Real-time Messages Research Articles

Design of EEG based thought identification system using EMD & deep neural network

Biological communication system for neurological disorder patients is similar to the Brain Computer Interface in a way that it facilitates the connection to the outside world in real time. The interdisciplinary field of Electroencephalogram based message depiction is gaining importance as it assists the paralysed person to communicate. In the proposed method a novel approach of feature extraction is done by Empirical Mode Decomposition on non- stationary & non-linear kind of EEG signal. EMD helps in the effective time frequency analysis by disintegrating the EEG signal in the form of six Intrinsic Mode Functions with help of the frequency components. In all nine features are extracted from the decomposed IMFs so as to predict the states or messages of the patient. The above computed features are then served to the Deep Neural Network to perform the classification. The performance of suggested method is studied through applying it to the acquired database generated by the designed hardware as well as also in real time message depiction. The maximum classification accuracy 97% for the acquired database & 85% in real time are obtained respectively by comparative analysis. The command messages generated from the proposed system helps the person suffering from neurological disorder to establish the communication link with the outside world in an efficient way. Thus, the proposed novel method shows better performance in real time message depiction purpose as related to other existing methods.

Data Stream Protocols for Real-Time Messaging : Optimizing Performance and Reliability

This article explores the design and implementation of data stream protocols for real-time messaging, focusing on key aspects that contribute to their performance and reliability. It examines protocol architectures, data formatting and serialization techniques, error handling mechanisms, and quality of service features. The article compares popular protocols such as WebSocket and MQTT, discusses performance optimization techniques, and addresses security considerations. Emerging trends like WebTransport, HTTP/3, and edge computing integration are also explored, highlighting their potential to reshape the landscape of real-time messaging. By providing a comprehensive overview of current practices and future directions, this article aims to equip developers and system architects with the knowledge needed to design efficient and reliable real-time communication systems.

Cooperative Perception System for Aiding Connected and Automated Vehicle Navigation and Improving Safety

Cooperative perception that integrates sensing capabilities from both infrastructure and vehicle perception sensors can greatly benefit the transportation system with respect to safety and data acquisition. In this study, we conduct a preliminary evaluation of such a system by integrating a portable lidar-based infrastructure detection system (namely, Traffic Scanner [TScan]) with a Society of Automotive Engineers (SAE) Level 4 connected and automated vehicle (CAV). Vehicle-to-everything (V2X) communication devices are installed on both the TScan and the CAV to enable real-time message transmission of detection results in the form of SAE J2735 basic safety messages. We validate the concept using a case study, which aims at improving CAV situation awareness and protecting vulnerable road user (VRU) safety. Field testing results demonstrate the safety benefits of cooperative perception from infrastructure sensors in detecting occluded VRUs and helping CAVs to plan safer (i.e., higher post-encroachment time) and smoother (i.e., lower deceleration rates) trajectories.

Encrypted Chat Application

In today's cyber landscape, the protection of digital communication channels stands as a paramount concern. This abstract dissects the cyber resilience strategies underpinning the development of secure chat applications, with a particular focus on user interface design using Jetpack Compose and encryption integration leveraging Kotlin. The examination commences with an in-depth analysis of the cyber defence mechanisms ingrained within chat interfaces crafted through Jetpack Compose, the cutting-edge toolkit tailored for native Android UI construction. By meticulously adhering to Material Design principles, developers fortify chat interfaces against cyber intrusions, placing a premium on real-time messaging functionalities and multimedia integration to foster user engagement while deterring malicious exploitation.The abstract plunges into the cornerstone role of encryption in fortifying cybersecurity, spotlighting Kotlin's robust encryption capabilities. Through the adept utilization of Kotlin AES encryption algorithms, developers erect an impregnable bastion around user messages, guaranteeing both confidentiality and integrity. Synergizing encryption with Firebase Firestore for fortified data storage and Firebase Authentication for resilient user validation fortifies the application's cyber defense, thereby ensuring user data remains impervious to cyber threats. By amalgamating Jetpack Composes resilient UI design tools with Kotlin's encryption prowess, developers erect a formidable cybersecurity fortress around chat applications. This symbiotic alliance not only safeguards user privacy and data integrity but also reinforces communication channels against the relentless onslaught of cyber adversaries in the ever-evolving cyber landscape Key Words: Encryption Integration, Secure Chat Application, Cyber Resilience Strategies, Cyber Defense Mechanism.

Perancangan Sistem Informasi Pengumuman dan Broadcasting (NotifyCasting)

Managing announcements and communication within a company often faces challenges in ensuring the effective and timely dissemination of information. This study aims to design and develop a web-based Announcement and Broadcasting Information System (NotifyCasting) to improve efficiency in information distribution within the company environment. The method used is the waterfall software development model, which includes the stages of requirement analysis, design, implementation, testing, and system maintenance. The results of the study show that the system successfully meets the functional and non-functional needs of the company by providing features such as real-time messaging and announcement delivery, audience filtering, and an accessible announcement archive. Testing with black box and white box methods indicates that the system functions according to the specified requirements, and the user interface is intuitive and easy to use. In conclusion, the NotifyCasting system can significantly enhance communication effectiveness within the company and can be adapted by other organizations for similar purposes.

Abstract PO2-11-04: Assessing quality of Life in breast cancer patients undergoing mHealth mobile monitoring: A descriptive Analysis

Abstract Background: Breast cancer (BC) can be associated with a significant symptom burden that can be related both to the disease and to administered therapies. In particular, protocol related toxicity has an extreme impact on patient quality of life (QoL), and can lead to lack of adherence and ultimately, compromise therapy success. There is increasing interest in adopting more effective approaches for active/continuous patient-reported symptom monitoring. Emerging evidence, (Denis et al. 2019), shows that this strategy can reduce toxicity, emergency room visits, and improve QoL. Objectives: We aimed to analyze the QoL of women with BC diagnosis who underwent mobile-based continuous monitoring, searching for possible trends and describing the population. Methods: We conducted a retrospective analysis using data from patients with BC undergoing systemic treatment and monitored by a mobile-based application between January 2022 and June 2023 at an outpatient clinic in Brazil. Thummi is a digital platform for remote monitoring of cancer patients which enables them to record symptoms, toxicities and lifestyle variables. Utilizes the data to generate personalized recommendations and advice. It is available in Brazil at no cost. Doctors indicate the use and can monitor their patients in real time. In addition to an algorithm that informs the user if there is a need to go to the emergency room or not, the medical team can exchange real-time messages with patients. Clinicians have access to patient data through a web tool, allowing them to visualize the information, address symptom alerts, and make informed clinical-therapeutic decisions. The evaluated sample is from patients who come from physicians who indicated the use, without control of their origin, indication of treatment or clinical stage. The data were extracted from the platform's total database. Participants were requested to complete the Quality of Life Questionnaire (EORTC EQ-5D) integrated into the app every month. Inclusion criteria comprised a BC diagnosis, age ≥18, and having responded to the QoL questionnaire with at least a one-month interval between scores. Patients who failed to meet these criteria were excluded from the analysis. A comparison of QoL scores was performed between two periods: T0 (the first recorded response) and T1 (the subsequent response with at least a one-month interval between scores). Descriptive statistical analyses were conducted to search for possible trends, describe the population, and comprehensively examine the differences in QoL scores between T0 and T1. Results: There were 134 active patients in the mobile-based platform with a BC diagnosis, but only 86 (64,17%) met the inclusion criteria for the final analysis. The median age of this group was 54 years old (range 22-78). The most common treatments administered were Cyclophosphamide-Doxorubicin (28 patients, 33%) and Trastuzumab-Deruxtecan (18 patients, 21%). The average QoL score in T0 was 0,70 (SD 0,1379) and 1,0 (SD 0,1560) at T1, representing an increase of 30% after using the app for one month. Conclusion: This study suggests that mobile-based monitoring has the potential to improve the QoL for women diagnosed with BC. The difference in average QoL scores between the initial and subsequent periods indicates a positive impact of utilizing the platform. Further research is needed to establish a better correlation and to explore the specific interventions that contribute to these improvements. Implementing mobile-based monitoring in clinical practice could enhance patient well-being, treatment adherence, and survival time and can potentially lessen both burden of disease and resource utilization in BC care. Citation Format: Alessandra Morelle, Carlos Barrios, Carlos Pereira, Vinicius Agibert, Pedhro Lennon Freitas, Matheus Rocha, Paulo Schulze. Assessing quality of Life in breast cancer patients undergoing mHealth mobile monitoring: A descriptive Analysis [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-11-04.

Secure Data Sharing in Federated Learning through Blockchain-Based Aggregation

In this paper, we explore the realm of federated learning (FL), a distributed machine learning (ML) paradigm, and propose a novel approach that leverages the robustness of blockchain technology. FL, a concept introduced by Google in 2016, allows multiple entities to collaboratively train an ML model without the need to expose their raw data. However, it faces several challenges, such as privacy concerns and malicious attacks (e.g., data poisoning attacks). Our paper examines the existing EIFFeL framework, a protocol for decentralized real-time messaging in continuous integration and delivery pipelines, and introduces an enhanced scheme that leverages the trustworthy nature of blockchain technology. Our scheme eliminates the need for a central server and any other third party, such as a public bulletin board, thereby mitigating the risks associated with the compromise of such third parties.

Wind Chat: Robust & Dynamic Flutter Messaging Application

"Wind Chat" is an Android messaging application developed using Flutter and Google Firebase. It enables users to sign in with their Gmail accounts, send friend requests, and engage in real-time messaging. It is a robust messaging application developed using cutting-edge technologies - Flutter and Google Firebase. Flutter, an open-source UI software development toolkit, allows for the creation of natively compiled applications for mobile, web, and desktop from a single codebase. Its expressive and flexible framework ensures a seamless user experience across various platforms. Complementing Flutter, Google Firebase serves as the backend infrastructure, providing a suite of powerful tools for authentication, real-time database management, and cloud storage. Firebase Authentication ensures secure user sign-ins through various methods, while Firebase Real time Database facilitates dynamic and synchronized data updates in real-time. Additionally, Firebase Cloud Storage caters to the reliable storage of multimedia content, a pivotal aspect of Wind Chat's functionality. This integration ensures secure and efficient user authentication and authorization, while also enabling dynamic and responsive data handling, critical for a real-time messaging application like Wind Chat. The combination of Flutter and Firebase empowers "Wind Chat" with a compelling and dynamic user interface, ensuring fluid navigation and an intuitive messaging experience. This fusion of innovative technologies underpins Wind Chat's ability to deliver a feature-rich and user-centric platform for seamless communication.

Combined Motivational Interviewing and Ecological Momentary Intervention to Reduce Hazardous Alcohol Use Among Sexual Minority Cisgender Men and Transgender Individuals: Protocol for a Randomized Controlled Trial.

Sexual minority cisgender men and transgender (SMMT) individuals, particularly emerging adults (aged 18-34 years), often report hazardous drinking. Given that alcohol use increases the likelihood of HIV risk behaviors, and HIV disproportionately affects SMMT individuals, there is a need to test interventions that reduce hazardous alcohol use and subsequent HIV risk behaviors among this population. Ecological momentary interventions (EMIs), which use mobile phones to deliver risk reduction messages based on current location and behaviors, can help to address triggers that lead to drinking in real time. This study will test an EMI that uses motivational interviewing (MI), smartphone surveys, mobile breathalyzers, and location tracking to provide real-time messaging that addresses triggers for drinking when SMMT individuals visit locations associated with hazardous alcohol use. In addition, the intervention will deliver harm reduction messaging if individuals report engaging in alcohol use. We will conduct a 3-arm randomized controlled trial (N=405 HIV-negative SMMT individuals; n=135, 33% per arm) comparing the following conditions: (1) Tracking and Reducing Alcohol Consumption (a smartphone-delivered 4-session MI intervention), (2) Tracking and Reducing Alcohol Consumption and Environmental Risk (an EMI combining MI with real-time messaging based on geographic locations that are triggers to drinking), and (3) a smartphone-based alcohol monitoring-only control group. Breathalyzer results and daily self-reports will be used to assess the primary and secondary outcomes of drinking days, drinks per drinking day, binge drinking episodes, and HIV risk behaviors. Additional assessments at baseline, 3 months, 6 months, and 9 months will evaluate exploratory long-term outcomes. The study is part of a 5-year research project funded in August 2022 by the National Institute on Alcohol Abuse and Alcoholism. The first 1.5 years of the study will be dedicated to planning and development activities, including formative research, app design and testing, and message design and testing. The subsequent 3.5 years will see the study complete participant recruitment, data collection, analyses, report writing, and dissemination. We expect to complete all study data collection in or before January 2027. This study will provide novel evidence about the relative efficacy of using a smartphone-delivered MI intervention and real-time messaging to address triggers for hazardous alcohol use and sexual risk behaviors. The EMI approach, which incorporates location-based preventive messaging and behavior surveys, may help to better understand the complexity of daily stressors among SMMT individuals and their impact on hazardous alcohol use and HIV risk behaviors. The tailoring of this intervention toward SMMT individuals helps to address their underrepresentation in existing alcohol use research and will be promising for informing where structural alcohol use prevention and treatment interventions are needed to support SMMT individuals. ClinicalTrials.gov NCT05576350; https://www.clinicaltrials.gov/study/NCT05576350. PRR1-10.2196/55166.

Design and Development of Collabosphere: A Cloud-based Collaboration Platform

Abstract: CollaboSphere is a cutting-edge cloud-based platform for collaboration that was created mainly with students in mind. It addresses the difficulties associated with information sharing and teamwork in both academic and professional settings. CollaboSphere combines the capabilities of a project directory and collaboration centre to provide features including task/issue tracking, real-time messaging, document sharing. CollaboSphere is powered by the powerful MERN stack, DevOps methodologies, and AWS cloud infrastructure, which guarantees performance, scalability, and reliability. Students may easily interact, share thoughts, and access collective expertise in this environment thanks to its novel features, which include a public project directory and an accessible interface. CollaboSphere is a new era of teamwork and knowledge sharing that embodies development and innovation in student collaboration through careful deployment, management, and scaling strategies

Energy aware and secure routing for hierarchical cluster through trust evaluation

With the rapid growth of technology advancement in wireless communication have resulted in interconnection of large number of devices covering wide range of application services. Wireless sensor network (WSN) have become popular and provide various benefits, including low-power smart computation, compact size, self organizing and ability to route via routing protocols. WSN covers a wide range of application services, including home automation, smart cities, fire detection, unmanned aerial vehicle (UAV), and object tacking. Sensor nodes are tiny, battery operated, and suffer from fundamental restrictions such as available bandwidth, memory space, power restrictions, and limited resources. Due to their communication characteristics and working environment, WSN faces energy optimization and secure data communication challenges to increase network lifetime and defend against malicious nodes. To overcome energy scarcity and consumption issues, clustering methods have proven smart and effective approaches to reduce energy consumption and extend network life. Low energy adaptive clustering hierarchy (LEACH), well-known clustering method, enhances energy efficiency by selecting cluster head (CH) on a rotation basis by employing random integer technique. To extend network stability period, extensions of LEACH was proposed. LEACH and its extensions mainly focus on optimal selection of CH and fail to concentrate on secure communication. Integration of blockchain and real-time message content validation (RMVC) in LEACH was modelled to detect malicious node through secure hash algorithm. However, existing scheme had complex cryptographic functions and higher computation overhead. In this paper, we present energy aware and secure routing (EASR) for hierarchical cluster based WSN. Existing LEACH is modified and an improved threshold value is adapted to select secure optimal CH and trust based routing model which combines direct and indirect trust opinion values to resist routing attacks for establishing trustworthy and reliable routes towards base station.

Shear thickening and charge-storing interlayer-based all-aerosol-sprayed wearable triboelectric sensor for industrial wireless human-machine interfaces

In harsh factory environment, achieving design and deployment of wearable sensor is challenging for foundation of industrial internet of things (IoT) systems. Herein, we introduce an all-aerosol-sprayed impact-resistant wearable triboelectric sensor (SC-WTS) that integrates SiO2 nanoparticle-based shear thickening and a charge-storing (SC) interlayer. This interlayer synergistically enhances both anti-impact and self-powered sensing performances, enabling precise detection with a wide pressure range of 0.25–10 kPa, outstanding linearity of 0.96, and high sensitivity of 21.1 V/kPa. Fabricated using an all-aerosol-spray process, the SC-WTS is highly adaptable to diverse wearable substrates, including Kevlar, leather, and plastic plates, and exhibits excellent impact resistance against low-velocity impacts. By conjugating the in-situ fabrication, outstanding self-powered sensing performance, and superior anti-impact performance, a real-time safety monitoring IIoT system was developed utilizing an industrial wireless server network and open platform communications unified architecture. The system enables supervisors to detect industrial collisions and emergency messages in real time through the triboelectric signals generated by the SC-WTS module, which is embedded in safety clothes of onsite workers. This work provides the practical concept of a WTS based on an SC interlayer with excellent anti-impact and self-powered sensing performance for wireless human-machine interfaces under extreme conditions at industrial sites.

Penerapan Bot Telegram untuk Sistem Monitoring Jaringan Intranet Daerah di Instansi Pemerintahan

Currently, the internet has become a basic need in the process of providing information and knowledge. Public, government and private services compete with each other in the use of information technology, which is expected to support employee performance and service. Therefore, human resources are needed who not only master material aspects, but also have adequate capacity and ability to apply and develop information in all fields. The use of this monitoring system is very necessary for the sidoarjo communicarions and information services departement and institutions that operate in the network sector and also have a fairly broad and complex customer coverage. Problems that often occur are slow internet connections, broken network cables, damaged network devices, etc. At that time, a network monitoring system was needed as a system that could detect when the network connection was lost. The research method used in this research is experimentation, namely a category of research that aims to find the cause and effect situation of a phenomenon. Therefore, this research is to investigate cause and effect, in accordance with previous conditions. From the results of the tests carried out, Mikrotik succeeded in sending notification messages in real time to the Telegram bot, with an average response time of 1 second in each condition. Router and Telegram will detect and send real-time message notifications to administrators if the internet connection status changes from off to active or vice versa

RMPF: Real-Time Message Processing Framework on Multi-Service Domains

RMPF: Real-Time Message Processing Framework on Multi-Service Domains

Value Co-creation in Sports Live Streaming Platforms: A Microfoundations Perspective

As a primarily synchronous social media form, social live streaming services offer real-time interaction between streamers and viewers, and among viewers. Users' value cocreation has become increasingly crucial for platform businesses to increase their competitive advantage. However, the previous studies using the microfoundations approach have only confirmed the employees' efforts to adopt technology as a way to achieve the firms' goals. In this article, we explore the microfoundations of external actors' (viewers and streamers) value cocreation on sports live streaming platforms (SLSPs). Taking China Sport as a case study, this article conducts netnography research with observations made of four live-streamed matches on the final matchday of the International Table Tennis Federation World Tour Grand Final 2019. In total, 16 204 real-time messages and 5540 gifting messages are reviewed. In-depth interviews are also conducted with 5 streamers and 15 viewers. As a result, a typology of viewers (managers, fans, and audiences) emerges, and five viewer–streamer–viewer value cocreation activities are revealed. Furthermore, the unique value-in-use among streamers and viewers in different activities is found. This study presents a model to show that the viewers' engagement and the value cocreation activities between viewers and streamers at a microlevel determines the value-in-use formation, which, in turn, contributes to the competitive advantages for SLSPs at a macrolevel. This study contributes to the existing literature on the engagement behavior and value cocreation by empirically examining the role of external actors' engagement as the microfoundations of value cocreation in the context of new social technologies—SLSPs.

Analysis of methods of classification of electronic messages based on neural network models

In the article, the creation of a mechanism for detecting and classifying messages is considered, with an assessment of how effectively different neural networks work and can recognize and classify different types of electronic messages, including phishing attacks, spam, and legitimate messages. A preliminary analysis of incoming messages has been performed, encompassing their headers, text, and other relevant attributes. For instance, in the case of emails, these attributes could be the 'subject' and 'sender' of the message. Methods for data preparation and processing have been reviewed, including text vectorization, noise removal, and normalization, to be utilized in training neural networks. Message tokenization has been performed by transforming them into a numerical format while considering the selection of features. For text messages, it is crucial to execute both tokenization and text vectorization. The model training was performed on the test data with prior splitting into two parts: 80% for training and 20% for testing. The training set is utilized for training the model, while the test set is used to evaluate its effectiveness. The peculiarity of the class structure of the data, namely the uniformity of the distribution of classes, is considered. In this case, spam occurs less frequently than legitimate messages, so class balancing techniques such as random deletion of redundant examples, upsampling, and subsampling were applied to ensure adequate model training. Optimization of network parameters was performed, by researching the optimal parameters of neural networks, such as the number and size of layers, activation functions, and optimization of hyperparameters to achieve the best performance. Hyperparameter optimization includes determining optimal settings for neural networks, such as layer size, activation functions, learning rate, and other parameters. The effectiveness was assessed by comparing the results and performance of various classification methods based on neural networks using metrics such as precision and F1-score. It was determined how well the methods can avoid misclassifications where legitimate messages are mistakenly identified as spam, and vice versa. A comparison of the methods' effectiveness in processing a large volume of messages in real time was conducted. An analysis of different architectures of neural network models was performed. Based on the analysis, it was revealed how effectively different neural network models can recognize and classify messages as spam.

Twitter's Strategy and Market Share Analysis

With the growth of the internet and smartphones, social media are having explosive growth in overall users. For already existing social media, like Twitter, Facebook, etc., appropriate strategic choices are indispensable for market expansion. In the case of Twitter, the relationship between its strategy and market share is vacant in academia. Therefore, the research topic of this paper is the relationship between Twitter's strategy and the amount of market share. The research method of this paper is as follows: Firstly, relevant literature is collected for systematic analysis, and secondly, the relationship between strategy implementation and market share change is analyzed through StatCounter's social media market share data. It was found that Twitter's strategy in deepening its features, i.e., real-time messaging and news, diversified communication, and interaction with celebrities was successful, but it did not get the desired results in expanding its own users' scope and providing new non-core features. Therefore, for existing social media, solidifying the existing target group is the first option in competition. For companies that want to run a social media business, identifying a specific market segment is the key to winning the fierce competition.

Real-time inspection and fault detection for large photovoltaic arrays based on drones and deep learning algorithms

In recent years, the installation of renewable energy generation systems based on photovoltaic (PV) panels has experienced massive increments and PV parks with thousands of panels are now becoming commonplace. Yet, there are some challenges, like inspection and fault detection. Lately, these operations have been approached using drones. This project adds the use of deep learning, more specifically proposes the convolutional neural network (CNN) algorithm, the YOLOv5 model and Real-Time Messaging Protocol (RTMP) protocol to achieve real-time detection of PV panels failures. The YOLOv5 model was trained by sets sorted into 9 different categories including fault and abnormal objects’ coverage. This multi-class classification system was investigated by a variety of evaluation indexes to show effectiveness and accuracy. The system was also examined with its different fault classes. The performance results demonstrate that the mean average precision could reach up to 98% with a good training set, confirming the feasibility of proposed approaches.

IOT Based Fuel Theft Monitoring System

Abstract: This research paper mainly focuses on development of a "Fuel Theft Monitoring System " which addresses the pressing issue of fuel theft, which poses significant financial losses and operational disruptions for vehicle owners and fleet managers. This project aims to develop an innovative solution that accurately measures the quantity of fuel poured into a tank and sends real-time message alerts in case of fuel theft events. The system incorporates sensor-based technology and a secure communication system to ensure precise monitoring and timely alerts. The project's results demonstrate accurate fuel measurement, effective detection of theft incidents and compatibility with various fuel tanks. The system's cost-effectiveness and potential integration with existing tracking systems contribute to its accessibility and usability for a wide range of users.

Students Community App Using Flutter

The Student Community App is a comprehensive digital platform meticulously crafted to cater to the diverse needs of today's student population. At its core, the app seeks to create a vibrant and interconnected community within educational institutions, transcending the limitations of physical boundaries. A key feature of the app is its intuitive and user-friendly interface, ensuring accessibility for all students. Real-time messaging capabilities form the backbone of communication, enabling instant and direct connections between individuals or within groups. This facilitates seamless collaboration on academic projects, extracurricular activities, and even fosters casual social interactions. The app goes beyond traditional communication tools by incorporating dynamic group forums. These forums serve as virtual spaces where students can engage in in-depth discussions, share valuable insights, and seek or offer academic support. This not only enhances the learning experience but also cultivates a culture of knowledge-sharing and mutual growth