to the structure, despite the various techniques available for achieving this. Infill walls are also efficient in providing stability to the building. Cladding can take the form of infill walls, which are constructed between the structural parts of a building. The structure offers support for the cladding system, while the cladding itself serves to partition the internal space from the outside world. Other types of cladding panels are attached to the exterior of the frame, but infill walls are installed in the spaces between the framing components. This makes it distinct from other types of cladding panels. Although they are required to withstand wind loads imparted to the face, the infill walls that are being considered for this project are not regarded as load-bearing infill walls. However, they are required to hold their weight. In this project, we will analyse the performance of a reinforced concrete building with a regular plan and different kinds of infill walls using the response spectrum method. The structure is modelled with E-TABS software.

Keywords: Infill Walls, Response Spectrum Analysis, E-Tabs
Scope of the Article: Structural Reliability Analysis

Download PDF

The post D79501112423 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

E79730112524

IJRTE Journal — Thu, 18 Jan 2024 08:58:17 +0000

Efficient Slice Creation in Network Slicing using K-Prototype Clustering and Context-Aware Slice Selection for Service Provisioning
A Priyanka¹, C Chandrasekar²

¹A Priyanka, Department of Computer Science, Periyar University, Salem, India.

²Dr. C Chandrasekar, Department of Computer Science, Periyar University, Salem, India.

Abstract: The advent of 5G technology has ushered in a new era of communication where the customization of network services is crucial to meet diverse user demands. Network slicing has emerged as a pivotal technology for achieving this customisation. In this research, we present an innovative approach to optimize network slicing in 5G by employing K-Prototype Clustering for slice creation and Context-Aware Slice Selection for efficient resource allocation. In slice creation, we delve into the innovative application of the K-Prototype clustering algorithm. Recognizing that 5G networks encompass numerical and categorical attributes, the K-Prototype algorithm enables the creation of network slices that cater to diverse service requirements. By harnessing this clustering technique, our proposed method optimises the creation of network slices, leading to improved resource utilisation and reduced network congestion. Furthermore, we introduce the concept of Context-Aware Slice Selection, which considers the dynamic and evolving nature of network demands. Contextawareness ensures that network slices are selected based on realtime contextual information, enabling a more adaptive and responsive network. This approach allows for the efficient allocation of resources and delivers a higher quality of service to end-users. To evaluate the performance of our proposed methodology, we employ key performance metrics, including slice selection accuracy, slice selection delay, and radio link failure. Through comprehensive testing and analysis, our research demonstrates that our approach consistently outperforms existing methods in terms of these metrics.

Keywords: Slice Creation, K-Prototype Clustering, Slice Selection, Context-Aware, Service Provisioning.
Scope of the Article: Computer Science and Its Applications

Download PDF

The post E79730112524 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

E79700112524

IJRTE Journal — Thu, 18 Jan 2024 07:56:07 +0000

Security-oriented Face Detection Technology Utilizing Deep Learning Techniques Along with the CASIA Datasets
Iqra Yamin¹, Yang Gaoming², Marcel BAKALA³, Muhammad Asad Yamin⁴, Usama Masood⁵

¹Iqra Yamin, Department of Computer Science and Engineering, Anhui University of Science and Technology, Huainan (Anhui), China.

²Yang Gaoming, Department of Computer Science and Engineering, Anhui University of Science and Technology, Huainan (Anhui), China.

³Marcel BAKALA, Department of Computer Science and Engineering, Anhui University of Science and Technology, Huainan (Anhui), China.

⁴Muhammad Asad Yamin, Department of Computational Science and Engineering, University of Rostock Germany.

⁵Usama Masood, Department of Mechanical Engineering, Anhui University of Science and Technology, Huainan (Anhui), China.

Abstract: Recently, face recognition technology has become increasingly important for safety purposes. Masks are now required in most countries and are increasingly used. Public health professionals advise people to cover their faces outdoors to reduce COVID-19 transmission by 65%. Detecting people without masks on their faces is crucial. This has become widely used as face recognition outperforms PINs, passwords, fingerprints, and other safety verification methods. Sunglasses, scarves, caps, and makeup have made facial identification harder in recent decades. Thus, such masks impact facial recognition performance. Face masks also render traditional facial recognition technology ineffective for face authorisation, security checks, school monitoring, and opening cellphones and laptops. Thus, we proposed Masked Facial Recognition (MFR) to recognise both veiled and exposed-face individuals, allowing them to avoid removing their masks to verify their identities. This deep learning model was trained using the Inception ResNet V1. CASIA is responsible for preparing pictures and using LFW to validate models. Dlib creates masked datasets utilizing vision algorithms. An accuracy of approximately 96% was achieved using our three trained models. Thus, covered and uncovered recognition of faces, as well as detection techniques, can be easily applied in security and safety verification. These systems can be utilised in various settings, including airports, train stations, and other public areas, to enhance security and deter crime. Overall, deep learning within face recognition technology has significant potential for improving safety and security in various settings.

Keywords: CASIA Dataset, Dlib, face recognition, Masked Facial Recognition.
Scope of the Article: Computer Science and Its Applications

Download PDF

The post E79700112524 appeared first on International Journal of Recent Technology and Engineering (IJRTE).