• Send Your paper at :
  • ijirtm@gmail.com
  • editor@ijirtm.com

Impact Factor - 5.445
Impact Factor - 5.68



IJIRTM: Volume-8, Issue-1, 2024

Paper Title : A Study to Assess the Effectiveness of Planned Teaching Program Regarding Stem Cell Preservation among Antenatal Mothers in Rural and Urban Area of Bhopal City Madhya Pradesh
Author Name : Prof.Dushyant Sharma
Keywords : Stem Cell Preservation, Antenatal, Mothers.
Abstract :

Mother and baby share a perfect bond from the period of conception and it is she who nurtures and gives the best of everything to her child. And with the advancement of the technologies she is not just bound to care for her baby at the present but she can now gift her baby with a gift of health, through stem cell and cord blood banking this is effort of science for construction of technology for tomorrow. To assess the pre exiting knowledge of antenatal mother regarding stem cell preservation. To find out the effectiveness of Planned Teaching Programme regarding antenatal mother regarding stem cell preservation by comparing pretest and posttest knowledge score. To find out the associations between the pretest knowledge score with selected demographic variables.

Download IJIRTM-8-1-0801202401
Paper Title : Congestion-Aware Multi-Route Establishment Routing for Mobile Ad Hoc Networks (MANET)
Author Name : Kanchan Narware, Prof.Chetan Agrawal, Prof.Pooja Meena
Keywords : MANET, Queue, Load balancing, Routing, Multipath, MEALBM, Congestion.
Abstract :

This research delves into the realm of Mobile Ad-hoc Networks (MANETs), dynamic and infrastructure-less wireless communication systems that offer flexibility for deployment at any time and place. With the nodes in MANETs constrained by limited battery capacity, energy optimization becomes a pivotal design consideration. This paper comprehensively reviews prior works aimed at enhancing the longevity and various performance parameters of MANETs. A primary challenge in MANETs is congestion, arising from the restricted link capacity between nodes. To tackle this issue, the research proposes a resolution using the Ad-hoc On-demand Multipath Distance Vector (AOMDV) routing protocol, introducing alternative paths to alleviate congestion. Comparative analysis with the MEALBM scheme underscores the superior performance of the proposed approach, affirming its effectiveness in addressing congestion and contributing to the overall enhancement of MANET capabilities.

Download IJIRTM-8-1-0801202402
Paper Title : A Review on Cloud Computing, Security Issues and Techniques
Author Name : Dinesh Kumar Malviya, Prof.Pooja Meena, Prof.Chetan Agrawal
Keywords : Cloud Computing, Security Issues, Techniques Used, Encryption.
Abstract :

Cloud computing is a network-built invention that allows users to access information whenever they need it. Existing cloud computing systems have stringent safeguards in place to preserve user data confidentiality. Cloud computing is made up of a number of technologies and regulations to protect infrastructure, services, and data. Since the infrastructure is not owned by the customer, the conventional security architecture is quite difficult to implement. The paper presented the review associated with cloud computing as well as strategies for users to mitigate these risks and problems. The paper also covers the current challenges for cloud computing to protect their infrastructure from threats and hackers.

Download IJIRTM-8-1-0801202403
Paper Title : No-Shows Appointment Prediction Using Machine Learning: A Review
Author Name : Vaishali Shukla, Prof.Pooja Meena, Prof.Chetan Agrawal
Keywords : No-Shows, Missed-Appointment, Machine Learning.
Abstract :

Clinics and hospitals globally are losing substantial profits due to patient no-shows. This challenge is faced by any healthcare facility allowing advance appointment bookings, as patients may not show up or cancel too late. To mitigate this, many hospitals use costly reminder systems and overbooking strategies, where multiple patients are booked for the same slot. These methods, however, do not fully address the economic and social impacts of high no-show rates. To tackle this issue, the paper presented a review on techniques used for appointment scheduling and intuitive management systems that includes features for automated handling of high-risk appointments using case-specific outcome predictions, thereby aiming to reduce idle time and overtime for medical staff. The paper also shows the role of machine learning for maintaining efficient handling of such system.

Download IJIRTM-8-1-0801202404
Paper Title : Examining Diabetic Retinopathy (DR) Through Deep Learning
Author Name : Gagan Deep Kaur, Prof.Pooja Meena, Prof.Chetan Agrawal
Keywords : Diabetic Retinopathy, Classification, Image Processing, Deep Learning, Segmentation, Severity Grade.
Abstract :

Diabetic retinopathy (DR) is a complex ailment affecting individuals with diabetes, leading to retinal damage and the potential onset of blindness. This condition disrupts retinal blood vessels, resulting in fluid leakage and severe distortion of vision. DR is a prevalent eye disease linked to chronic diabetes and stands as the primary cause of blindness among working-age adults globally, potentially impacting more than 93 million individuals. This study introduces an automated classification system designed to analyze fundus images under varying illumination and fields of view. Leveraging machine learning models such as Otsu, Random Forest, and Clehe algorithm, the system generates severity grades for diabetic retinopathy. The incorporation of machine learning models, particularly Random Forest, brings forth the advantage of high variance and low bias. This characteristic allows the classifier not only to diagnose diabetic retinopathy but potentially detect a broader spectrum of nondiabetic diseases. Visualizations of features learned by the Region-Based Convolutional Neural Network(RCNN) and Gray Level Matrix Co-occurrence (GLMC) demonstrate that the signals used for classification predominantly originate from clearly observable parts of the image. Notably, moderate and severe diabetic retinal images showcase macroscopic features aligned with the architecture's training and validation accuracy. This research introduces a promising avenue for automated diabetic retinopathy severity classification, offering potential advantages in early diagnosis and intervention for the eye health of diabetic patients.

Download IJIRTM-8-1-0801202405