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All Journal International Journal of Applied and Advanced Multidisciplinary Research (IJAAMR) International Journal of Educational and Psychological Sciences (IJEPS) International Journal of Integrated Science and Technology
Meenakshi Thalor
Unknown Affiliation
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences

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AI in Healthcare 5.0: Opportunities and Challenges Soham Date; Meenakshi Thalor
International Journal of Applied and Advanced Multidisciplinary Research Vol. 2 No. 1 (2024): January, 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijaamr.v2i1.281

Abstract

The advent of Explainable AI (XAI) in healthcare, often referred to as Healthcare 5.0, presents both significant opportunities and challenges. XAI promises to enhance clinical decision-making by providing transparent and interpretable insights into AI-driven diagnoses and treatment recommendations, thereby increasing trust and adoption among healthcare practitioners. This paper explores the evolving landscape of XAI in healthcare, highlighting its potential to improve patient outcomes, reduce errors, and optimize resource allocation. However, it also addresses the challenges of implementing XAI, including data privacy concerns, regulatory hurdles, and the need for robust validation methods. Balancing these opportunities and challenges is critical for realizing the full potential of XAI in revolutionizing healthcare delivery.
A Hybrid Acoustic And Deep Learning Approach For Enhanced Speech Emotion Recognition Bharshankar, Gargi; Meenakshi Thalor
International Journal of Applied and Advanced Multidisciplinary Research Vol. 1 No. 1 (2023): September 2023
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijaamr.v1i1.291

Abstract

Emotion recognition in speech is a key research topic in human-computer interaction. Understanding emotions in conversations can shed light on a person's well-being. This study introduces a hybrid architecture that combines acoustic and deep features for improved speech emotion recognition. Acoustic features like RMS energy and MFCC are extracted from voice records. Additionally, sound spectrogram images are processed using deep networks like VGG16 and ResNet to obtain deep features. These are merged into a hybrid feature vector, refined by the ReliefF algorithm. For classification, the Support Vector Machine is employed. Testing on datasets like RAVDESS and EMO-DB yielded accuracy rates up to 90.21%. Our method consistently outperformed existing techniques in accuracy.
Character Recognition in Air-Writing based on Network of Radars for Human-Machine Interface Bhalerao, Siddhi; Meenakshi Thalor
International Journal of Applied and Advanced Multidisciplinary Research Vol. 1 No. 2 (2023): October 2023
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijaamr.v1i2.345

Abstract

Radar technology can detect hand gestures without touching, making it an intuitive way to interact with computers. Air-writing means writing in the air with hand movements. We’ve created an air-writing system using millimeter-wave radars. Our method has two steps: first, we figure out where your hand is and track its movement. Then, we use this data in two ways: one uses a special kind of neural network to understand the hand’s path and recognize characters, and the other turns the hand's path and recognize characters, and the other turns the path into an image and uses another neural network to figure out which letters were drawn. The first method works really well, with a 98.33% accuracy rate for character recognition, similar to the second method. We tested this with real data from three radar sensors at 60 GHz. This setup and method show promise for human-computer interaction without touching.
Customized Education Artificial Intelligence's Role In Tailored E-Learning. Kanbur, Gaurang; Meenakshi Thalor
International Journal of Applied and Advanced Multidisciplinary Research Vol. 1 No. 3 (2023): November 2023
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijaamr.v1i3.469

Abstract

This research is driven by the immense potential of personalized e-learning systems to address the challenges of effective online education delivery. It focuses on proposing an efficient architectural framework for personalized e-learning systems, exploring various techniques and challenges and offering innovative solutions. The paper conducts a thorough review of current state-of-the-art methodologies in implementing personalized e-learning systems, along with discussions on the crucial requirements and challenges for successful deployment. Furthermore, it presents an efficient framework for building effective e-learning systems, while also discussing mechanisms, challenges and future research directions that the research community can consider. The subsequent sections of this paper provide a detailed exploration of the research, followed by a proposal for a personalized learning system, and insights into important issues for the community to address. The paper concludes by summarizing its findings and contributions.  
Machine Learning Based Weed Detection System Gajbhiye, Prathamesh; Meenakshi Thalor
International Journal of Applied and Advanced Multidisciplinary Research Vol. 1 No. 4 (2023): December 2023
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijaamr.v1i4.568

Abstract

This abstract underscores the importance of weed detection in crop cultivation to prevent plant diseases and minimize crop losses. To address these challenges and promote eco-friendly practices, the authors propose a weed detection program employing K-Nearest Neighbors, Random Forest, Decision Tree algorithms, and the YOLOv5 neural network. The abstract also provides a concise overview of existing research in weed identification using machine learning and deep learning. The authors developed a YOLOv5-based weed detection system and evaluated the performance of the algorithm, showing traditional classifiers achieve accuracies of 83.3%, 87.5%, and 80%, while the neural network scores range from 0.82 to 0.92 for each class. The study demonstrates the effectiveness of this approach in classifying low-resolution weed images.
A Proposed Healthcare Architecture using Cloud Computing in WSN Environment with a case study Meenakshi Thalor; Yash Gharat
International Journal of Integrated Science and Technology Vol. 2 No. 1 (2024): January 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijist.v2i1.1288

Abstract

Internet of Things (IoT) considers a future that can connect something/someone/ any service with appropriate information communication technology that it brings innovation in the fields of home, smart home, medical system, article surveillance, and logistics. Because of this inclination, this paper develops a trailblazer: Internet of Things-aware, intelligent architecture, that can be used to monitor and track patients and other related computing devices personnel, and within hospitals premises. In line with the IoT vision, we propose IoT based healthcare architecture dependent on different but complementary technologies, especially the WSN, RFID, and smart mobile, all interacting via a Constrained Application Protocol (CoAP) over low-power wireless personal area network (6LoWPAN) framework. The framework possesses the capability to gather data instantaneously in natural status and in physical conditions of patients after which it gets processed for analysis, thereby providing services to the user. The proposed model also focuses on the security aspect in the network of the healthcare system. The security model proposes IoT based medical services which comprises three security services: protection, detection and reaction services
Psychological Assessment of Autism Spectrum Disorders using Machine Learning Meenakshi Thalor; Sarthak Patil
International Journal of Educational and Psychological Sciences Vol. 2 No. 1 (2024): January 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijeps.v2i1.1297

Abstract

This research aims to advance the field of autism spectrum disorder (ASD) assessment by proposing a machine learning-based approach tailored for school and community settings. Leveraging a diverse dataset encompassing behavioral, physiological, and neuroimaging data, we apply advanced machine learning algorithms to develop predictive models for early ASD detection. Our study integrates expert interviews to validate the clinical utility of these models and explores ethical considerations surrounding data privacy and bias. Preliminary results show promising accuracy in ASD identification. This research contributes to a more accessible and objective ASD assessment, with implications for early intervention and inclusive support, particularly in educational and community contexts.
Co-Authors Bhalerao, Siddhi Bharshankar, Gargi Gajbhiye, Prathamesh Kanbur, Gaurang Sarthak Patil Soham Date Yash Gharat