New possibilities for fog-based vehicle monitoring have emerged with the expansion of fog computing, but present privacy concerns provide a significant barrier that limits the extent to which vehicles can participate. Because of its potential to improve road network security and vehicle productivity, the field of Vehicle-based Ad hoc Networks (VANETs) is gaining prominence. The security problems with VANETs, such as data confidentiality and message access control still need better solutions to provide better Quality of Service (QoS). The effectiveness of VANET networks is diminished due to their instability problem. Vehicles continually add requests to the Road Side Unit (RSU) queue whenever they want specific information. For ever-evolving networks like VANETs, better routing is a continual process. Fundamental problems arise in large-scale systems when centralized procedures are used to assign jobs to the nodes along a route. The present centralized system for computing and safety has many needs, including the protection of data storage, user authentication, access control, system availability across multiple network connections, and the provision of a real-time data flow overview. Distributed problem solving and work sharing between multiple agents can improve the system's scalability. This paper provides a brief survey on the secured outsourcing and privacy preservation-based traffic monitoring model with routing and task scheduling models in Fog-enabled VANETS. This survey presents the limitations of the traditional models that help the researchers design new solutions for secure outsourcing in VANETs.

EPVM: An efficient privacy-preserving palm vein model for user authentication

To enhance the scientific rigor and user alignment of children’s furniture design, this study proposes a data-driven multi-criteria evaluation framework for design optimization. Focusing on wooden seating products for children, online product reviews were collected and preprocessed using Python and the Jieba word segmentation tool to extract authentic user needs. An evaluation index system was established by filtering indicators through expert focus group discussions and the coefficient of variation method. During the weighting phase, subjective weights were derived using an improved Analytic Hierarchy Process (AHP), while objective weights were calculated via the CRITIC method. A game-theoretic approach was employed to integrate both into a composite weight vector. Finally, the TOPSIS–RSR model was applied to rank and classify the performance levels of four wooden children’s seating designs. Based on the results, specific design guidance strategies were proposed. The proposed framework effectively captures user requirements, balances subjective and objective information, and provides a clear decision-making pathway for selecting optimal design solutions. The study not only advances theoretical research but also offers practical guidance for design, with strong potential for extension to other furniture categories and resource-driven product design domains.

The Employee Management System, or EMS, it's this web app built on a three-tier setup. React.js handles the frontend stuff. Node.js with Express.js runs the backend. MySQL keeps the database going. All that makes it scalable and keeps data secure, you know. Basically, it tackles workforce headaches like managing employee lifecycles, assigning projects, and sorting admin tasks. Helps boost efficiency around the office. It breaks down into four main modules that tie together. First, user authentication and role management. Then employee info handling. Attendance and leave tracking comes next. Project management with real-time updates rounds it out. Role-based controls mean admins get their dashboard. Managers have theirs. Employees see what fits their job. No one poking around where they shouldn't. EMS automates a bunch of things. Attendance gets tracked without hassle. Leaves get approved quicker. Projects stay monitored. Even payments sort themselves out. Cuts down errors. Lightens the manual load a lot. Performance wise, it speeds up admin work. Routine tasks process faster. Data stays more accurate than old-school methods. Plus, it pushes transparency. Encourages team collaboration. Keeps operations secure. Really useful for companies shifting to digital ways. As a platform, it's scalable. User-friendly too. Ready for whatever comes next. Evolves as workplaces change. Ensures data stays safe. Operations remain clear. Adapts to HRM challenges that keep growing.

Abstract - The Online Exam Portal is a comprehensive web-based application developed using the CodeIgniter framework, designed to streamline and automate the entire process of conducting examinations. Traditional examination methods often involve significant manual effort in scheduling, administering, and evaluating tests, which can be time-consuming, error-prone, and lack transparency. This system addresses these challenges by providing a secure platform with multiple functionalities, including user authentication, role-based access control, exam scheduling, question management, automated evaluation, and result generation. Additionally, it supports diverse question formats such as multiple-choice, descriptive, and true/false questions, enabling flexible assessment strategies. By automating routine administrative tasks, the system enhances efficiency, minimizes human errors, and ensures fairness and transparency in the evaluation process. The report elaborates on the motivation behind developing the system, a detailed literature survey, the overall system design, implementation methodologies, experimental results validating system performance, and potential directions for future enhancement, such as integrating AI-based assessment and advanced analytics. This project not only serves educational institutions but can also be adapted for corporate training and certification programs. Keywords: Online Examination, Web-based Application, CodeIgniter, Automated Evaluation, Exam Scheduling, Question Management, Result Generation, User Authentication, E-learning, Educational Technology.

The Skin Cancer Detection System shows the whole process and web app features are made in this study. Security is kept strong with login and registration pages that include password rules and user authentication. Once logged in, where users can see the main screen and upload a dermoscopic image or take a live photo with their camera for quick skin cancer prediction. Both uploaded and captured images are processed by a deep learning system that is built with Flask. This system identifies the skin lesion and classifies it as either melanoma or benign. It also creates visual explanations called Grad-CAMs, which highlight the parts of the image that helped the model make its decision, making the results easier to understand. The system has a real-time mode that analyzes video frames on the fly to detect any unusual changes. It also keeps user data private by storing everything locally. The results screen shows the predicted category, confidence level, and the area of the lesion, making it simple for both medical experts and regular users to understand the diagnosis. In short, this system is an accurate, easy-to-use, and ethical AI tool that helps detect skin cancer at an early stage.

In today's landscape, the widespread adoption of cloud computing has been accompanied by a corresponding increase in security vulnerabilities, with Distributed Denial-of-Service (DDoS) attacks posing one of the most serious challenges by overwhelming resources such as CPU power, memory, and network bandwidth, thereby disrupting services for legitimate users. Detecting DDoS attacks in cloud environments is particularly difficult due to the similarity between malicious and legitimate traffic, often originating from numerous geographically dispersed sources. This study evaluates the effectiveness of five supervised machine learning algorithms Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), and Naïve Bayes (NB) for detecting DDoS attacks in cloud computing environments using the publicly available Software Defined Networking (SDN) DDoS Attack Dataset. Comprehensive preprocessing including normalization, feature selection, and Synthetic Minority Oversampling Technique (SMOTE) was applied, along with rigorous regularization strategies to mitigate overfitting. Experimental results demonstrate that Random Forest achieved the highest balanced performance (95% accuracy, 96% precision, 95% recall), followed by KNN (94%), SVM (93%), DT (92%), and Naïve Bayes (91%). These findings confirm the potential of machine learning for reliable DDoS detection while emphasizing the importance of proper model regularization to ensure generalizability. Future work should explore larger datasets, real-time traffic analysis, and hybrid models to further enhance robustness.

Purpose: The primary objective of this systematic review is to evaluate the effectiveness of Deep Learning (DL) architectures in detecting "cold-start" fake accounts on Instagram newly created profiles that lack sufficient historical data for traditional detection. Methodology: The methodology focused on five core DL frameworks Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Generative Adversarial Networks (GANs), and Autoencoders evaluating their ability to process non-textual features, metadata, and early-stage behavioral patterns. Findings: The findings reveal that hybrid models, specifically those combining GANs for data augmentation with LSTMs for sequence analysis, achieve the highest detection accuracy of up to 96.4% for cold-start profiles. However, a significant transparency-accuracy trade-off persists, as ensemble methods often lack the interpretability required for platform-wide implementation and struggle to distinguish between "quiet" legitimate users and sophisticated "human-impersonating" bots during the critical first 48 hours of account activity. Unique Contribution to Theory, Policy and Practice: This study contributes to theory by introducing an integrated framework for "digital identity evolution" that moves beyond static feature analysis toward dynamic, behavior-based detection. In practice, it provides platform developers with a technical roadmap for implementing hybrid-optimization models, such as combining DL with bio-inspired algorithms like GWO and PSO, to reduce false-positive rates. Finally, for policy, the research offers evidence-based recommendations for regulatory frameworks regarding social media transparency, asserting that early detection is essential to mitigate the $1.3 billion annual economic loss caused by influencer fraud and advertising waste.

The rapid growth of mobile technology has significantly transformed the healthcare industry by enabling convenient and on- demand access to medical services through smartphones. Mobile health applications have reduced dependency on traditional hospital visits and improved accessibility to healthcare resources. This shift has enhanced patient engagement and streamlined healthcare service delivery by integrating essential medical functions into a single digital platform. This project focuses on the development of a comprehensive Android application designed to improve healthcare delivery by facilitating doctor appointment booking, medicine ordering, healthcare package exploration, and consultation with top doctors in the user’s locality. The application is developed using Java in Android Studio with a Firebase backend to support real-time data management, secure user authentication, and reliable cloud-based data storage. These technologies ensure scalability, fast data synchronization, and secure handling of sensitive medical information. The application also integrates a secure payment gateway to enable hassle-free transactions for medicine purchases, appointment fees, and other medical services. The authentication system ensures secure session management, while cloud storage and real-time databases manage critical functionalities such as storing prescriptions, tracking medicine orders, and updating doctor availability. Overall, the proposed healthcare application provides an all-in-one solution for managing health needs through smartphones, offering improved accessibility, enhanced user experience, and efficient connectivity between patients and healthcare providers. Furthermore, the system ensures data security and reliability through robust authentication and encrypted storage mechanisms. Its modular architecture supports future enhancements such as telemedicine and advanced health analytics. By leveraging cloud technologies and a user- centric design, the application enhances healthcare efficiency and service quality.

Context. The current task of evaluating formant data (formant frequencies, their spectral density level, amplitude-frequency spectrum envelope, formant frequency spectrum width) in voice authentication systems is considered. The object of the study is the process of digital preprocessing of the voice signal when extracting formant data.Objective. Evaluation of the effectiveness of traditional procedures for digital preprocessing of a user voice signal and development of proposals for improving the quality of formant data extraction.Method. A mathematical model for extracting formant data from an experimental voice signal has been developed to study the influence of normalization and segmentation procedures on the quality of the resulting estimates. By modeling the process of extracting formant data, the results of digital processing of normalized and non-normalized voice signals are compared. The influence of the processed frame duration of the experimental voice signal on the quality of the formant frequencies assessment is estimated. The results are obtained for the experimental phoneme and morpheme.Results. The obtained results show that when processing a voice signal with a sufficient signal-to-noise ratio, normalization procedures are not mandatory when extracting formant data. Moreover, normalization leads to a less accurate measurement of the spectrum width of formant frequencies. It is also unacceptable to use a processed frame duration of less than 40 ms. These results allow us to modify the traditional method of voice signal preprocessing. The use of the modeling method in the study of the experimental voice signal confirms the reliability of the results obtained.Conclusions. The scientific novelty of the research results lies in the modification of the voice signal preprocessing methodology in authentication systems. Eliminating normalization procedures at high signal-to-noise ratios of the voice signal, which occurs in user authentication systems, makes it possible to increase the speed of formant data extraction and more accurately estimate the width of the formant frequency spectrum. Selecting a frame duration of at least 40 ms for the processed signal significantly improves the accuracy of formant frequency determination. Otherwise, the estimates of the formant frequencies will be high. Moreover, when processing phonemes, the processed voice signal cannot be divided into frames. Practical application of research results allows to increase the efficiency and accuracy of the formant data generation. Prospects for further research may be studies of the influence of normalization and framing procedures on other elements of a template of the authentication system user.

Typically, library budgeting uses incremental funding increases that tend to simply evolve budgets from the previous year without consideration of evolving patron needs, technology changes, or programmatic needs of equity and access. This article looks backwards on the construct of incremental budgeting and forward to the possibility of using Zero Based Budgeting (ZBB) as a prospective planning approach for public and academic libraries. Zero-based budgeting requires departments to begin from scratch with each budget period, requiring each proposed expense to be justified in relation to the institution’s strategic priorities, demonstrate value delivery in services, and model measurable impacts. The research examines the practice of zero-based budgeting in higher education libraries with a review of several controlled simulations and by observation of institutional examples from different universities. The result of the analysis yielded four additional benefits- more transparency of the budget process; termination of unneeded budget lines; enhanced correlation between budget lines and legitimate user needs; and accelerated institutional response to unanticipated mid-year changes in budgeting. The modifications to the zero-based budget plan prototype provide discrete cost centres with a footprint around an institution's mission, uses multi-dimensional criteria to justify each expense, and adapts user-impact scoring techniques that align with, and in every case reside in the original strategic plans of the institution. Interviews with key budget stakeholders and electronically administered surveys provide qualitative perspectives that suggest considerable increases in confidence about budget sustainability, a stronger culture of stewardship, and broader participation from an increasingly diverse pool of constituents in deliberative budget discussions. Together, these perspectives re-frame zero-based budgeting as more than just a tool for controlling costs; they frame it as a forward-looking framework that distributes services equitably, manages pilot projects, and ensures the sustainability of library service in the long term. This emphasizes a new forward orientation of adaptive budget processes that can respond quickly to emerging community priorities, an ability that is grounded in empirical proof-of-concept and co-developed by a robust, continuously engaged, diverse network of stakeholders. Here, ZBB is a reasonable, future-proof strategy that can allow libraries to work within tight budgetary constraints, while continuing to develop and deliver services that have a meaningful, quantifiable social impact.

Abstract Extended Reality (XR) technologies are becoming integral to daily life. However, password-based authentication in XR disrupts immersion due to poor usability, as entering credentials with XR controllers is cumbersome and error-prone. This leads users to choose weaker passwords, compromising security. To improve both usability and security, we introduce a multimodal biometric authentication system that combines eye movements and brainwave patterns using consumer-grade sensors that can be integrated into XR devices. Our prototype, developed and evaluated with 30 participants, achieves an Equal Error Rate (EER) of 0.298%, outperforming eye movement (1.820%) and brainwave (4.920%) modalities alone, as well as state-of-the-art biometric alternatives (EERs between 2.5% and 7%). Furthermore, this system enables seamless authentication through visual stimuli without complex interaction.

The QUIC transport protocol introduces a paradigm shift in session persistence through the use of Connection Identifiers (CIDs), decoupling connections from the traditional network 4-tuple. While this enables seamless connection migration, it introduces a critical dependency on the state-tracking capabilities of intermediate middleboxes and Load Balancers (LBs). This paper introduces QUICsand, a novel attack vector that leverages CID-induced state exhaustion. By "drowning" the LB's mapping table with high-entropy, orphaned CIDs, an attacker can force the infrastructure into an "Ambiguity State." In this state, the LB reverts to deterministic hashing, allowing an attacker to predict and collide with legitimate user traffic. We present a Proof-of-Concept (PoC) demonstrating a session takeover success rate of $12.2% in simulated high-traffic environments.

Cloud-Based Print Management Systems represent a paradigm shift of the old style of on-shelf print infrastructures to an innovative, scalable, secure, and cost-effective print systems hosted by the cloud. These systems use cloud computing to print jobs, user authentication, and device communication without any complications in various platforms and network environments. The architecture is usually made up of the following core components which are: print servers, connectors, clients, and end-user devices and they are built using standardized workflows and APIs to guarantee that they will be interoperable with enterprise systems and networks. Some of the main features are secure print release system, highly developed authentication, smart routing of print jobs, mobile device-ability, which guarantees flexibility and access by distributed workforces. Centralized control and compliance monitoring help organizations to save on capital expenditure, streamline maintenance, increase scalability levels, and ensure better security. It is still facing challenges with data privacy, network latency and interoperability with legacy systems though, and it requires a strong encryption solution, redundancy plans and deployment choices on a hybrid basis. The comparative analysis of the top known solutions, namely, Microsoft Universal Print, Papercut, and PrinterLogic, reveals three different degrees of scalability, integration with the main enterprise, and compliance preparedness after Google Cloud Print was discontinued.

In personal computers, data is input through devices such as keyboards and mice, and various services are received from the internet. To provide these online services, secure user authentication methods are essential. Knowledge-based authentication methods, such as PINs or passwords, have been widely implemented in most services due to their ease of implementation. However, security threats such as brute-force attacks, phishing attacks, and keyboard data attacks that intercept sensitive user information have emerged. To counter these security threats, image-based authentication methods using mouse input were introduced. However, vulnerabilities arose when functions like GetCursorPos() or WM_INPUT messages were used, allowing mouse input data to be intercepted, thereby undermining image-based authentication. To defend against these attacks, counter-defence methods were developed to generate fake mouse data, protecting actual mouse data. With the advent of these defence methods, there has been a demand for attack methods to classify fake and real mouse data. Recently, machine learning-based methods have been employed on the attacker’s side to classify real mouse data, effectively distinguishing fake from real mouse data and compromising the security of image-based authentication methods. Therefore, this paper proposes a defence technology to safely protect mouse data from theft attacks using machine learning, specifically leveraging Generative Adversarial Networks (GANs). To achieve the goal of this defence technology, the distribution of fake mouse data generated using GANs was analyzed, verifying the feasibility of mouse defence methods. In summary, a system incorporating the defence technology was constructed, and a dataset containing both fake and real mouse data was created. Based on the constructed environment, the performance of the mouse data defence technology was evaluated. The results showed that it reduced performance by up to 37% in the dataset with the highest performance of existing machine learning-based attack methods. This study concludes that the proposed mouse data defence technology effectively addresses vulnerabilities and security threats related to user authentication information in various services relying on image-based authentication methods.

As public interest in a healthy lifestyle increases, the need for an efficient and integrated fitness center management system is increasing. At Salma Fitness Wonogiri, the process of setting schedules and booking classes is still carried out manually through short messages or direct confirmation, so it often causes various problems, such as inaccurate attendance data, the number of participants that exceed class capacity, delays in submitting schedule information, and recording cash payments that are less efficient and prone to errors. This research aims to design and develop a mobile application for a fitness class schedule booking system that is integrated with Flutter-based QRIS digital payments. This application provides user authentication features, real-time class schedule information, class bookings, and non-cash payments through QRIS. Firebase is used as a backend for user authentication and storage of schedule, transaction, and booking history data. The development method applied is Agile Development, so it allows the development of features gradually according to user needs. Initial test results show that the app is able to speed up the booking process, minimize data entry errors, improve the accuracy of the number of class participants, and make it easier to manage payments and transaction reports. Thus, this application is expected to increase operational effectiveness and service quality at Salma Fitness Wonogiri.

The exponential growth of Internet infrastructure and the widespread adoption of smart sensing devices have empowered industrial personnel to conduct remote, real-time data analysis within the Industrial Internet of Things (IIoT) framework. However, transmitting this real-time data over public channels raises significant security and privacy concerns. To prevent unauthorized access, user authentication mechanisms are crucial in the IIoT environment. To mitigate security vulnerabilities within IIoT environments, a novel user authentication and key agreement protocol is proposed. The protocol is designed to restrict service access exclusively to authorized users of designated smart sensing devices. By incorporating cryptographic hash functions, chaotic maps, Physical Unclonable Functions (PUFs), and fuzzy extractors, the protocol enhances security and functional integrity. PUFs provide robust protection against tampering and cloning, while fuzzy extractors facilitate secure biometric verification through the integration of smart cards, passwords, and personal biometrics. Moreover, the protocol accommodates dynamic device enrollment, password and biometric updates, and smart card revocation. A rigorous formal security analysis employing the Real-or-Random (ROR) model was conducted to validate session key security. Complementary informal security analysis was performed to assess resistance to a broad spectrum of attacks. Comparative performance evaluations unequivocally demonstrate the protocol’s superior efficiency and security in comparison to existing benchmarks.

The tourism industry has experienced remarkable growth in virtual tours over the past few years. Despite this expansion, limited research has explored how online authentic experiences influence tourists’ motivation, satisfaction, and behavioral intention. This study investigates online experience engagement by focusing on perceived authenticity. Using Airbnb Online Experience as a case of online authentic experiences, and adopting the theory of consumption values, it examines six types of consumption value as precursors to online authentic experiences and users’ intention to continue engagement, and analyzes their effects. The findings indicate that five of the six consumption values, with the exception of enjoyment, significantly predict both online authentic experiences and the intention to reengage. This study provides both theoretical insights and practical implications for the design of online experience.

GenWise is a cross-platform intelligent mobile suite designed to unify diverse AI-driven utilities into a single, cohesive application experience. Developed in Flutter for multi-device compatibility, GenWise integrates Google Generative Language Models (Gemini) for natural-language reasoning, understanding, and text generation; Vertex AI Imagen 2 for prompt-based visual synthesis; and Firebase for secure user authentication alongside lightweight cloud data persistence. The system is targeted toward learners, educators, content creators, and software developers who often rely on fragmented AI tools that lack interoperability and consistent workflows. GenWise consolidates these capabilities into modular, extensible components such as Chat-with-PDF for document question answering, Code Explainer for static code understanding, AI UI Designer for rapid prototyping of interfaces into structured HTML/CSS, and Communication Practice using speech-to-text and text-to-speech for conversational skill development. Additional creative and productivity features—such as Resume Builder, Question Paper Generator, Knowledge Duel, Image-to-Story transformation, Poster Generator, Lyrics/Recipe assistants, and image compression/upscaling—demonstrate the system’s breadth. This paper presents the architecture, design decisions, and implementation strategies that enable GenWise to function as a unified AI toolkit. We describe the chunking and retrieval mechanisms used for document Q&A, the conversational pipeline for speech-based interactions, and the modular plugin-style design pattern that supports rapid tool integration. Security considerations, performance optimizations, and limitations—such as dependency on cloud inference and variability in generative outputs—are discussed. Finally, we outline future directions including vector-store-based retrieval augmentation, partial offline inference via ONNX Runtime, advanced analytics, and adaptive personalization. GenWise illustrates how emerging AI capabilities can be orchestrated into a stable, production-ready platform suitable for educational, creative, and productivity-oriented tasks.

Time management and self-organization are critical skills for students, yet many struggle with procrastination, lack of focus, and ineffective planning. The objective of this project is to design and develop a Learning Behavior and Analysis for Users that helps students organize their tasks effectively while keeping them motivated to achieve academic goals. The proposed system is implemented as a cross-platform mobile application using Flutter and Firebase. It enables secure user authentication, task scheduling with reminders, and cloud-based data storage. To improve user engagement, a gamification module has been integrated, which provides rewards, badges, and progress tracking features that encourage consistent task completion. The system design follows modular principles, incorporating functional requirements such as task creation, reminders, and notifications, along with non-functional requirements such as usability, security, and scalability. Testing was carried out at multiple levels—unit, integration, and usability—to ensure reliability and performance. The results indicate that the system not only improves task completion rates but also enhances user motivation and satisfaction compared to existing task management tools. Future enhancements may include AI-based personalized suggestions and integration with wearable devices for real-time productivity tracking.