An Intelligent IoT Framework for Heart Diseases Prediction Using Harris Hawk Optimized GRNN

Parvathy S.; A. Packialatha

doi:https://doi.org/10.54216/JISIoT.130119

International Journal of Neutrosophic Science | 2024

Enhanced Brain Tumor Diagnosis through Differential and Canonical Quadri –Partitioned Neutrosophic Set Classification Methods:A Comparative Study

An early cancer diagnosis is carried out for adequate management of diseases. Magnetic resonance imaging (MRI) is most commonly preferred method for cancer diagnosis. Due to the uncontrolled and rapid growth of cells, brain tumor is occurred. If not treated at a preliminary phase, it may lead to death. Thus, a noteworthy prerequisite for a successful treatment outcome is an early and precise diagnosis.Many conventional methods are discussed for performing efficient tumor detection. But, conventional classification methods not distinguish MRI as primary and metastases tumors in an accurate manner. Therefore, the performance comparison of deep learning-based classification (i.e., Differential Quadri-Partitioned Neutrosophic Interval-valued Polynomial Attention-based Deep CNN (DQNI-PADCNN) method and Canonical Quadri-Partitioned Neutrosophic Set based Otsuka–Ochiai Deep Recurrent Neural Network (CQNS-ODRNN) method) is introduced to provide exact image classification results. The brain MRI images are considered as an input. MRI image classification is carried out through CNN and RNN to find the brain tumor disease. Before the classification process, input images are de-noised. The noise-removed images are get segmented to identify the region of interested regions. Later, the images are classified into four classes such as glioma, meningioma, no tumor, and pituitary classes to detect the brain tumor. Both classification methods use Quadri-Partitioned Neutrosophic set for categorizing the images. Depending on CNNs and RNNs achievement in handling intricate tasks, an optimal multi-class brain tumor diagnosis is carried out. Experimental evaluation is implemented using MATLAB 2017 for brain tumor detection with the Brain Tumor MRI dataset. To the total number of MRI images, the various performance metrics are calculated in terms of sensitivity, specificity, accuracy, and time for the detection of brain tumors.

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A. Panimalar mail -

P. Sugapriya mail -

D. Aarthi mail -

S. Santhosh Kumar mail -

K. Mohana mail -

F. Nirmala Irudayam mail

link https://doi.org/10.54216/IJNS.240420

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2024

Product of rings based on neutrosophic sets

In this paper, we introduce the notion of the intrinsic product of neutrosophic sets, and some related properties are investigated. Characterizations of neutrosophic subrings, neutrosophic ideals, neutrosophic quasi-ideals, and neutrosophic bi-ideals are given.

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Aiyared Iampan mail -

S. R. Vidhya mail -

N. Rajesh mail -

B. Brundha mail

link https://doi.org/10.54216/IJNS.240421

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2024

Two Inclusive Subfamilies of bi-univalent Functions

The aim of this article is to establish two new and qualitative subfamilies F(ε, κ, &alefsym;) and G(ε, κ, &alefsym;) of biunivalent functions. For functions in these subfamilies, we determine the first two Maclaurin coefficient estimations |C2| and |C3|, and address the Fekete–Szeg¨o problem. Additionally, we mention some corollaries related to the main results.  

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Tariq Al-Hawary mail -

Ala Amourah mail -

Jamal Salah mail -

Feras Yousef mail

link https://doi.org/10.54216/IJNS.240422

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

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Journal of Intelligent Systems and Internet of Things | 2024

Improving Loan Status Prediction Accuracy with Generative Adversarial Networks: Addressing Data Scarcity and Bias

A precise and reliable loan status prediction is of the essence for financial institutions, However, the lack of real-world data and biases within that data can greatly impact the accuracy of machine learning models. Another challenge faced by loan status prediction models is class imbalance, where one category (such as approved loans) is much more common than another (such as defaulted loans), leading to skewed predictions towards the majority class. This study inspects Generative Adversarial Networks (GANs) to augment the data and improve the machine learning models’ performance. Several machine learning (ML) models including but not limited to Support Vector Machines (SVM) and ensemble bagged trees were employed on a Kaggle loan dataset (380 samples). Baseline training and testing accuracies were 86.9% and 86.3% (SVM) and 84.5% and 82.1% (ensemble). ActGAN (Activating Generative Networks) was then utilized to generate synthetic data points for both accepted and rejected loans. Retraining the models with new augmented data showed remarkable improvements: SVM accuracies for training and testing rose to 94.4% and 93.4%, while ensemble models achieved 97.4% and 95.8%, respectively. Other ML models were also explored such as KNN, Decision tree and logistic Regression and showed promising results in terms of accuracy as compared to the state of art. These findings put forward that GAN-based data augmentation can enhance the performance of loan status prediction. Future research could explore GAN’s impact of different architectures and assess the general applicability of this approach.

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Enas A. Raheem mail -

Ahmed M. Dinar mail -

Mazin Abed Mohammed mail -

Bourair Al-Attar mail

link https://doi.org/10.54216/JISIoT.130118

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

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Journal of Cognitive Human-Computer Interaction | 2024

Comprehensive Survey of Driver Drowsiness Systems

Driver monitoring systems have been improved over time as artificial intelligence and computer technology have advanced. Several experimental studies have collected real-world driver drowsiness data and used various artificial intelligence algorithms and feature combinations to dramatically improve the real-time effectiveness of these systems. This study presents an updated assessment of the driver sleepiness detection systems implemented over the last decade. In modern automobiles, assessing the driver's cognitive condition is an important aspect of passenger safety. The term "cognitive state" refers to a driver's mental and emotional state, which has a substantial impact on their ability to drive safely. Drivers' cognitive states may be altered by factors such as fatigue, distraction, stress, or disability. Intelligent automotive technology may be able to adapt and aid the driver by identifying varied conditions in real-time, reducing the frequency of accidents. The face, being an integral component of the body, communicates a significant quantity of information. The facial expressions, such as blinking and yawning patterns, exhibit changes in a driver when they are experiencing fatigue.

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Anandhi S. mail -

Deepti S. mail -

Anitha Pai mail

link https://doi.org/10.54216/JCHCI.080202

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

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Journal of Cognitive Human-Computer Interaction | 2024

UNI – A Retrieval Augmented Generation Powered Virtual Assistant for College Related Queries

This paper unveils an advanced chatbot engineered to cater specifically to college-related inquiries. Harnessing the power of BARD and incorporating a wake word activation system with automatic speech recognition, the chatbot offers an enhanced user experience marked by both linguistic sophistication and spoken command initiation. The methodology encompasses the nuanced process of pre-training on diverse corpora, fine-tuning to optimize responsiveness to college-specific queries, and the seamless integration of intent classification and entity recognition. These facets collectively empower the chatbot to understand and respond effectively to the intricacies of user inputs. A comprehensive knowledge base is curated to ensure not only accurate information retrieval but also to foster a depth of contextual understanding. This project signifies a pioneering leap in providing an innovative, user-friendly, and ethically driven solution for addressing college-related queries through natural language interactions. By showcasing practical advancements in chatbot technology tailored to the educational landscape, this research contributes to the evolving landscape of intelligent virtual assistants.

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Sanjana J. mail -

Mahadev Prasad Y. N. mail -

Srinivas B. mail -

Sharanya S. mail -

Madhusudhan M. mail

link https://doi.org/10.54216/JCHCI.080203

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

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Journal of Cognitive Human-Computer Interaction | 2024

Personalized Music Playlists via Deep Learning Emotion Detection

Music holds significant sway in enriching the lives of individuals, serving as a vital source of entertainment for enthusiasts and listeners alike. Moreover, it transcends mere amusement, often adopting a therapeutic role in people’s lives. In the ever-evolving landscape of music and technology, this project emerges as a groundbreaking endeavor, driven by the profound impact music holds on individuals’ lives. Leveraging technological advancements in music players, such as playback control and genre classification, our focus is on revolutionizing playlist creation. Instead of the laborious manual curation of playlists, we introduce automation based on users’ emotional states, identified through real-time facial expression analysis via a camera. The human face, a rich source of mood indicators, becomes the key input for our system. By directly extracting emotional cues from facial expressions, the project aims to swiftly deduce the user’s emotional state, crafting a tailored playlist without the need for time-consuming manual efforts. Implemented through deep learning using VGG16 model, the system ensures intricate emotion recognition from image input. Python, OpenCV, and Keras facilitate seamless video processing and deep learning functionalities, complemented by a music player library for smooth playback control. This amalgamation of computer vision and deep learning delivers an interactive music player that dynamically selects tracks aligned with users’ real-time emotional expressions, offering a personalized and immersive musical experience.

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M. Spoorthi mail -

Harshitha H. M. mail -

Pooja R. mail -

Anusha M. K. mail -

Preethi R. mail

link https://doi.org/10.54216/JCHCI.080204

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

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Journal of Cognitive Human-Computer Interaction | 2024

Mindspeak: Empowering Communication with Brain Keyboard

Brain-Computer Interface (BCI) technology stands as a groundbreaking innovation, revolutionizing the way individuals with severe motor disabilities interact with the world. The integration of Electroencephalogram (EEG) sensors within applications like the Brain Keyboard marks a pivotal stride forward. By capturing and interpreting brain signals triggered by simple actions such as eye blinking, these sensors empower users to control a virtual keyboard, transcending the limitations imposed by traditional motor pathways. This direct channel between the human brain and external devices offers an unprecedented avenue for communication, particularly invaluable for those grappling with conditions like paralysis or locked-in syndrome. The profound impact of BCIs extends far beyond facilitating textual communication; they represent a lifeline, a bridge toward autonomy and engagement for individuals facing profound physical challenges. Through these interfaces, users can articulate thoughts, express emotions, and actively participate in social interactions, fundamentally enhancing their quality of life. This technological marvel not only breaks down communication barriers but also holds promise in broader applications. As BCIs evolve, their potential encompasses enabling control over robotic prosthetics, granting users the ability to accomplish tasks once deemed impossible. Moreover, the implications of BCIs stretch into the realm of neuroscience, offering a unique window into understanding cognitive processes and neurological disorders. The ability to decode and interpret brain activity not only aids in facilitating communication but also paves the way for groundbreaking research and potential therapies. Challenges persist, such as enhancing signal accuracy and streamlining usability, yet the remarkable benefits that BCIs offer to individuals with motor disabilities continue to fuel ongoing innovation in this dynamic field. Ultimately, the fusion of EEG sensors, processing units, and user interfaces in BCIs heralds a new era of inclusivity and empowerment, where individuals previously marginalized by physical limitations find newfound avenues for expression, interaction, and independence. This transformative technology not only unlocks communication but also holds the key to reshaping our understanding of the human brain and its intricate workings, promising a f uture where disabilities no longer confine one's ability to engage with the world.

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Vimala Imogen P. mail -

Jeevaa Katiravan mail -

Nitish R. G. mail -

Vishnudharshan R. mail

link https://doi.org/10.54216/JCHCI.080205

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2024

Visual Harmony Tailoring Video Recommendations through Text

This research develops a novel approach for mood-based YouTube video suggestions. Using cutting-edge textual data analysis techniques, through the application of Natural Language Processing (NLP) techniques combined with sentiment analysis based on the FrameNet framework, users' everyday experiences and feelings are carefully analyzed to determine their current mood in the text. The process of content curation is made easier by the extraction of pertinent video metadata with the help of the YouTube API key. The integration of video metadata with textual mood extraction allows for the development of an extremely engaging and personalized content recommendation system. Users are provided with content that resonates with their current emotional state by matching the recommended movies' mood with the one deduced from the textual input. This improves user satisfaction and enriches their experience.

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Jayakaran P. mail -

Litheeswaran S. mail -

Janakiraman S. mail -

Manikandan mail -

S. Malathi mail

link https://doi.org/10.54216/JCHCI.080206

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

An Intelligent IoT Framework for Heart Diseases Prediction Using Harris Hawk Optimized GRNN

Recently, Heart diseases is considered as the one of deadliest diseases which has resulted in the increased death rates across the globe. Predicting heart diseases requires vast experiences along with advanced knowledge. IoT and AI are two emerging technologies that help in heart disease prediction. High diagnostic accuracy with minimal processing overhead, however, continues to be a design problem for researchers. To address this problem, this paper develops the Intelligent IoT structure for the better prediction of cardiac diseases employing Harris Hawk Optimized Gated Modified Recurrent Units (HHO-M-GRU). The paper also proposes the real time data collection using IoT wearable test beds which comprises of electrocardiography sensors (ECG) interfaced with MICOTT Boards & ESP8266 transceivers. For later processing, the acquired data are saved on the cloud. The proposed deep learning network is utilized for evaluating the received heart data and used for predicting the heart diseases. Additionally, the suggested HHO-GRU is trained with the versatile datasets which consist of normal and abnormal stages of heart diseases. By calculating the suggested model's performance measures, including accuracy, precision, recall, specificity, and F1-score, a thorough experiment is conducted. The proposed framework was implemented in Keras libraries with Tensorflow 2.1.1 as backend. Furthermore, prediction performance and complexity overhead is compared using the other cutting-edge deep learning algorithms already in use to demonstrate the model's superiority. in predicting the heart diseases. The suggested approach beats previous models for learning with respect to of accurate prediction (99%) and minimal computing overhead, according to the results.

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Parvathy S. mail -

A. Packialatha mail

link https://doi.org/10.54216/JISIoT.130119

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new