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8 pages, 1237 KiB

Open AccessProceeding Paper

Brain Tumor Detection and Classification Using Transfer Learning Models

by Vinod Kumar Dhakshnamurthy, Murali Govindan, Kannan Sreerangan, Manikanda Devarajan Nagarajan and Abhijith Thomas

Eng. Proc. 2024, 62(1), 1; https://doi.org/10.3390/engproc2024062001 - 28 Feb 2024

Viewed by 745

Abstract

Diagnosing brain tumors is a time-consuming process requiring radiologist expertise. With the growing patient population and increased data volume, conventional procedures have become expensive and ineffective. Scholars have explored algorithms for detecting and classifying brain tumors, focusing on precision and efficiency. Deep learning [...] Read more.

Diagnosing brain tumors is a time-consuming process requiring radiologist expertise. With the growing patient population and increased data volume, conventional procedures have become expensive and ineffective. Scholars have explored algorithms for detecting and classifying brain tumors, focusing on precision and efficiency. Deep learning methodologies are being used to create automated systems that can diagnose or segment brain tumors with precision and efficiency, particularly in brain cancer classification. This approach facilitates transfer learning models in medical imaging. The present study undertakes an evaluation of three foundational models in the domain of computer vision, namely AlexNet, VGG16, and ResNet-50. The VGG16 and ResNet-50 models demonstrated praiseworthy performance, thereby instigating the amalgamation of these models into a groundbreaking hybrid VGG16–ResNet-50 model. The amalgamated model was subsequently implemented on the dataset, yielding a remarkable accuracy of 99.98%, sensitivity of 99.98%, and specificity of 99.98% with an F1 score of 99.98%. Based on a comparative analysis with alternative models, it can be deduced that the suggested framework exhibits a commendable level of dependability in facilitating the timely identification of diverse cerebral neoplasms. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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7 pages, 1170 KiB

Open AccessProceeding Paper

Development of an Artificial Neural Network-Based Image Retrieval System for Lung Disease Classification and Identification

by Atul Pratap Singh, Ajeet Singh, Amit Kumar, Himanshu Agarwal, Sapna Yadav and Mohit Gupta

Eng. Proc. 2024, 62(1), 2; https://doi.org/10.3390/engproc2024062002 - 28 Feb 2024

Viewed by 311

Abstract

The rapid advancement of medical imaging technologies has propelled the development of automated systems for the identification and classification of lung diseases. This study presents the design and implementation of an innovative image retrieval system utilizing artificial neural networks (ANNs) to enhance the [...] Read more.

The rapid advancement of medical imaging technologies has propelled the development of automated systems for the identification and classification of lung diseases. This study presents the design and implementation of an innovative image retrieval system utilizing artificial neural networks (ANNs) to enhance the accuracy and efficiency of diagnosing lung diseases. The proposed system focuses on addressing the challenges associated with the accurate recognition and classification of lung diseases from medical images, such as X-rays and CT scans. Leveraging the capabilities of ANNs, specifically convolutional neural networks (CNNs), the system aims to capture intricate patterns and features within images that are often imperceptible to human observers. This enables the system to learn discriminative representations of normal lung anatomy and various disease manifestations. The design of the system involves multiple stages. Initially, a robust dataset of annotated lung images is curated, encompassing a diverse range of lung diseases and their corresponding healthy states. Subsequently, a pre-processing pipeline is implemented to standardize the images, ensuring consistent quality and facilitating feature extraction. The CNN architecture is then meticulously constructed, with attention to layer configurations, activation functions, and optimization algorithms to facilitate effective learning and classification. The system also incorporates image retrieval techniques, enabling the efficient searching and retrieval of relevant medical images from the database based on query inputs. This retrieval functionality assists medical practitioners in accessing similar cases for comparative analysis and reference, ultimately supporting accurate diagnosis and treatment planning. To evaluate the system’s performance, comprehensive experiments are conducted using benchmark datasets, and performance metrics such as accuracy, precision, recall, and F1-score are measured. The experimental results demonstrate the system’s capability to distinguish between various lung diseases and healthy states with a high degree of accuracy and reliability. The proposed system exhibits substantial potential in revolutionizing lung disease diagnosis by assisting medical professionals in making informed decisions and improving patient outcomes. This study presents a novel image retrieval system empowered by artificial neural networks for the identification and classification of lung diseases. By leveraging advanced deep learning techniques, the system showcases promising results in automating the diagnosis process, facilitating the efficient retrieval of relevant medical images, and thereby contributing to the advancement of pulmonary healthcare practices. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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8 pages, 4254 KiB

Open AccessProceeding Paper

A Framework for Early Detection of Glaucoma in Retinal Fundus Images Using Deep Learning

by Murali Govindan, Vinod Kumar Dhakshnamurthy, Kannan Sreerangan, Manikanda Devarajan Nagarajan and Suresh Kumar Rajamanickam

Eng. Proc. 2024, 62(1), 3; https://doi.org/10.3390/engproc2024062003 - 28 Feb 2024

Viewed by 318

Abstract

Glaucoma is a highly perilous ocular disease that significantly impacts human visual acuity. This is a retinal condition that causes damage to the Optic Nerve Head (ONH) and can lead to permanent blindness if detected in a late stage. The prevention of permanent [...] Read more.

Glaucoma is a highly perilous ocular disease that significantly impacts human visual acuity. This is a retinal condition that causes damage to the Optic Nerve Head (ONH) and can lead to permanent blindness if detected in a late stage. The prevention of permanent blindness is contingent upon the timely identification and intervention of glaucoma during its initial stages. This paper introduces a convolutional neural network (CNN) model that utilizes specific architectural designs to identify early-stage glaucoma by analyzing fundus images. This study utilizes publicly accessible datasets, including the Online Retinal Fundus Image Database for Glaucoma Analysis and Research (ORIGA), Structured Analysis of the Retina (STARE), and Retinal Fundus Glaucoma Challenge (REFUGE). The retinal fundus images are fed into AlexNet, VGG16, ResNet50, and InceptionV3 models for the purpose of classifying glaucoma. The ResNet50 and InceptionV3 models, both of which demonstrated a superior performance, were merged to create a hybrid model. The ORIGA dataset achieved high accuracy with an F1 Score of 97.4%, while the STARE dataset achieved higher accuracy with an F1 Score of 99.1%. The REFUGE dataset also showed excellent performance, with an F1 Score of 99.2%. The proposed methodology has established a reliable glaucoma diagnostic system, aiding ophthalmologists and physicians in conducting accurate mass screenings and diagnosing glaucoma. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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7 pages, 871 KiB

Open AccessProceeding Paper

Exploration of Multi-Task Scheduling in Multi-Access Edge Computing

by J. Anand and B. Karthikeyan

Eng. Proc. 2024, 62(1), 4; https://doi.org/10.3390/engproc2024062004 - 29 Feb 2024

Viewed by 296

Abstract

The emergence of multi-access edge computing (MEC) has brought about significant advancements in application design and deployment by providing computing resources at the network’s edge. MEC provides computing resources on the fringes of the network, allowing for near-real-time data processing and fast responses [...] Read more.

The emergence of multi-access edge computing (MEC) has brought about significant advancements in application design and deployment by providing computing resources at the network’s edge. MEC provides computing resources on the fringes of the network, allowing for near-real-time data processing and fast responses to user requests. In this context, scheduling plays a crucial role in offloading decisions in multi-access edge computing. The motivations for scheduling are to improve the quality of the experience, reduce latency, and increase performance. In this paper, we explore the various scheduling techniques available for MEC systems, including static scheduling, dynamic scheduling, heuristics, meta-heuristics and hybrid scheduling. We analyze the advantages and disadvantages of each technique and discuss how they can be used to optimize the performance of MEC applications. We also present a case study of an MEC system and demonstrate how the various scheduling techniques can be used to maximize its performance. Finally, we address both the challenges and prospects of MEC scheduling and suggest directions for future research. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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9 pages, 2296 KiB

Open AccessProceeding Paper

Self-Adaptive Waste Management System: Utilizing Convolutional Neural Networks for Real-Time Classification

by Siddharth Bhattacharya, Ashwini Kumar, Kumar Krishav, Sourav Panda, C. M. Vidhyapathi, S. Sundar and B. Karthikeyan

Eng. Proc. 2024, 62(1), 5; https://doi.org/10.3390/engproc2024062005 - 29 Feb 2024

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Abstract

This research presents a novel Self-Adaptive Waste Management System (SAWMS) that integrates advanced technology to address the pressing challenges of waste sorting and classification. SAWMS leverages Convolutional Neural Networks (CNNs) in conjunction with conveyor belt technology to achieve real-time object classification and self-training [...] Read more.

This research presents a novel Self-Adaptive Waste Management System (SAWMS) that integrates advanced technology to address the pressing challenges of waste sorting and classification. SAWMS leverages Convolutional Neural Networks (CNNs) in conjunction with conveyor belt technology to achieve real-time object classification and self-training capabilities. The system utilizes sensors for object detection and a camera for image capture, enabling an accurate initial classification of waste objects into predefined categories such as food waste, metal, and plastic bottles. Notably, our proposed system sets itself apart by its unique ability to adapt and self-train based on classification errors, ensuring ongoing accuracy even in the face of changing waste compositions. Through dynamic adjustments of the conveyor belt’s destination, it efficiently directs waste objects to their appropriate bins for disposal or recycling. This research demonstrates the potential of SAWMS to revolutionize waste management practices, offering an agile and sustainable solution to the evolving challenges of waste sorting and disposal. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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10 pages, 1271 KiB

Open AccessProceeding Paper

Social Media in the Digital Age: A Comprehensive Review of Impacts, Challenges and Cybercrime

by Gagandeep Kaur, Utkarsha Bonde, Kunjal Lalit Pise, Shruti Yewale, Poorva Agrawal, Purushottam Shobhane, Shruti Maheshwari, Latika Pinjarkar and Rupali Gangarde

Eng. Proc. 2024, 62(1), 6; https://doi.org/10.3390/engproc2024062006 - 01 Mar 2024

Viewed by 932

Abstract

There are very renowned social media platforms like Instagram, Twitter, Facebook, etc., with each of which being used by different shareholders across the world to communicate with each other. Social media is a pool of online communication platforms that are based on community [...] Read more.

There are very renowned social media platforms like Instagram, Twitter, Facebook, etc., with each of which being used by different shareholders across the world to communicate with each other. Social media is a pool of online communication platforms that are based on community input, content sharing, and collaborations. The way we communicate, share information, and connect with other people has been revolutionized by social media. This has led to a series of benefits but also posed many challenges, especially in cybersecurity. This paper investigates the varied influences of social media, examining both its good and negative consequences across a variety of industries. It focuses specifically on the cybersecurity concerns posed by the growing usage of social media, shedding light on the vulnerabilities encountered by individuals and organizations. This investigation includes a study of common cybercrimes like phishing, social engineering, burglary via social networking, virus attacks, cyberstalking, identity theft, and cybercasing. This study emphasizes the importance of a complete and targeted cybersecurity approach that includes preventive measures such as privacy enhancements, user training, sophisticated email filtering, robust authentication, and encryption technologies. Individuals and organizations can traverse the evolving social media ecosystem with greater cyber resilience by addressing these challenges and using proactive tactics. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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7 pages, 909 KiB

Open AccessProceeding Paper

Enhancing Fire and Smoke Detection Using Deep Learning Techniques

by Sujith Chitram, Sarthak Kumar and S. Thenmalar

Eng. Proc. 2024, 62(1), 7; https://doi.org/10.3390/engproc2024062007 - 03 Mar 2024

Viewed by 461

Abstract

This exploration focuses on the effective detection of fire and smoke in various environments, both indoors and outdoors, through the application of real-time object detection and image-processing deep learning algorithms. Capturing the essence of our investigation study delves into advancements and applications of [...] Read more.

This exploration focuses on the effective detection of fire and smoke in various environments, both indoors and outdoors, through the application of real-time object detection and image-processing deep learning algorithms. Capturing the essence of our investigation study delves into advancements and applications of real-time deep learning algorithms in safeguarding lives and property from fire-related risks. There are several factors that affect the outbreak of fire, which then becomes uncontrollable just with human efforts. Smoke is the prelude of many fire incidents. Apart from that, smoke emitted by some electronic or chemical components can possess harmful gases which are very much injurious to human health and must be prevented. This paper’s aim is to provide a comprehensive review of existing research in this field, highlighting significant milestones achieved in the path of our exploration. The analysis will concentrate on evaluating the effectiveness of various strategies implemented to date and their practical applications in real-world settings. The in-depth examination will highlight why image-based detection can be more efficient than currently employed sensors. This work illustrates the critical importance of leveraging cutting-edge technology to enhance fire and smoke detection, ultimately contributing to the safety and well-being of individuals and the protection of property. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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8 pages, 1426 KiB

Open AccessProceeding Paper

An Auditory System Interface for Augmented Accessibility: Empowering the Visually Impaired

by Lalit Kumar, Ajay Shriram Kushwaha and Agrim Jain

Eng. Proc. 2024, 62(1), 8; https://doi.org/10.3390/engproc2024062008 - 05 Mar 2024

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Abstract

In 2023, global data revealed that approximately 2.2 billion people are affected by some form of vision impairment. By addressing the specific challenges faced by individuals who are blind and visually impaired, this research paper introduces a novel system that integrates Google Speech [...] Read more.

In 2023, global data revealed that approximately 2.2 billion people are affected by some form of vision impairment. By addressing the specific challenges faced by individuals who are blind and visually impaired, this research paper introduces a novel system that integrates Google Speech input and text-to-speech technology. This innovative approach enables users to perform a variety of tasks, such as reading, detecting weather conditions, and finding locations, using simple voice commands. The user-friendly application is designed to significantly improve social interaction and daily activities for individuals who are visually impaired, underscoring the critical role of accessibility in technological advancements. This research effectively demonstrates the potential of this system to enhance the quality of life for those with visual impairments. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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13 pages, 3027 KiB

Open AccessProceeding Paper

A Comprehensive Review of Metaverse: Taxonomy, Impact, and the Hype around It

by Gagandeep Kaur, Rashi Pande, Ritika Mohan, Shlok Vij, Poorva Agrawal, Purushottam Shobhane, Latika Pinjarkar, Shruti Maheshwari and Pooja Bagane

Eng. Proc. 2024, 62(1), 9; https://doi.org/10.3390/engproc2024062009 - 06 Mar 2024

Viewed by 385

Abstract

There has been widespread interest in the concept of the metaverse in recent years. The aim of this comprehensive review paper is to provide an in-depth analysis of the taxonomy, technological foundations, and historical evolution of the metaverse. The study explores both the [...] Read more.

There has been widespread interest in the concept of the metaverse in recent years. The aim of this comprehensive review paper is to provide an in-depth analysis of the taxonomy, technological foundations, and historical evolution of the metaverse. The study explores both the positive and negative dimensions of the metaverse, including ethical dilemmas, using a robust analytical framework. In addition to studying the metaverse’s impact on various sectors of society and the economy, this study offers an insight into how it will develop in the coming years. A notable highlight is the exploration of the estimated revenue forecast for metaverse money-making, projecting a substantial 40 billion USD by the 2030s. Further, the paper examines cyberbullying within the metaverse, shedding light on the unique challenges it poses. The hype surrounding the metaverse has also been analyzed, as well as its implications for the broader technological landscape. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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5 pages, 716 KiB

Open AccessProceeding Paper

ChatGPT-Powered URL-Based Research Paper Summarizer

by Krishnaveni Srinivasan, Geetha Ganesan and Eashwar Sivakumar

Eng. Proc. 2024, 62(1), 10; https://doi.org/10.3390/engproc2024062010 - 12 Mar 2024

Viewed by 266

Abstract

Generative Pre-Trained Transformer (GPT) models excel in text generation, text compilation, and language-related tasks. ChatGPT is based on the GPT model that responds to queries and has human-like conversational capabilities. This includes writing posts for social media, software codes, emails and essays, etc. [...] Read more.

Generative Pre-Trained Transformer (GPT) models excel in text generation, text compilation, and language-related tasks. ChatGPT is based on the GPT model that responds to queries and has human-like conversational capabilities. This includes writing posts for social media, software codes, emails and essays, etc. We tried to use ChatGPT for creating a research paper summary. During our experimentation using ChatGPT, we found that ChatGPT cannot directly read links or URLs, as it is trained on a large amount of text data. Research paper summarization is essential in academics. Hence to solve this problem of research paper summarization using ChatGPT, a software system was designed. In this paper, we present how the capability of ChatGPT could be enhanced to summarize pdfs when a URL is provided. Furthermore, the generated summary can be modified in different styles of writing such as creative, expanded, shortened, and professional. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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7 pages, 1009 KiB

Open AccessProceeding Paper

A Survey on Applications of Distributed Ledger Technology in Healthcare

by Shinzeer C. K., Ajay Shriram Kushwaha and Avinash Bhagat

Eng. Proc. 2024, 62(1), 11; https://doi.org/10.3390/engproc2024062011 - 14 Mar 2024

Viewed by 340

Abstract

Blockchain technology is a distributed, accessible, open, and decentralized digital ledger used in healthcare to record transactions between devices. It addresses issues like data integrity, privacy, and confidentiality. The COVID-19 pandemic has accelerated the deployment of digital technologies, such as Distributed Ledger Technology, [...] Read more.

Blockchain technology is a distributed, accessible, open, and decentralized digital ledger used in healthcare to record transactions between devices. It addresses issues like data integrity, privacy, and confidentiality. The COVID-19 pandemic has accelerated the deployment of digital technologies, such as Distributed Ledger Technology, which enables a decentralized consensus between operational data states. This technology can enhance data access, integrity, and patients’ control over their credentials. A survey was conducted with the help of the World Health Organization, Kaggle, and the Ministry of Health Government of India and Kerala with the aim to identify potential uses for blockchain technology in immunization and the collection of patient data. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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7 pages, 510 KiB

Open AccessProceeding Paper

Machine Learning-Based Classification of Autism Spectrum Disorder across Age Groups

by Resmi Karinattu Reghunathan, Poornima Nanjagoundan Palayam Venkidusamy, Raju Gopalakrishna Kurup, Bindu George and Neetha Thomas

Eng. Proc. 2024, 62(1), 12; https://doi.org/10.3390/engproc2024062012 - 15 Mar 2024

Viewed by 359

Abstract

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that has gained significant attention in recent years due to its increasing prevalence and profound impact on individuals, families, and society as a whole. In this study, we explore the use of different machine [...] Read more.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that has gained significant attention in recent years due to its increasing prevalence and profound impact on individuals, families, and society as a whole. In this study, we explore the use of different machine learning classifiers for the accurate detection of ASD in children, adolescents, and adults. Furthermore, we conduct feature reduction to identify key features contributing to ASD classification within each age group using Cuckoo Search Algorithm. Logistic Regression has the highest accuracy compared to the other two models. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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10 pages, 6551 KiB

Open AccessProceeding Paper

Multi-Choice Diet Recommendation Application for Indian Scenario Based on Insights from Ensemble Learning Techniques

by Karthika Subbaraj

Eng. Proc. 2024, 62(1), 13; https://doi.org/10.3390/engproc2024062013 - 18 Mar 2024

Viewed by 230

Abstract

Indian diet has changed over the years as a result of geographic, cultural, and traditional influences. However, Western influences have recently had a substantial impact, including changes in eating behaviors and an increase in the consumption of fast food. Adoption of these foods [...] Read more.

Indian diet has changed over the years as a result of geographic, cultural, and traditional influences. However, Western influences have recently had a substantial impact, including changes in eating behaviors and an increase in the consumption of fast food. Adoption of these foods has also led to a shift from traditional Indian meals, which mostly consists of a variety of grains, vegetables, lentils, and spices. In contrast to fast food, which is frequently highly processed and deficient in important nutrients, these traditional diets are frequently more wholesome, balanced, and nutritious. Obesity rates in India have risen rapidly, with a significant increase in cases of diabetes, heart disease, and other diet-related illnesses. According to the World Health Organization (WHO), the occurrence of obesity tripled between 1975 and 2016 and is continually rising. This research work seeks to give customers a meal plan that complies with their calorie needs based on their BMI and TDEE. The meal plan will only include foods that are available in India. A dish may also be substituted if the user does not have access to it. The meal plan is generated using the random forest classifier and the alternatives are provided by using the KNN algorithm. The classifier has an accuracy of 91%. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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7 pages, 3097 KiB

Open AccessProceeding Paper

Domination Number in the Context of Some New Graphs

by Slashi Leel, Sweta Srivastav, Sangeeta Gupta and Geetha Ganesan

Eng. Proc. 2024, 62(1), 14; https://doi.org/10.3390/engproc2024062014 - 18 Mar 2024

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Abstract

Let G = (V, E) be a graph, where D is a subset of V, such that the set of vertices in the set (V-D) is adjacent to at least one vertex in set D. Then, set D is known as the domination [...] Read more.

Let G = (V, E) be a graph, where D is a subset of V, such that the set of vertices in the set (V-D) is adjacent to at least one vertex in set D. Then, set D is known as the domination set. In other words, a dominating set in graph G is a set of vertices S, such that every vertex in the graph either belongs to S or is adjacent to at least one vertex in S. In this paper, we investigate the domination number for some different types of graphs like antiprism graph A_n, alternate pentagonal snake A(PS_n), cycle with one chord, and m-copies of cycle C_n with one chord. The domination number is frequently employed in computer science for tasks such as to optimize network design, algorithm design, security analysis, etc. Complex computational and network related challenges can be solved by domination number. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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6 pages, 876 KiB

Open AccessProceeding Paper

Neuroscience Empowering Society: BCI Insights and Application

by Harish S. Sinai Velingkar, Roopa Kulkarni and Prashant Patavardhan

Eng. Proc. 2024, 62(1), 15; https://doi.org/10.3390/engproc2024062015 - 18 Mar 2024

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Abstract

The study of brainwaves and brain–computer interfaces (BCIs) or brain–machine interfaces (BMIs) has emerged as a transformative field with the potential to revolutionize society’s well-being. This technical paper delves into the multifaceted domain of brainwave analysis and its integration with BCIs, presenting an [...] Read more.

The study of brainwaves and brain–computer interfaces (BCIs) or brain–machine interfaces (BMIs) has emerged as a transformative field with the potential to revolutionize society’s well-being. This technical paper delves into the multifaceted domain of brainwave analysis and its integration with BCIs, presenting an approach that aims to enhance the fabric of society through various applications, with BCIs aiding in various assistive technologies, the detection of neurological abnormalities, and biofeedback mechanisms for improved concentration. This study explores the relationship between brainwave patterns and the levels of focus using EEG data. The results reveal distinct changes in brainwave activity, notably in the delta and beta frequency ranges, corresponding to different levels of cognitive engagement. Building upon these findings, we propose the development of a biofeedback-based concentration enhancement program for students. This study, using an approach equipped with real-time EEG monitoring and feedback mechanisms, aims to empower students to improve their concentration, particularly in educational settings. Such an innovative approach holds promise for enhancing academic performance and learning experiences, offering valuable insights into the optimization of cognitive functions through neurofeedback interventions. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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7 pages, 1190 KiB

Open AccessProceeding Paper

Implementation of an Advanced Health-Monitoring System Capable of Real-Time Analysis and Alerting

by Kumari Pragya Prayesi, Shabana Azami, Vineet Raj Singh Kushwah, Sagarika Nayak, Santosh Yerasuri, T. Sumallika and Mohit Gupta

Eng. Proc. 2024, 62(1), 16; https://doi.org/10.3390/engproc2024062016 - 20 Mar 2024

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Abstract

The integration of technology into healthcare has moved from being a luxury to being a need at a time when there is an ever-increasing focus on individual health and wellbeing. The real-time monitoring of one’s health metrics and prompt alerts in the event [...] Read more.

The integration of technology into healthcare has moved from being a luxury to being a need at a time when there is an ever-increasing focus on individual health and wellbeing. The real-time monitoring of one’s health metrics and prompt alerts in the event of anomalies or irregularities hold enormous promise for proactive health management. This study’s objective and value are revealed in this setting. This project combines cutting-edge hardware and software technologies that have been specially designed to meet the fitness and health requirements of the modern person. The ESP8266 microcontroller, a flexible and potent platform, which functions as the system’s brain, easily integrates with a pulse sensor to deliver precise and uninterrupted heart rate monitoring. Real-time analysis of the recorded physiological data enables the early diagnosis of anomalies, abnormal heart rate patterns, or perhaps serious health events. The system uses a Buzzer to trigger a notice when such abnormalities are discovered, assuring prompt user attention. This cutting-edge health-monitoring system was designed with user security, privacy, and accessibility at its core, in addition to usefulness. A favorable user experience is guaranteed by the rigorous calibration of the alerting mechanisms, which deliver useful information without raising unnecessary concern. This initiative, which takes a user-centric approach, seeks to serve a wide range of users, from elders and people with chronic health conditions to athletes and fitness aficionados. This system’s implementation process has gone through a number of stages, including hardware integration, firmware development, algorithm design, and user interface refining. The equipment’s health-monitoring capabilities have undergone evaluation and calibration to guarantee their dependability and accuracy. Moreover, this project’s evolution has been significantly shaped by user input and continuous improvements. This essentially epitomizes the nexus of contemporary technology and healthcare, providing a practical answer for those who are concerned about their health and advancing the development of health-monitoring systems. It equips users with the tools required to play a proactive role in promoting the early identification of health issues, ensuring users’ wellbeing and offering peace of mind in a society that is becoming more and more health-conscious. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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8 pages, 751 KiB

Open AccessProceeding Paper

Edge Computing in Context Awareness: A Comprehensive Study

by V. Mahalakshmi and B. Karthikeyan

Eng. Proc. 2024, 62(1), 17; https://doi.org/10.3390/engproc2024062017 - 15 Mar 2024

Viewed by 228

Abstract

Mobile edge computing (MEC), which is now gaining a lot of momentum, allows users to use its services with low latency, location awareness, and mobility assistance to offset the drawbacks of cloud computing. The quality of the experience, reduced latency, and boosted performance [...] Read more.

Mobile edge computing (MEC), which is now gaining a lot of momentum, allows users to use its services with low latency, location awareness, and mobility assistance to offset the drawbacks of cloud computing. The quality of the experience, reduced latency, and boosted performance are the ultimate context-aware goals. There have been many context-aware efforts in the past. In this study, we reviewed many elements of the proposed context-aware approach for edge cloud computing, including their benefits and drawbacks. Additionally, we looked at such context-aware techniques to determine which were practical given the situation. We anticipate that the survey will be carefully considered in the creation of new context-aware methods. Future directions and problems have also been looked at. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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8 pages, 1027 KiB

Open AccessProceeding Paper

The Development of Early Flood Monitoring and a WhatsApp-Based Alert System for Timely Disaster Preparedness and Response in Vulnerable Communities

by P. Kavitha, Yatheesh.K.C., Akash Anand, Sruthi Sreenivasan, Hashim Mohammed S, Naiwrita Borah and Dhrubajyoti Saikia

Eng. Proc. 2024, 62(1), 18; https://doi.org/10.3390/engproc2024062018 - 20 Mar 2024

Viewed by 450

Abstract

Although many innovations have been achieved and natural disasters are well known to be extremely detrimental to persons and property, there is still no 100% assurance that alerts and real-time monitoring will work. Vulnerable communities sometimes relied on crude warning systems, such as [...] Read more.

Although many innovations have been achieved and natural disasters are well known to be extremely detrimental to persons and property, there is still no 100% assurance that alerts and real-time monitoring will work. Vulnerable communities sometimes relied on crude warning systems, such as flood gauges, observation towers, and local messengers, to deal with the unpredictable nature of floods. However, the effectiveness and reach of these strategies were constrained, leaving many people vulnerable to the disastrous effects of flooding. Therefore, the integration of cutting-edge technology and a system that is integrated and innovative overcomes the limits of conventional flood monitoring systems. The incorporation of WhatsApp, a widely used messaging service, into the flood monitoring and alerting process is a unique aspect of our system. We increase the reach and efficiency of our early flood warning system by combining standard SMS with WhatsApp messages. Additionally, our system includes sophisticated flood monitoring features that continuously monitor crucial parameters, including water levels. Administrators and authorized operators can respond quickly when the system sends alerts in reaction to aberrations from established thresholds. This invention bridges the gap between cutting-edge hardware and modern communication methods, representing a substantial advance in flood management technology. In conclusion, this research emphasizes how technology has the potential to improve catastrophe preparedness and response. It offers evidence of how innovation may be used to solve pressing problems and protect vulnerable areas from natural catastrophes, ultimately boosting resilience to flood events. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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7 pages, 1072 KiB

Open AccessProceeding Paper

Traffic Signal Control System Using Contour Approximation Deep Q-Learning

by R. S. Ramya, K. K. Bharath, K. Revanth Krishna, Kancham Jaswanth Reddy, Maddipudi Sri Bhuvan and K. R. Venugopal

Eng. Proc. 2024, 62(1), 19; https://doi.org/10.3390/engproc2024062019 - 22 Mar 2024

Viewed by 333

Abstract

A reliable transit system is essential and offers a lot of advantages. However, traffic has always been an issue in major cities, and one of the main causes of congestion in these places is intersections. To reduce traffic, a reliable traffic control system [...] Read more.

A reliable transit system is essential and offers a lot of advantages. However, traffic has always been an issue in major cities, and one of the main causes of congestion in these places is intersections. To reduce traffic, a reliable traffic control system must be put in place. This research sheds light on how to consider dynamic traffic at intersections and minimize traffic congestion using an end-to-end deep reinforcement learning approach. The goal of the model is to reduce waiting times at these crossings by controlling traffic in various scenarios after receiving the necessary training. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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9 pages, 431 KiB

Open AccessProceeding Paper

Comparing Different Machine Learning Techniques in Predicting Diabetes on Early Stage

by Shweta Yadu, Rashmi Chandra and Vivek Kumar Sinha

Eng. Proc. 2024, 62(1), 20; https://doi.org/10.3390/engproc2024062020 - 22 Mar 2024

Viewed by 366

Abstract

One of the diseases that is constantly spreading and is estimated to cause a significant number of deaths worldwide is diabetes mellitus. It is determined by the quantity of a blood sugar molecule made from glucose. The possibility of this disease has been [...] Read more.

One of the diseases that is constantly spreading and is estimated to cause a significant number of deaths worldwide is diabetes mellitus. It is determined by the quantity of a blood sugar molecule made from glucose. The possibility of this disease has been predicted using a variety of methods. To forecast diabetes at an early stage, adequate and clear data on diabetic individuals are needed. In this study, 520 records from a hospital in Bangladesh with 16 different characteristic numbers were used to make predictions. At UCI, this dataset is accessible to everyone. We used Random Forest, Ada Booster, KNN, and Bagging algorithms after feature selection. Through 10-fold cross-validation, it was discovered that the Random Forest method had the best test accuracy, scoring 97.03% correctly and 95.03% correctly. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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9 pages, 3264 KiB

Open AccessProceeding Paper

Comparing New Wireless Sensor Network Protocols through Simulation and Data Analysis

by Himanshu Agarwal, Atul Pratap Singh, Ajeet Singh, Amit Kumar, Pratik K. Agrawal and S. Saranya

Eng. Proc. 2024, 62(1), 21; https://doi.org/10.3390/engproc2024062021 - 07 Apr 2024

Viewed by 220

Abstract

The resource-constrained nature of wireless sensor networks (WSNs) creates a number of difficulties in their operation and design that lower their performance. However, distinct applications with unique constraints in their nature make it more difficult for such resource-constrained networks to meet application objectives. [...] Read more.

The resource-constrained nature of wireless sensor networks (WSNs) creates a number of difficulties in their operation and design that lower their performance. However, distinct applications with unique constraints in their nature make it more difficult for such resource-constrained networks to meet application objectives. These issues can be observed at various WSN layers, from the physical layer up to the application layer. Routing protocols are primarily focused on WSN functioning at the routing layer. These obstacles make routing protocols perform worse, which lowers the performance of WSNs as a whole. This study’s objective is to pinpoint WSN performance issues and examine how they affect routing protocol performance. To this end, a detailed literature review was conducted to determine the problems influencing the performance of the routing protocols. Then, an actual investigation was carried out by simulating various routing protocols, taking into consideration these issues, in order to validate the impact of the discovered challenges from the literature. The findings are shown. On the basis of the findings from the empirical study and the literature review, suggestions are offered for a better protocol choice in light of the application nature and the problems that need to be addressed. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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6 pages, 1994 KiB

Open AccessProceeding Paper

Facial Expression Recognition Using Pre-trained Architectures

by Resmi K. Reghunathan, Vineetha K. Ramankutty, Amrutha Kallingal and Vishnu Vinod

Eng. Proc. 2024, 62(1), 22; https://doi.org/10.3390/engproc2024062022 - 22 Apr 2024

Viewed by 179

Abstract

In the area of computer vision, one of the most difficult and challenging tasks is facial emotion recognition. Facial expression recognition (FER) stands out as a pivotal focus within computer vision research, with applications in various domains such as emotion analysis, mental health [...] Read more.

In the area of computer vision, one of the most difficult and challenging tasks is facial emotion recognition. Facial expression recognition (FER) stands out as a pivotal focus within computer vision research, with applications in various domains such as emotion analysis, mental health assessment, and human–computer interaction. In this study, we explore the effectiveness of ensemble methods that combine pre-trained deep learning architectures, specifically AlexNet, ResNet50, and Inception V3, to enhance FER performance on the FER2013 dataset. The results from this study offer insights into the potential advantages of ensemble-based approaches for FER, demonstrating that combining pre-trained architectures can yield superior recognition outcomes. Full article

(This article belongs to the Proceedings of The 2nd Computing Congress 2023)

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