Neutrosophic analysis of the factors determining the development of humorous discourse in videos using the TOPSIS method

Alejandra A. Vera; Xiomara N. Lindo; Paolo A.; Muhammad Eid

doi:https://doi.org/10.54216/IJNS.240423

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

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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

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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

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Journal of Cybersecurity and Information Management | 2024

Novel Slumped SRAM Configuration using QCA Leveraging Differential Voltage Sensing for Enhanced Stability and Efficiency

This paper presents a novel Slumped Static Random-Access Memory (SRAM) configuration utilizing Quantum-dot Cellular Automata (QCA) technology, aimed at achieving enhanced stability and efficiency. Traditional CMOS-based SRAM designs face significant challenges related to power consumption and scalability as technology nodes shrink. QCA, with its potential for ultra-low power dissipation and high-density integration, emerges as a promising alternative. Our proposed SRAM configuration leverages a unique differential voltage sensing mechanism to bolster the stability of the memory cells, particularly under conditions of variability and noise. Through detailed simulations and comparative analysis, we demonstrate that the Slumped SRAM configuration not only improves static noise margin (SNM) but also reduces power consumption and enhances overall read/write speed. The results indicate a substantial improvement in stability and operational efficiency, positioning this design as a viable solution for future high-performance, low-power memory applications. Through detailed simulations and comparative analysis, we demonstrate that the Slumped SRAM configuration achieves a static noise margin (SNM) improvement of 35% over conventional CMOS-based SRAM designs. Additionally, the proposed design reduces power consumption by 40% and enhances read/write speed by 25%. These results indicate a substantial improvement in stability and operational efficiency, positioning this design as a viable solution for future high-performance, low-power memory applications.

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N. Naga Saranya mail -

V. Jean Shilpa mail -

K. Jayakumar mail -

P. Senthil mail -

M. Arun mail

link https://doi.org/10.54216/JCIM.140114

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Prediction of Traffic Congestion in Vehicular Ad-Hoc Networks Employing Extreme Deep Learning Machines (Edrlm)

Vehicular Ad-Hoc Networks (VANETs) represent a crucial component of intelligent transportation systems (ITS), enabling vehicles to communicate with each other and with roadside infrastructure. Predicting traffic congestion in VANETs is essential for enhancing road safety, optimizing traffic flow, and improving overall transportation efficiency. Traditional machine learning methods have shown promise in this domain; however, they often fall short in handling the complex, high-dimensional data typical of VANETs. To address these challenges, this study employs Extreme Deep Learning Machines (EDRLM), an advanced deep learning technique, for traffic congestion prediction. The EDRLM framework leverages the strengths of deep neural networks and extreme learning machines, offering a robust and scalable solution for processing the dynamic and heterogeneous data in VANETs. By integrating feature extraction, selection, and prediction into a unified model, EDRLM can capture intricate patterns and temporal dependencies within traffic data. The proposed model is trained and validated using real-world VANET datasets, incorporating various traffic parameters such as vehicle speed, density, and inter-vehicular distances. Our experimental results demonstrate that EDRLM outperforms conventional machine learning algorithms in terms of prediction accuracy, computational efficiency, and robustness to noise and missing data. The model's ability to provide timely and precise congestion predictions can facilitate proactive traffic management strategies, including dynamic routing and adaptive traffic signal control, ultimately leading to reduced travel times and enhanced road safety. This study underscores the potential of EDRLM in transforming traffic management in VANETs, paving the way for more intelligent and adaptive ITS solutions. Future research directions include exploring hybrid models combining EDRLM with other advanced machine learning techniques and expanding the framework to accommodate emerging vehicular communication technologies such as 5G and Internet of Things (IoT) devices.

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R. Logesh Babu mail -

Jagannadha Naidu K. mail -

V. Jeya Ramya mail -

Regan D. mail

link https://doi.org/10.54216/JCIM.140115

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2024

Sharp Estimates for the Zalcman Conjecture and Second Order Hankel Determinant

In this work, we found sharp estimates for the Zalcman conjecture and second order Hankel determinant for the inverse function when it belongs to the class of starlike functions with respect to symmetric points, denoted by . These results are new.

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Audy Hatim Saheb mail

link https://doi.org/10.54216/GJMSA.0100206

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2024

Neutrosophic analysis of the factors determining the development of humorous discourse in videos using the TOPSIS method

YouTube is moving towards personalized media. In 2011, Enchufe TV became an Ecuadorian online comedy series known for its witty humor. Taking advantage of the openness of the Internet, the video is currently available to watch on the YouTube platform. The purpose of this study is to conduct an unbiased analysis of the factors that determine the development of humorous discourse in TV Antufe's YouTube videos using the TOPSIS method. To understand the growth of the show and its audience, we compared its premiere year to 2022 across 10 years. At the same time, data such as comedy type, language level, audio-visual narrative, and humorous discourse were collected to quantitatively understand the popularity and influence of the play at the time. There. Variables such as views, audience engagement, and subscriber base growth are analyzed, as well as objective measures of content relevance and influence within the platform environment. Enchufe TV's decline in user activity can also be explained by several factors, such as the emergence of new platforms and content saturation. We also found that blue spoken words were the most widely used, with popularity varying by year of study.  

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Alejandra A. Vera Vera mail -

Xiomara N. Lindo Quito mail -

Paolo A. Ortiz mail -

Muhammad Eid Balbaa mail

link https://doi.org/10.54216/IJNS.240423

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

Details open_in_new