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Enhancing security in digitized healthcare system using blockchain technology.

In the modern medical industry, sensors and other medical electronic equipment such as ECG, thermometers, etc. that measure vitals like blood pressure, body temperature, heartbeat, and blood cells are used to record patient data. The information gathered helps to create a patient profile, which in turn helps doctors treat patients appropriately and helps users gain insights into their health. But, the information collected is regionally specific and not worldwide. Thus, this may create problems in transparency, integrity, and much more. The medical details of patients collected are stored in a simple database which appears to be a centralized application that is prone to be modified or hacked by intruders and also suffers from fault tolerance. This paper aims to solve this problem using Blockchain and Data Possession methodologies. Blockchain helps to store multiple copies of data in a decentralized fashion. Thus, a secured model could be implemented using blockchain in which the patient's data can be securely sent to any medical professional, thus promoting transparency, security, and integrity. Provable Data Possession helps the users to verify that their information is intact and has not been tampered. It can be achieved using cryptography which encrypts the information of the patients stored. This paper will be beneficial for the medical department and the public such that it will be beneficial in terms of transparency, scalability, and security.

E-SDAPONIKS: A Cloud-Based Aquaponics Management System with Data Analytics

This study endeavors to create an advanced aquaponics management system, elevating monitoring capabilities through the integration of environmental sensors. By harnessing the power of the Internet of Things (IoT) and cloud-based technology, users gain unprecedented control over devices while seamlessly recording and analyzing system data. Complementing this, a user-friendly mobile application provides realtime insights into feed consumption, water parameters, and harvest schedules, enhancing operational efficiency. The Agile Methodology serves as the framework for this project, enabling a flexible and iterative approach to hardware and software development. Key hardware components include temperature and humidity sensors, electrical conductivity sensors, and pH sensors, essential for comprehensive monitoring. Facilitating seamless integration, an ESP32 microcontroller manages these sensors, boasting built-in Bluetooth and Wi-Fi modules to facilitate connectivity with the mobile application. In assessing project performance, adherence to ISO 25010 requirements and IEEE standards validates the system’s robustness and reliability.

Enhanced Home Security System using IoT

In our report, we propose an inventive domestic security framework harnessing the control of IoT innovation. This framework coordinating cameras, movement detecting, capacity, and Ethernet network to make a comprehensive surveillance arrangement. Through this seamless integration, users gain remote accessibility and control via mobile devices and computers, facilitating proactive monitoring and real-time alerting. The report dives into intricate technical details, including system architecture, firmware development, network protocols, and security measures, providing a comprehensive guide for the implementation and deployment of IoT-driven home security solution.

Blockchain-based crowdsourced deep reinforcement learning as a service

Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for solving complex problems. However, its full potential remains inaccessible to a broader audience due to its complexity, which requires expertise in training and designing DRL solutions, high computational capabilities, and sometimes access to pre-trained models. This necessitates the need for hassle-free services that increase the availability of DRL solutions to a variety of users. To enhance the accessibility to DRL services, this paper proposes a novel blockchain-based crowdsourced DRL as a Service (DRLaaS) framework. The framework provides DRL-related services to users, covering two types of tasks: DRL training and model sharing. Through crowdsourcing, users could benefit from the expertise and computational capabilities of workers to train DRL solutions. Model sharing could help users gain access to pre-trained models, shared by workers in return for incentives, which can help train new DRL solutions using methods in knowledge transfer. The DRLaaS framework is built on top of a Consortium Blockchain to enable traceable and autonomous execution. Smart Contracts are designed to manage worker and model allocation, which are stored using the InterPlanetary File System (IPFS) to ensure tamper-proof data distribution. The framework is tested on several DRL applications, proving its efficacy.

Sentiment Analysis and Topic Classification with LSTM Networks and TextRazor

In the ever-evolving landscape of social media, where user-generated content shapes digital discourse, the need for nuanced sentiment analysis and topic extraction is paramount. This paper presents a comprehensive approach utilizing advanced Natural Language Processing (NLP) techniques to enhance user experience and foster a healthier digital environment. Leveraging Long Short-Term Memory (LSTM) networks for sentiment analysis and TextRazor for topic extraction, the system provides insights into emotional tones and key themes within social media discussions. Through intuitive visualizations, users gain awareness of sentiment trends and topic distributions, empowering informed engagement. The results demonstrate high accuracy in sentiment classification with 86% and effective topic identification, contributing to the mitigation of misinformation and negativity online. This research underscores the potential of advanced NLP methods in cultivating constructive digital spaces and sets the stage for further innovation in the field.

Privacy-preserving authentication approach for vehicular networks

Vehicle AdHoc networks have an important role in intelligent transport systems that enhance safety in road usage by transmitting real traffic updates in terms of congestion and road accidents. The dynamic nature of the vehicular AdHoc networks make them susceptible to attacks because once malicious users gain access to the network they can transform traffic data. It is essential to protect the vehicular ad hoc network because any attack can cause unwanted harm, to solve this it is important to have an approach that detects malicious vehicles and not give them access to the network. The proposed approach is a privacy preserving authentication approach that authenticates vehicles before they have access to the vehicular network thereby identifying malicious vehicles. The model was executed in docker container that simulates the network in a Linux environment running Ubuntu 20.04. The model enhances privacy by assigning Pseudo IDs to authenticated vehicles and the results demonstrate effectiveness of the solution in that unlike other models it boasts faster authentication and lower computational overhead which is necessary in a vehicular network scenario.

Recommender System in E-Commerce

In the realm of e-commerce, recommendation systems play a pivotal role in guiding users towards relevant products. However, existing systems often grapple with inefficiencies in handling large datasets and fail to deliver personalized recommendations tailored to individual preferences. Addressing these challenges, the study introduces an innovative approach leveraging graph databases to enhance the performance of e- commerce recommendation systems. Through comprehensive analysis, the study delves into four critical aspects: database comparison, user exploration frequency across product categories, the diversity of available category types, and user browsing history analysis. This investigation unveils Neo4j's superior efficiency over MySQL in managing extensive datasets, laying the groundwork for more robust recommendation engines. By scrutinizing user behaviour patterns, the recommender system predicts preferences with precision, promising a tailored and gratifying shopping experience for users. Moreover, with support for a diverse array of category types, users gain flexibility in exploring products based on varied criteria, addressing a crucial need in the market for personalized shopping experiences. Leveraging insights gleaned from user browsing history, the system delivers refined recommendations, poised to elevate user satisfaction and engagement within the competitive landscape of e- commerce. In conclusion, the study highlights the significance of recommendation systems in enhancing the e- commerce experience. By leveraging graph databases, particularly Neo4j, over traditional systems like MySQL, significant improvements in managing extensive datasets are demonstrated.

Dynamic knowledge graph applications for augmented built environments through “The World Avatar”

The proliferation of digital building models in recent years has led to a corresponding rise in specialised, non-interoperable models. These models impede sustainable developments by forming data silos that hinder cross-application data exchange and knowledge discovery processes. Although Semantic Web solutions hold promise in addressing these silos, current approaches primarily focus on developing novel ontologies, yielding similar outcomes. But it is unclear how these methodologies could support broader knowledge discovery processes and application requirements. This paper addresses these research challenges by introducing a dynamic knowledge graph as implemented within The World Avatar for interoperable building models. We demonstrate its value through two distinct applications in urban energy management and laboratory automation. The dynamic knowledge graph revolves around a comprehensive structured knowledge model constructed from ontologies and agents. Ontologies semantically annotate data and represent domain knowledge and their relationships with standardised definitions. When augmented with an agent architecture, the resulting knowledge model can align stakeholder perspectives and accommodate the dynamic and scalable nature of urban data. Moreover, the dynamic knowledge graph fosters innovative human-machine interactions through visualisation interfaces to augment knowledge discovery processes in the built environment for greater efficiencies and innovation. As the knowledge model expands, users gain access to a broader spectrum of private and public data sources and technologies, while reducing integration barriers. This is especially pertinent for smaller and less influential entities like municipal and local governments with limited resources, who can realise substantial benefits at reduced costs.

Smart Onion Grading System: Integrating Temperature Control and Gas Sensing for Enhanced Quality Maintenance

Abstract: India stands as the world's second-largest producer of onions, highlighting its significant role not just as a vegetable staple but also as a key contributor to foreign exchange earnings in the realm of fruits and vegetables. However, the variability in India's climate poses a challenge, often leading to the rot or decay of onions. Consequently, preserving onions becomes imperative, and adhering to the temperature guidelines outlined by the National Onion Association (NOA) is crucial. According to NOA, onions are best stored at temperatures ranging from 0 to 4°C alongside a humidity level of 60 to 70%. To address this issue, we have developed an onion preservation system that meticulously maintains onions within the specified parameters. Our system employs temperature and humidity sensors to continuously monitor these conditions. Utilizing a Peltier module, the system regulates the air temperature within the storage tank, ensuring it remains within the recommended range. Additionally, users gain access to up-to-date market trends pertaining to onions, enhancing their decision-making capabilities.

BudgetBliss: Predictive Budget Planning using Neural Networks and Socioeconomic Factors

BudgetBliss is a transformative web application developed to revolutionize personal finance management through the integration of Artificial Neural Networks. Leveraging a sophisticated architecture, BudgetBliss provides users with a comprehensive platform to effectively plan and manage their income. The development process involved meticulous data collection and preprocessing, followed by the implementation of an Artificial Neural Network model. By analyzing users' spending habits and categorizing income, BudgetBliss offers detailed financial breakdowns tailored to individual needs. This methodology ensures the accuracy and relevance of the insights provided to users. Furthermore, BudgetBliss serves as an inadvertent educational tool, fostering financial literacy as users gain insights into their spending habits and make informed decisions about their money. Additionally, the automation of categorization, analysis of spending habits, and provision of personalized insights significantly reduce financial stress, empowering users to feel in control of their finances and focus on achieving their goals with confidence. In today's fast-paced society, where financial management significantly impacts overall well-being, BudgetBliss addresses the critical need for a system that enhances household financial control. By enabling users to make informed decisions and gain a deeper understanding of their financial landscape, BudgetBliss facilitates a path towards contentment and fulfillment in life. Keywords - Budget planning, Personal finance, Artificial Neural Networks, Financial management, Well-being

Personalized special training follow-up application Cardiofit2

Mobile applications are preferred by many people due to the convenience they provide to users and their low cost. Mobile applications, which are widely used in many fields, also appear in many different forms in the field of fitness and health. Mobile application experts, who closely follow technological developments, update these applications periodically. Thus, with new training and nutrition recommendations provided to users, users are provided with easy access to technological developments as well as developments in sports. Mobile applications also aim to create a fitness and health routine for their users in daily life with the feedback they provide based on user preferences and motivating warnings to users. The fitness and health application we have developed called "Cardiofit2" also allows users to save their personal information and access personalized exercise programs created using this information. The application is designed to enable users to easily achieve their fitness goals with weekly exercise programs in the short and long term. It also aims to provide more comprehensive support to users with features such as exercise history, daily water consumption warnings, pedometer and smart watch integration. Cardiofit2 is an improvable platform that helps users gain healthy lifestyle habits with its aesthetic design and strong technical infrastructure. The application aims to guide users in achieving their fitness goals with its user-friendly interface, personalized exercise programs and health tracking features. It supports users' transition from a sedentary lifestyle to a healthier lifestyle with regular feedback and motivational alerts.

Expense Tracker App

Abstract: The expense tracker application presents a comprehensive solution for efficient financial management and savings optimization. With a user-friendly home page showcasing transaction summaries and interactive graphs, users gain immediate insights into their spending patterns. The dedicated savings page facilitates goal-setting and progress tracking, enhancing financial discipline. Additionally, the app features an expense updating mechanism for real-time data accuracy. Future iterations will incorporate machine learning algorithms to provide personalized recommendations and predictive insights, further empowering users to make informed financial decisions. With its intuitive interface and future-proof design, the application emerges as a pivotal tool in navigating the complexities of personal finance, fostering proactive savings habits and financial well-being.

ATM Security based on Face Verification

Abstract: To provide a reliable security solution, this project focuses on creating an ATM security system based on facial recognition using OpenCV, machine learning, and deep learning. The paper examines how facial recognition technology can improve ATM security, offering a non-intrusive and highly accurate method of identity verification. By analyzing unique facial features, such as facial component sizes and shapes, this technology can authenticate users in real-time reliably. The proposed system integrates facial recognition software with existing ATM infrastructure. Users are prompted to look into a camera for facial authentication when approaching the ATM. Upon a successful match, users gain access to ATM functionalities, ensuring a seamless and secure transaction experience. This ATM security system emphasizes robust user verification before granting financial transaction access. By integrating face recognition and personal question verification, the system provides a multilayered security approach, increasing user confidence and deterring unauthorized access attempts.

IoT-BASED COMPACT SOIL MOISTURE MONITORING DEVICE

The project delineates a sophisticated smart irrigation system leveraging the principles of the Internet of Things (IoT). Central to the system is an Arduino microcontroller, serving as the control hub, orchestrating the operation of a 5V semi-submersible water pump based on real-time soil moisture levels. An integral component of the setup is a Bluetooth module, facilitating seamless connectivity between the user's mobile application and the irrigation system. Through the mobile application, users gain access to sensor data values transmitted via Bluetooth. This bidirectional communication empowers users to monitor soil moisture levels remotely, enabling informed decision-making regarding irrigation needs. The system operates autonomously by activating the water pump when soil moisture levels dip precariously low and deactivating the pump once the moisture level reaches a predefined threshold. This intelligent automation ensures efficient water usage, preventing over-irrigation while fostering optimal soil conditions for plant growth. In essence, this IoT-based smart irrigation system not only introduces automation and precision to the irrigation process but also enhances user interaction through mobile connectivity. The synergy of Arduino control, a submersible water pump, and Bluetooth communication exemplifies a user- friendly and resource-efficient approach to modernizing traditional irrigation practices.

Real-Time Web-Based Monitoring System for Temperature, Humidity, and Solar Panels in Ramie Drying Facilities

Purpose: To address the manual monitoring challenges in processing ramie fibers, especially during drying. The purpose is to create a monitoring system that oversees room temperature, humidity, and the status of solar panels, crucial factors in ramie productivity.Methods: Real-time web-based system development that monitors room temperature, humidity, and the performance of solar panels in a ramie drying room using the Internet of Things architecture ESP32 with communication through GSM SIM 800L in rural areas.Results: The system can display real-time information such as temperature data, humidity, and electrical energy parameters derived from the solar panel's utilization in the ramie drying room. By doing so, users gain efficiency and effectiveness in obtaining information, significantly enhancing ramie fiber productivity.Novelty: Integration of sensor instruments, low-power ESP32 microcontrollers, GSM Telecommunication, Solar Cell Energy as a power source, and a real-time web-based Monitoring Information System implemented in a ramie drying dome. This simplifies the monitoring process and optimizes limited resources such as space, energy, telecommunications, and human resources, which are typically constrained infrastructure in the ramie fiber agricultural system.

Barriers to and facilitators of user engagement with web-based mental health interventions in young people: a systematic review.

Many young people (YP) are diagnosed with mental illnesses and require support. Web-based mental health interventions (W-MHIs) have been increasingly utilized by YP, healthcare providers, and parents due to reasons including convenience and anonymity. W-MHIs are effective in improving mental health in YP. However, real-world engagement with W-MHIs remains low. Therefore, understanding barriers/facilitators of user engagement with W-MHIs is necessary to promote W-MHIs and help users gain optimal benefits through higher engagement. This review aims to identify barriers/facilitators of user engagement with W-MHIs in YP aged 10-24years. A systematic search of five databases for English language, peer-reviewed publications was conducted between January 2010 and February 2023. Studies examining factors influencing user engagement with W-MHIs, described as barriers or facilitators, were included. Study quality was assessed using the Mixed Methods Appraisal Tool. A narrative synthesis was performed. Of 4088 articles identified, 69 studies were included. Barriers/facilitators were reported by young people (63 studies), providers (17 studies), and parents/caregivers (8 studies). YP perceived that usefulness and connectedness were the most common facilitators, whereas low-perceived need was the most reported barrier. Both providers and parents reported that perceived usefulness for YP was the most common facilitator, whereas concerns about program effectiveness and privacy were noted as barriers. This review found that program- and individual-related factors were important determinants of engagement with W-MHIs. This review provides guidance on the future design and development of new interventions, narrowing the gap between existing W-MHIs and unmet needs of users.

Patient-Reported Outcomes After Vestibular Implantation for Bilateral Vestibular Hypofunction

Standard-of-care treatment proves inadequate for many patients with bilateral vestibular hypofunction (BVH). Vestibular implantation is an emerging alternative. To examine patient-reported outcomes from prosthetic vestibular stimulation. The Multichannel Vestibular Implant (MVI) Early Feasibility Study is an ongoing prospective, nonrandomized, single-group, single-center cohort study conducted at Johns Hopkins Hospital that has been active since 2016 in which participants serve as their own controls. The study includes adults with severe or profound adult-onset BVH for at least 1 year and inadequate compensation despite standard-of-care treatment. As of March 2023, 12 candidates completed the eligibility screening process. The MVI system electrically stimulates semicircular canal branches of the vestibular nerve to convey head rotation. Patient-reported outcome instruments assessing dizziness (Dizziness Handicap Inventory [DHI]) and vestibular-related disability (Vestibular Disorders-Activities of Daily Living [VADL]). Health-related quality of life (HRQOL) assessed using the Short Form-36 Utility (SF36U) and Health Utilities Index Mark 3 (HUI3), from which quality-adjusted life-years were computed. Ten individuals (5 female [50%]; mean [SD] age, 58.5 [5.0] years; range, 51-66 years) underwent unilateral implantation. A control group of 10 trial applicants (5 female [50%]; mean [SD] age, 55.1 [8.5] years; range, 42-73 years) completed 6-month follow-up surveys after the initial application. After 0.5 years of continuous MVI use, a pooled mean (95% CI) of within-participant changes showed improvements in dizziness (DHI, -36; 95% CI, -55 to -18), vestibular disability (VADL, -1.7; 95% CI, -2.6 to -0.7), and HRQOL by SF36U (0.12; 95% CI, 0.07-0.17) but not HUI3 (0.02; 95% CI, -0.22 to 0.27). Improvements exceeded minimally important differences in the direction of benefit (exceeding 18, 0.65, and 0.03, respectively, for DHI, VADL, and SF36U). The control group reported no mean change in dizziness (DHI, -4; 95% CI, -10 to 2), vestibular disability (VADL, 0.1; 95% CI, -0.9 to 1.1) or HRQOL per SF36U (0; 95% CI, -0.06 to 0.05) but an increase in HRQOL per HUI3 (0.10; 95% CI, 0.04-0.16). Lifetime HRQOL gain for MVI users was estimated to be 1.7 quality-adjusted life-years (95% CI, 0.6-2.8) using SF36U and 1.4 (95% CI, -1.2 to 4.0) using HUI3. This cohort study found that vestibular implant recipients report vestibular symptom improvements not reported by a control group. These patient-reported benefits support the use of vestibular implantation as a treatment for bilateral vestibular hypofunction.

Customer awareness and cyber security in the Organisation for Economic Co-operation and Development countries

In certain circumstances, millions of documents have been exposed due to an increase in the yearly incidence of cyber security breaches in recent years. In the context of the banking industry’s digital transition in the Organisation for Economic Co-operation and Development (OECD), this study investigates consumer knowledge of and satisfaction with cyber security. The study is empirical and based on the data obtained from 240 banking clients in OECD. Cyber attacks, phishing, and hacking have been examined from diverse angles. The effects of cyber attacks, phishing, hacking, cyber security help, and expectations on cyber security’s technical awareness are investigated using analysis of variance (ANOVA) and bivariate regression analysis. The findings demonstrate how the banking industry has benefited from digital change, and users gain from online services. Nonetheless, a customer’s degree of awareness regarding hacking, phishing, and cyber attacks will have an impact on how satisfied they are with digital transactions. The findings also showed that banks should regularly offer training programs to protect their clients from cyber attacks and that customers need more assurance from banks about security-related issues. Banks might easily meet their long-term sustainability goals if they implemented better safer cyber security management. This paper has repercussions for policymakers, investors, and business organizations. Importantly, our study reveals how customer awareness and cyber security are related in OECD.

Stackelberg game theory based model to guide users’ energy use behavior, with the consideration of flexible resources and consumer psychology, for an integrated energy system

Users' energy use behavior has great impact on the economic and efficient operation of integrated energy systems (IES). This paper proposes energy usage strategies based on integrated demand response (IDR) by developing a Stackelberg game based simulation model, in which the user-side flexible resources (FRs) and comfort cost are considered. In the paper, a framework of community integrated energy system (CIES) and an IDR model considering the consumer psychology were established first, in which the community users were viewed as a user aggregator (UA). A Stackelberg game simulation model was then developed for studying the energy interactions between community integrated energy service provider (CIESP) as the leader and UA as the follower based on the proposed IDR model. The benefit gain and energy use behavior of the UA were analyzed, by using the model developed, in six scenarios. The study found the strategy developed with the consideration of FRs and consumer psychology could make the UA's cost of purchasing energy reduced by 17.14 %. Meanwhile, the consumer surplus could increase by 11.58 %. The results also showed that by introducing user-side gas boilers and distributed energy storage more benefit gain for users and improved load regulation capabilities for CIES could be achieved.

UTILIZATION OF SOCIAL MEDIA AS A QUARTER LIFE CRISIS EDUCATION MEDIA

Introduction : Quarter life crisis is an individual's response to moving towards the reality of life in which there is instability, continuous change, various choices and the emergence of panic due to feeling helpless. Life changes like this are accompanied by the emergence of various kinds of emotional reactions such as anxiety, panic, stress, and confusion about goals, as well as doubts about one's own abilities and fear of failure. Methods: in utilizing social media, using qualitative methods, which include collecting data by interviews and delivering material. Purpose: to take an approach involving visual narratives, interviews, and practical solutions in providing a comprehensive view of the quarter life crisis and providing positive value in using social media and having a good impact on oneself. Results: The author's video has its own interest among Instagram users, as evidenced by the author's video views reaching 3,753 and video likes reaching 89 likes. This shows that Instagram users gain knowledge about the quarter life crisis. Conclusion: Currently there are still students who experience changes in emotional reactions such as anxiety, panic, stress, and confusion and doubt about their own abilities. Video about the quarter life crisis by conducting interviews with students to get various perspectives on the quarter life crisis and strategies for dealing with it. The video content will be disseminated via social media such as Instagram.