Volume-13 Issue-4, November 2024 – International Journal of Recent Technology and Engineering (IJRTE) https://www.ijrte.org Exploring Innovation | ISSN:2277-3878(Online) | A Periodical Journal | Reg. No.: C/819981 | Published By BEIESP Tue, 24 Jun 2025 06:30:34 +0000 en-US hourly 1 https://www.ijrte.org/wp-content/uploads/2021/02/cropped-IJRTE-Logo-WS-32x32.png Volume-13 Issue-4, November 2024 – International Journal of Recent Technology and Engineering (IJRTE) https://www.ijrte.org 32 32 D817013041124 https://www.ijrte.org/portfolio-item/d817013041124/ Mon, 25 Nov 2024 04:07:41 +0000 https://www.ijrte.org/?post_type=portfolio&p=49522 The post D817013041124 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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A Comprehensive Approach to Predict Chronic Impairment of the Pulmonary System Through the Application of Artificial Neural Network Algorithm
Adisree. R1, Mohamed Javed Khan A.2

1Adisree. R., Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.

2Mohamed Javed Khan A., Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.  

Manuscript received on 12 October 2024 | Revised Manuscript received on 23 October 2024 | Manuscript Accepted on 15 November 2024 | Manuscript published on 30 November 2024 | PP: 24-27 | Volume-13 Issue-4, November 2024 | Retrieval Number: 100.1/ijrte.D817013041124 | DOI: 10.35940/ijrte.D8170.13041124

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© 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: COPD is a respiratory condition with airflow restriction and increased inflammation in the air passages. It is the main reason for sickness and death around the world, which requires sophisticated diagnostic instruments. This research examines the application of Artificial Neural Networks (ANNs) in predicting Chronic Obstructive Pulmonary Disease (COPD). The clinical dataset has been trained and validated; the ANN achieved an accuracy of over 93.75%. Our findings demonstrate that the ANN model is effective in facilitating early detection of COPD, which could improve clinical decision-making and patient outcomes.

Keywords: Chronic Obstructive Pulmonary Disease, Artificial Neural Networks, Forced Expiratory Volume, Forced Vital Capacity.
Scope of the Article: Artificial Intelligence & Methods

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]]> D816713041124 https://www.ijrte.org/portfolio-item/d816713041124/ Mon, 25 Nov 2024 04:01:54 +0000 https://www.ijrte.org/?post_type=portfolio&p=49519 The post D816713041124 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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The Influences of Macroscopic Modeling of Traffic Dynamics in Urban Road Segments Considering Side Friction Influences: A Case Study
Edwin Mararo Lekariap1, Zachary Gariy2, Timothy Nyomboi3

1Edwin Mararo Lekariap, Department of Civil, Construction and Environmental Engineering, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya.

2Zachary C. Abiero Gariy, Department of Civil, Construction and Environmental Engineering, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya.

3Timothy Nyomboi, Department of Civil, Construction and Environmental Engineering, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya.  

Manuscript received on 10 October 2024 | Revised Manuscript received on 25 October 2024 | Manuscript Accepted on 15 November 2024 | Manuscript published on 30 November 2024 | PP: 28-36 | Volume-13 Issue-4, November 2024 | Retrieval Number: 100.1/ijrte.D816713041124 | DOI: 10.35940/ijrte.D8167.13041124

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© 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: Urban road networks are essential arteries of modern cities, facilitating the movement of people and goods. However, efficient traffic flow management in urban areas remains a critical challenge, often exacerbated by factors such as side friction. This research article presents a comprehensive macroscopic modelling approach to understanding and incorporating the effects of side friction on traffic flow in the context of Nakuru City, Kenya. Nakuru City, like many growing urban centres, grapples with traffic congestion and associated issues, demanding innovative solutions for sustainable urban mobility. Side friction, a multifaceted factor influenced by various urban elements such as parked vehicles, pedestrian activities, and roadside infrastructure, has a significant impact on traffic flow dynamics. This study employed a macroscopic modeling framework to capture the complex interplay between traffic flow and side friction in Nakuru City’s road links. Through extensive data collection and analysis, combined with advanced traffic modelling techniques, the research developed a nuanced understanding of how side friction impacts traffic flow patterns.

Keywords: Macroscopic Modeling, Nakuru City, Side Friction, Traffic Management, Urban Road Links, Urban Traffic Flow.
Scope of the Article: Civil Engineering and Applications

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]]> D816113041124 https://www.ijrte.org/portfolio-item/d816113041124/ Mon, 25 Nov 2024 03:54:24 +0000 https://www.ijrte.org/?post_type=portfolio&p=49517 The post D816113041124 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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A Comprehensive Approach to Improving Enterprise Management Processes Through Applying Deep Learning Algorithms: A Case Study
Mikaël Ange Mousse

Dr. Mikaël A. Mousse, Institut Universitaire de Technologies, Uniersité de Parakou, Parakou, Benin.

Manuscript received on 26 September 2024 | Revised Manuscript received on 15 October 2024 | Manuscript Accepted on 15 November 2024 | Manuscript published on 30 November 2024 | PP: 19-23 | Volume-13 Issue-4, November 2024 | Retrieval Number: 100.1/ijrte.D816113041124 | DOI: 10.35940/ijrte.D8161.13041124

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© 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: This paper addresses the critical need to enhance the management of pension payments at Benin’s National Social Security Fund (NSSF), highlighting the pivotal role of deep learning methodologies in achieving optimization. As a key institution responsible for safeguarding the financial well-being of the population through social security programs, the NSSF faces unique challenges in managing the large volume of regular pension payments. This study presents an innovative deep learning-based approach specifically designed to address the complexities of pension payment management at the NSSF in Benin. By leveraging advanced neural network architectures, our methodology aims to streamline and optimise the processes involved in pension disbursements, with a focus on enhancing accuracy, efficiency, and overall operational effectiveness. Key components of this approach include analysing historical pension payment data, demographic trends, and relevant socioeconomic indicators to develop predictive models that inform policy decisions. These models help forecast and adapt to changing patterns, ensuring timely and accurate pension payments. Through a detailed case study on the NSSF of Benin, we demonstrate the practical application of deep learning in addressing the specific challenges faced by public entities managing pension payments.

Keywords: Deep Learning, Pension Payment Management, National Social Security Fund, Optimization.
Scope of the Article: Artificial Intelligence & Methods

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]]> D815413041124 https://www.ijrte.org/portfolio-item/d815413041124/ Mon, 25 Nov 2024 03:46:12 +0000 https://www.ijrte.org/?post_type=portfolio&p=49513 The post D815413041124 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Enhancing the Assessment and Optimization of Critical Elements through Fuzzy Aggregation: A Methodological Framework for Evaluating E-Services
Makund Arora

Makund Arora, Department of Electrical Engineering, Dayalbagh Educational Institute, Dayalbagh, Agra (Uttar Pradesh), India.

Manuscript received on 01 September 2024 | Revised Manuscript received on 13 September 2024 | Manuscript Accepted on 15 November 2024 | Manuscript published on 30 November 2024 | PP: 7-18 | Volume-13 Issue-4, November 2024 | Retrieval Number: 100.1/ijrte.D815413041124 | DOI: 10.35940/ijrte.D8154.13041124

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© 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: This paper addresses the challenge of accurately measuring the E-Service Quality (ESQ) of app-based travel portals by using a structured framework of linguistic and mathematical variables. The approach allows for the evaluation and improvement of key performance indicators through fuzzy aggregation, providing a quantitative understanding of service quality in dynamic environments. The proposed method evaluates the system’s state by analysing the parametric values of its subcomponents, which define the system at any given time. We identify three primary factors crucial for assessing ESQ: the state change required relative to the current system state and the relative significance of tasks. The paper elaborates on three critical variables: the importance of a factor (Si), its observed level (Li), and the contribution of the factor (Δi) towards ESQ. These variables are assessed using fuzzy aggregation, converting linguistic inputs into crisp numeric outputs, thereby quantifying the impact of each factor. The methodology provides a structured and flexible approach to evaluating and enhancing the performance of travel portals, focusing on key performance indicators and their corresponding weights.

Keywords: E-Service Quality, Travel Apps, Mobile Service Quality, Technology Acceptance, Digital Tourism, Customer Experience, Service Quality Dimensions.
Scope of the Article: Recent Engineering & Technology

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]]> D815313041124 https://www.ijrte.org/portfolio-item/d815313041124/ Mon, 25 Nov 2024 03:40:35 +0000 https://www.ijrte.org/?post_type=portfolio&p=49511 The post D815313041124 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Integrating the Syracuse Algorithm with K-MEAN: A Comprehensive Approach to Energy Optimization in Wireless Sensor Networks
Yacouba OUATTARA

Yacouba OUATTARA, University Joseph KI-ZERBO, Ouagadougou, Kadiogo, Burkina Faso. 

Manuscript received on 31 August 2024 | Revised Manuscript received on 12 September 2024 | Manuscript Accepted on 15 November 2024 | Manuscript published on 30 November 2024 | PP: 1-6 | Volume-13 Issue-4, November 2024 | Retrieval Number: 100.1/ijrte.D815313041124 | DOI: 10.35940/ijrte.D8153.13041124

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© 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: In deploying a sensor network in a challenging environment, it is crucial to consider energy consumption to ensure an extended network lifespan. Since the inception of sensor networks, researchers have proposed various energy-saving solutions outlined in the introduction. In our study, we introduce a novel approach for cluster formation and positioning of clusters and base stations to minimise energy consumption in cluster implementations using the K-MEAN algorithm. Through simulation, we demonstrate that the Syracuse-WSN algorithm significantly outperforms the traditional K-MEANS algorithm in conserving energy consumption.

Keywords: Energy, K-MEAN, Syracuse, WSN, SHEM.
Scope of the Article: Distributed Sensor Networks

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