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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Kumar, P. Ananthajothi, K. Duraimurugan, N. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331541170

Several studies on the advantage of eating mindfully have concentrated on the idea that maintaining a healthy diet can aid people in preventing the onset of chronic illness including cancer, diabetes, and obesity. It is time-consuming to learn the nutritional worth and contents of each item we eat on a regular basis. So, we require a "Intelligent Chat Bot"that uses artificial intelligence to respond to queries about food's nutritional information and other healthier substitutes. It makes use of algorithms and information databases to comprehend user input and produce responses about diet and nutrition. Natural language comprehension techniques form the basis of the chatbot, but voice synthesis and recognition are also crucial for the achievement of the agent's interactions. This Chabot offers the user the nutritional information for the foods we consume every day, together with recommendations for local food options from the closest market, and also safe chat history storage for future users' input. © 2024 IEEE.

关键词： Bot (Internet)

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Machine Learning Prognosis for Smartphone Dependency 5

Machine Learning Prognosis for Smartphone Dependency

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Julian, Anitha Prathima, S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Concern over smartphone addiction has grown recently as more and more people show signs of addiction, including compulsive phone use, decreased productivity, and even physical and mental health issues. Consequently, a growing necessity exists for the creation of efficient tools to forecast smartphone addiction and pinpoint individuals susceptible to this behavior. The proposed model is crafted to predict smartphone addiction using data collected from a survey of individuals who use smartphones. The survey encompassed inquiries related to demographics, phone use patterns, and various psychological aspects like anxiety, depression, and stress. a popular and effective machine learning method, to build our model. After pre-processing the data, the model is trained and its performance assessed by its accuracy, to see whether the model could be learned efficiently. Our findings demonstrated how effective the recommended treatment was at preventing smartphone addiction. Key features crucial for predicting addiction included patterns of phone use, such as the frequency of checking notifications, daily hours spent on the phone, and the predominant types of frequently used apps. Other important factors included age, gender, and stress levels. Our designed model has multiple possible uses. The solution finds its usage by healthcare professionals to identify individuals prone to potential risks of developing smartphone addiction and provide appropriate interventions. It could also be used by app developers to design apps that are less addictive and promote healthier phone use habits. © 2024 IEEE.

关键词： addiction healthcare machine learning prediction smart phone

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BCI-Based Motor Imagery EEG Signal Classification Using a Novel Method (EEG-ITT) in Upper-Limb Exoskeleton

BCI-Based Motor Imagery EEG Signal Classification Using a No...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Titkanlou, Maryam Khoshkhooy Monjezi, Ehsan Moucek, Roman University of West Bohemia Department of Computer Science and Engineering Pilsen Czech Republic Shahid Chamran University Department of Electrical Engineering Ahvaz Iran

ISBN: (纸本)9798350386226

Brain-computer interface (BCI) is an emerging technology that receives, processes, and converts brain signals into commands sent to output devices to perform desired tasks. Motor imagery (MI) based on electroencephalograms (EEGs) is one of the most widely used BCI paradigms, and it has demonstrated potential as an effective tool for neurorehabilitation. Recently, neural networks-in particular, deep architectures-have received substantial attention for the analysis of EEG signals (BCI applications). This paper proposes a new classification algorithm called EEG-ITT to increase the accuracy of classification motor imagery EEG signals using a non-invasive brain-computer interface. Utilizing a motor imagery dataset of 29 healthy subjects, including males aged 2126 and females aged 18-23, the proposed model demonstrated the highest accuracy, at 79.53 %. The noise injection method has also been implemented for data augmentation. © 2024 IEEE.

关键词： Deep neural networks

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Integrating Data science and Cybersecurity Strategies for Enhancing Power System Resilience

Integrating Data Science and Cybersecurity Strategies for En...

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2024 International Conference on Sustainable Power and Energy, ICSPE 2024

作者： Pareek, Shashank Srivastava, Pratyaksha Sharma, Sudhanshu Abhinav, Kumar Patel, Rupesh Kumar, Saurabh Chandigarh University Department of Computer Science and Engineering Mohali India O.P. Jindal University Department of Electrical Engineering Raigarh India

ISBN: (纸本)9798350366051

The following paper aims at analyzing the role of data science and cybersecurity in strengthening the power systems. Globally, the threats are becoming more complex and frequent where conventional security measures fail to provide the much-needed security solutions. The current paper provides the comparative analysis of data science solutions to enhance the threat detection and response, such as machine learning algorithms and real-time anomaly detection systems. Neural Networks were identified to have yielded the highest detection rates and the least false positive returns and On the other hand clustering based anomaly detection systems was identified as most efficient when used in real time scenarios. The use of such methods along with conventional approaches in cybersecurity enhances the capacities for detecting and mitigating emerging threats hence strengthening the existing power systems. The findings therefore support the development of complex methodologies that enhance the fight against new and changing threats in the protection if critical infrastructure. © 2024 IEEE.

关键词： Cybersecurity

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A Deep Learning Approach to Improve Pulmonary Cancer Diagnosis Using CNN 3

A Deep Learning Approach to Improve Pulmonary Cancer Diagnos...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Sudar, K. Muthamil Nagaraj, P. Raghavendra, V. Jaashvitha, V. Jayasri, Y. Reddy, Yerva Dasaradha Ramanjaneya Mepco Schlenk Engineering College Department of Computer Science and Engineering Virudhnagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India

ISBN: (纸本)9798350361186

It has been proposed that physicians use machine learning-based lung cancer prediction algorithms to treat indeterminate pulmonary nodules that are discovered by accident or during screening. These methods might improve decisionmaking, decrease variability in nodule classification, and decrease the proportion of benign nodules that require unnecessary monitoring or investigation. In this work, we summarise the principal lung cancer prediction methods that have been put out so far and discuss some of the advantages and disadvantages of each. We discuss some of the challenges in creating and verifying such methods and outline the path to clinical acceptance. To ensure the best possible quality of input data for the deep learning models, the preprocessing step involves anatomical area segmentation, noise reduction, and image enhancement. The process of feature extraction involves using convolutional neural networks (CNNs) to extract intricate patterns and features from the lung pictures, which are then utilized to diagnose benign and malignant tumors. Typically, a deep neural network or an ensemble of models is used as the classification network to process this information and determine the final diagnosis. A faster and better outcome for patients may result from the recommended pulmonary cancer detection system's ability to assist medical professionals in accurately identifying malignant tumors in lung imaging. Deep learning is projected to be used in lung cancer detection, which will eventually result in earlier and more effective treatment, hence reducing the burden of this fatal condition. © 2024 IEEE.

关键词： Classification (of information)

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Image to Text Conversion Using Deep Learning Algorithms: Survey 10

Image to Text Conversion Using Deep Learning Algorithms: Sur...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Saranya, M. Anusuya, V. Veera Santhiya, R. Vazhan Arul Srimathi, R. National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (纸本)9798350384369

Image Captioning (ICs) seamlessly combines the realms of computer Vision (CV) and Natural Language Processing (NLP) task involved in producing textual sentences that summarise the image content in a way which is understandable to humans. The primary objective is to enable machines to convey the content of images in human-readable language. Image understanding and text formation are typically the two primary processes in the image captioning process. A family of Convolutional Neural Network (CNN) is used to retrieve visual information from an input image during the image understanding process. This visual information includes the image's scenes, relationships, and objects. To translate these visual features into coherent and contextually appropriate captions, recurrent neural network, long short-term memory, transformer-based models, etc. are used in the text generation step. In this article, we propose a survey about the previous research paper and the fundamental steps involved in image captioning tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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Non-contact mmWave Physiological Sensor in Eyewear Based on Spoof Localized Surface Plasmons

Non-contact mmWave Physiological Sensor in Eyewear Based on ...

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2023 IEEE/MTT-S International Microwave Symposium, IMS 2023

作者： Yang, Xin Kurt, Selman A. Zeng, Qihang Tian, Xi Zhao, Mingmin Ho, John S. National University of Singapore Department of Electrical and Computer Engineering Singapore Singapore University of Pennsylvania Department of Computer and Information Science United States

ISBN: (纸本)9798350347647

We develop a compact physiological sensor based on spoof localized surface plasmons (LSPs) to simultaneously detect blinks and heartbeats from the temporal region of the human head by attaching the sensor to eyewear. A double-layer spoof plasmonic resonator is integrated with a 24-GHz transceiver and a microcontroller unit (MCU) on a flexible printed circuit board (fPCB). Motions of the skin caused by blinking and heartbeat modulate the dielectric environment around the sensor, which are sensitively transduced into phase variations of the continuous wave signals from the spoof plasmonic resonator, which can be transmitted to a display device via Bluetooth. Experimental results demonstrate that the sensor is capable of providing accurate physiological monitoring with an average error of 11 ms for blink interval and 21 ms for averaged individual heartbeat interval. © 2023 IEEE.

关键词： Wearable sensors

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PECMS: modeling a personalized energy and comfort management system based on residents’ behavior anticipation in smart home

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Journal of Reliable Intelligent Environments 2024年第2期10卷 123-136页

作者： Raeiszadeh, Mahsa Tahayori, Hooman Bahadori-jahromi, Ali Department of Computer Science and Engineering and IT Shiraz University Shiraz Iran School of Computing and Engineering University of West London London United Kingdom

Indoor electrical systems are aimed to provide comfort to the occupants. However, their operation is contingent on the presence or needs of the residents. Hence, to optimize energy consumption and guarantee the desired comfort level of residents, any indoor energy control system should consider the occupancy dynamism within houses and the occupants' behavior patterns. Moreover, there is a growing demand for localized and personalized comfort controls in residential buildings to improve the occupants’ satisfaction. This paper presents a Personalized Energy and Comfort Management System (PECMS) that optimizes building energy consumption and meanwhile maintains residents’ intended comfort levels by predicting their trajectories. Considering home thermal characterization, PECMS coordinates the building system devices and residents by anticipating residents’ behavior using an activity mining and tracking method. With this capability, efficient scheduling of the electrical devices would be achieved. PECMS is simulated and tested on a dataset from a real-world smart home project, including the home’s actual thermal zones, temperatures, and residents’ preferences. Comparative analysis is conducted to evaluate PECMS against existing methods and systems, showcasing its effectiveness in achieving the desired trade-off between energy consumption and occupant comfort levels. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Energy utilization

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A YOLO - Based Approach for Object and Distance Detection with Speech Output

A YOLO - Based Approach for Object and Distance Detection wi...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Karsanth, K. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373783

This paper introduces a novel approach to real-time distance detection, employing the state-of-the-art YOLO (You Only Look Once) algorithm. Our system seamlessly integrates YOLO's object recognition capabilities with distance estimation, providing a comprehensive solution for visually impaired individuals. The proposed system employs speech output to convey detected objects and their respective distances, enhancing accessibility and autonomy for users with visual impairments. Experimental results demonstrate the effectiveness and efficiency of our approach in various scenarios, marking a significant advancement in assistive technology © 2024 IEEE.

关键词： Object recognition

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LipNET: END-to-ENDLEVEL of Visual and Video Lip Reading using CNN Network 15

LipNET: END-to-ENDLEVEL of Visual and Video Lip Reading usin...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Deepak, N.R. Pal, Simran R. Abhishek, D.Y. Chandrashekar Vishal, D. Jayasurya, K. Department of Computer Science Engineering HKBK College of Engineering Karnataka India

ISBN: (纸本)9798331300579

The indispensability of lip reading has garnered paramount significance, propelled by the strides achieved in the realm of deep *** transformative paradigm shift not only heralds a commitment to enhanced communication access, especially catering to the auditory challenges faced by individuals with hearing impairments, but also unfolds a plethora of multifaceted *** sophisticated model, characterized by scrupulous preprocessing techniques and state-of-the-art feature extraction methodologies, attains a commendable echelon of accuracy that transcends the limitations inherent in traditional *** amalgamation of lip reading, an intricate cognitive artistry centred around the comprehension of spoken language through discerning visual cues extracted from lip and facial movements, has undergone a profound evolution, reaching new heights with the integration of Convolutional Neural Networks (CNNs). © Grenze Scientific Society, 2024.

关键词： Eye movements

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