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SSN and PII Information Inside Logs

The paper deals with the threat of SSN and PII disclosure within log systems, especially those monitoring tools employed to improve customer experience. This paper studies the problems of SSN and PII disclosure, including but not limited to the chance of identity theft, financial fraud, and negative reputation to institutions. It offers a holistic plan covering data analysis, coding changes, continuous monitoring, and keeping a good relationship with stakeholders. Organizations will reinforce their data security posture through robust solutions and proactive measures, follow all needed regulations, and develop trust with their customers and stakeholders in a rapidly growing, data-driven environment.

Effects of Forest Logging Systems on the River Flow Regime Indices Using Graphical Techniques: A Case Study in a Small Natural Forest

This study aims to investigate the impact of forest exploitation methods on monthly discharge and hydrological indices of river flow using graphical methods in a forested watershed in North Iran. To achieve this, 10 hydrological index changes related to river flow regime influenced by the Shelterwood/clear cutting, Femel cutting, and the Near Nature approach forest cutting methods were assessed. According to the results, it can be stated that the Shelterwood/clear cutting method influenced monthly flow indices by increasing the coefficient of variations and intensifying runoff production, while the Femel cutting and the Near Nature approach methods contributed to regulating the flow regime and sustaining river flow. The influence of various tree-cutting techniques on river flow values and fluctuations is more evident during the wettest months compared to low-water months. The period of Shelterwood/clear cutting disrupted the natural correlation between precipitation and runoff production. Furthermore, the shift from Shelterwood/clear cutting to Femel cutting and the Near Nature approach progressively diminished the slope of the curve, indicating a reduction in monthly runoff at both measurement stations. In conclusion, opting for an appropriate method, such as the Near Nature approach, is preferable from both ecological and hydrological perspectives when managing forest areas in the study region and similar conditions involving comparable topography, climate, soil, and forest stands. The index-based coupled with graphical methodology employed appropriately demonstrates the influence of logging techniques on monthly flow patterns, which provides valuable insights into evaluating the repercussions of alternative management interventions on river flow dynamics.

Impact of salvage logging on short-term natural regeneration in montane forests of the Alps after large windthrow events

Wind disturbances are one of the main drivers of forest dynamics in Europe, shaping forest stands and modifying the ecosystem services provisioning. Salvage logging is often most common strategy adopted after a high-severity disturbance in managed stands. Understanding natural regeneration dynamics including their interaction with the logging operations, is crucial to understand how forests will be changing under a climate with increasing variability and to design adequate adaptive post-disturbance management strategies. In this study, we focused on 148 stands damaged by storm Vaia (2018). The aim was to analyze natural regeneration dynamics under different logging systems and to investigate influences of site characteristics and disturbance legacies on sapling growth and seedling emergence. The sampling protocol consisted of one transect per stand, perpendicular to one of the intact forest edges, and with a length of 80 m. Along the transect, we collected soil cover, natural seedling and sapling stem density, and deadwood quantity in four sample plots of 3 m radius each at distances of 0, 20, 40, and 80 meters from the edge (592 plots in total). Regeneration species composition was mainly driven by previous stand composition, with some exceptions depending on seed dispersal strategy. Distance from the edge significantly influenced seedlings and saplings occurrence in large gaps and affected the browsing damage percentage, together with deadwood presence. According to GLM’s models, distance from the edge, elevation, and logging methods influenced seedling establishment. At the same time, species characteristics, edge structure, deadwood and logging damages significantly influenced pre-storm seedlings and saplings presence and health. In conclusion site factors, disturbance legacies, and logging strategies are key points to consider in post-disturbance management for a fast forest recovery.

Mathematical Frameworks for Threat Intelligence Analysis: Leveraging Graph Theory and Machine Learning for Cyber Threat Assessment

Because there are so many complex online risks, we need new ways to look at threat data. This study suggests a complete approach that combines graph theory and machine learning methods to make figuring out cyber threats better. The basic idea behind networks is graph theory, which lets us show the complicated connections between different things in a connected world. This approach gives a full picture of the danger scene by representing cyber entities and how they interact as nodes and lines in a graph. This makes it easier to spot trends and outliers. The system includes machine learning techniques that make use of the huge amount of data that is available for analyzing cyber threats. Supervised learning methods are used for classification tasks. These let threats be put into groups based on past data and known patterns of bad behavior. Unsupervised learning methods, on the other hand, make finding anomalies easier by noticing changes in how networks normally behave. These machine learning models learn to adapt to changing threats by being trained and improved over and over again. This makes methods for finding threats and stopping them more effective. Combining graph theory and machine learning makes it possible to get useful information from a huge number of different data sources. Graph-based analytics bring together different kinds of data, like network traffic, system logs, and threat intelligence feeds, into a single view. This helps you see the connections between things that don't seem to be related. Machine learning algorithms improve this analysis by finding small patterns and trends that point to bad behavior. This gives cybersecurity professionals the power to stop new threats before they happen. Scalability and freedom are built into the suggested system so it can adapt to changing cyber dangers and network platforms. It can handle big datasets and real-time streaming data well by using distributed computer structures and flexible machine learning methods. This makes sure that threats are found and dealt with quickly. Putting graph theory and machine learning together is a good way to make threat intelligence research better in defense.

Semantic Hierarchical Classification Applied to Anomaly Detection Using System Logs with a BERT Model

Semantic Hierarchical Classification Applied to Anomaly Detection Using System Logs with a BERT Model

Research on techniques for detecting brute-force attacks on corporate email

Conventional brute-force attacks can now be detected and identified based on statistical analysis of logs and traffic data. However, they fail to detect low-frequency and distributed brute-force attack behaviors. To address different attack methods, new detection techniques have emerged. This study compares various machine learning algorithms and selects two methods, namely the clustering algorithm k-means and bdscan, as well as the decision tree algorithm for data learning. In one approach, normal user login data is integrated with enterprise email log data. The data is first statistically analyzed and filtered, followed by quantifying data characteristics using information entropy. Subsequently, machine learning algorithms are employed for classification, and the results are visualized for display. In another approach, labeled raw data is used to train a model using the decision tree algorithm. By comparing the two analysis results, a more accurate model can be obtained. These analytical methods can help enterprises strengthen email security and defend against low-frequency and distributed brute-force attacks.

Development and Evaluation of an Arduino-Based Data Logging System Integrated with Microsoft Excel for Monitoring On-Grid Photovoltaic Systems

This paper presents the development and evaluation of an Arduino-based data logging system integrated with Microsoft Excel for monitoring on-grid photovoltaic (PV) systems. The system combines open-source hardware and software to enable real-time data acquisition, logging, and analysis of key performance metrics such as solar irradiance, temperature, voltage, and current levels. Leveraging the versatility of Arduino microcontrollers and the accessibility ofMicrosoft Excel, the proposed system offers a cost-effective and user-friendly solution for PV system monitoring. The integration of the MS Data Streamer add-in for Excel facilitates seamless data logging and visualization, empowering PV system owners, researchers, and practitioners with actionable insights for optimizing system performance and contributing to a sustainable energy future. Experimental validation of the system demonstrates its effectiveness in accurately measuring and logging sensor data, highlighting its potential for widespread adoption in renewable energy monitoring applications.

Telehealth With Comprehensive Live-Fed Real-World Data as a Patient Care Platform for Lung Cancer: Implementation and Evaluation Study.

Telehealth has emerged as a popular channel for providing outpatient services in many countries. However, the majority of telehealth systems focus on operational functions and offer only a sectional patient journey at most. Experiences with incorporating longitudinal real-world medical record data into telehealth are valuable but have not been widely shared. The feasibility and usability of such a telehealth platform, with comprehensive, real-world data via a live feed, for cancer patient care are yet to be studied. The primary purpose of this study is to understand the feasibility and usability of cancer patient care using a telehealth platform with longitudinal, real-world data via a live feed as a supplement to hospital electronic medical record systems specifically from physician's perspective. A telehealth platform was constructed and launched for both physicians and patients. Real-world data were collected and curated using a comprehensive data model. Physician activities on the platform were recorded as system logs and analyzed. In February 2023, a survey was conducted among the platform's registered physicians to assess the specific areas of patient care and to quantify their before and after experiences, including the number of patients managed, time spent, dropout rate, visit rate, and follow-up data. Descriptive and inferential statistical analyses were performed on the data sets. Over a period of 15 months, 16,035 unique users (13,888 patients, 1539 friends and family members, and 174 physician groups with 608 individuals) registered on the platform. More than 382,000 messages including text, reminders, and pictures were generated by physicians when communicating with patients. The survey was completed by 78 group leaders (45% of the 174 physician groups). Of the participants, 84% (65.6/78; SD 8.7) reported a positive experience, with efficient communication, remote supervision, quicker response to questions, adverse event prevention, more complete follow-up data, patient risk reduction, cross-organization collaboration, and a reduction in in-person visits. The majority of the participants (59/78, 76% to 76/78, 97.4%) estimated improvements in time spent, number of patients managed, the drop-off rate, and access to medical history, with the average ranging from 57% to 105%. When compared with prior platforms, responses from physicians indicated better experiences in terms of time spent, the drop-off rate, and medical history, while the number of patients managed did not significantly change. This study suggests that a telehealth platform, equipped with comprehensive, real-world data via a live feed, is feasible and effective for cancer patient care. It enhances inpatient management by improving time efficiencies, reducing drop-off rates, and providing easy access to medical history. Moreover, it fosters a positive experience in physician-patient interactions.

Evaluation of Productivity and Cost Analysis on a Combined Logging System

The efficient planning and control of timber harvesting operations is crucial to growth productivity and reducing costs, and different systems or methods are applied to obtain elevated performance. In particular, cable yarding is a highly appreciated and very efficient wood extraction method in areas that are difficult to access with ground-based extraction machines. Combined logging systems are not diffuse and not often implemented. For this reason, the use and the application of innovative methods are limited. However, some combinations have recently been observed in forest operations. This study, conducted in a deciduous stand in Bulgaria, paid attention to the combination of a tractor-mounted tower yarder (Valentini V400) with a clambunk skidder (Timberjack 1010D), aiming to examine the viability and develop the operational efficiency of the two-stage extraction system and to define the time, log volume extracted per unit by the yarder and the clambunk, and the yarding and skidding costs. The researchers carried out a time-motion study and performed a regression analysis to identify those variables that are most likely to affect the duration of work elements and productivity. For cost analysis, the COST model was used. The mean productivity of the tower yarder was 10.34 m3/PMH and 8.11 m3/SMH, while for the clambunk skidder, the productivity was 6.23 m3/PMH and 4.93 m3/SMH. The net costs for the combined logging system were calculated as 120.87 €/PMH and 14.93 €/m3. This study adds new data about the correct use of both machines, supporting their application in beech forests.

Where Are Czech Adolescents Active? The Patterns of Movement and Transport Behavior in Different Active Living Domains.

To understand the environmental determinants of physical activity (PA), precise spatial localization is crucial. This cross-sectional study focuses on the spatiotemporal distribution of PA among Czech adolescents (n = 171) using Global Positioning System loggers and accelerometers. The results showed that adolescents spent most of their time in sedentary behavior, with 57.2% and 58.5% of monitored time at home and school, respectively. The park and playground had the lowest proportion of sedentary behavior but also the lowest amount of moderate to vigorous PA (MVPA). However, when considering the time spent in each domain, the highest proportion of MVPA was seen in publicly accessible playgrounds (13.3% of the time). Chi-square analysis showed that the relative distribution of different PA intensities did not differ across spatial domains. Based on these results, the authors propose 2 key strategies for increasing MVPA in adolescents: Increase the time spent in activity-supportive environments, such as parks and playgrounds, and design techniques to increase MVPA at home and school settings.

Book Leans Head

The BookLeansHead is a "Book Management System" is a comprehensive project designed to enhance the management of books. The system incorporates advanced database management techniques, user-friendly interfaces, and automation to streamline various aspects of books management operations. Key features include real-time book tracking, userfriendly interfaces for both employee and user, and automated processes for tasks such as book check-in/check-out, overdue notifications, and inventory management. BookLeansHead is a project used to develop a computerized system of maintaining all the daily activities.. This project has many features that differ from normal book management systems like the facility of user login, employee login and the facility of Admin login. It helps to improve management of the book property.This project has three modules as user, employees and Admin.BookLeansHead is one of the most tedious processes, which involves the regular updating of many files and records. As new members enroll for membership the management.

CARRER PATH

The "Career Road Map Path" app is a cutting-edge platform designed to revolutionize the way individuals navigate their professional journeys. With a strong emphasis on user security and personalized guidance, the app offers a seamless experience through its intuitive login system, allowing users to access their profiles with email or phone number authentication. Catering to the diverse needs of users, the app divides them into two distinct categories: "Career Path Seekers" and "Job Seekers." The former category empowers individuals to embark on a well-defined path by selecting their study level and specifying their fields of interest. Leveraging this information, the app generates a customized roadmap that outlines crucial educational milestones, suggests relevant courses and internships, and even recommends skill-building activities tailored to their chosen career trajectory. Meanwhile, the Job Seekers segment provides an immersive dual-pathway approach: Government and Private jobs. Each pathway offers an extensive array of job listings complete with comprehensive descriptions, explicit qualification requirements, and clear application procedures. For government job aspirants, the app furnishes details about upcoming job opportunities, including vital information about job roles, eligibility criteria, application deadlines, and exam schedules. On the other hand, those seeking private sector employment can seamlessly explore openings across various industries, locations, and job types. Recognizing the significance of exam preparation in achieving career goals, the app goes beyond job listings and incorporates a dedicated exam preparation component. This feature equips users with study materials, practice tests, and real-time notifications about exam dates, ensuring they are well-prepared and confident when facing competitive assessments. Furthermore, the app encourages user interaction, enabling individuals to engage with the platform through feedback, reviews, and ratings for courses, resources, and job listings. This dynamic community interaction fosters a collaborative environment where users can benefit from shared experiences, insights, and advice. The power of technology truly comes to the fore as the app employs advanced machine learning algorithms. These algorithms continuously analyse user behaviours, preferences, and success stories to fine-tune its recommendations, ensuring that the guidance provided becomes increasingly tailored and effective over time. In Summary the "Career Road Map Path" app represents a holistic solution that seamlessly blends data security, personalized career guidance, comprehensive job listings, exam preparation resources, and a thriving user community. By converging these essential elements, the app aspires to redefine the way individuals approach their career aspirations and set them on a trajectory toward success with confidence and clarity. Keywords Career navigation, Personalized guidance, User security, Intuitive login system, Career Path Seekers, Job Seekers, Dual-pathway approach, Government jobs, Private sector employment, Exam preparation, Community interaction, Machine learning algorithms, Data-driven recommendations, Holistic career solution, Confidence and clarity.

Analyzing Potential Failures and Effects in a Pilot-Scale Biomass Preprocessing Facility for Improved Reliability

This study demonstrates a failure identification methodology applied to a preprocessing facility generating conversion-ready feedstocks from biomass meeting conversion process critical quality attribute (CQA) specifications. Failure Modes and Effects Analysis (FMEA) was used as an industrially relevant risk analysis approach to evaluate a logging residue preprocessing system to prepare feedstock for pyrolysis conversion. Risk evaluations considered both system-level and operation unit-level assessments considering process efficiency, product quality, cost, sustainability, and safety. Key outputs included estimations of semi-quantitative risk scores for each failure, identification of the failure impacts, identification of failure causes associated with material attributes and process parameters, ranking success rates of failure detection methods, and speculation of potential mitigation strategies for decreasing failure risk scores. Results showed that deviations from moisture specifications had cascading consequences for other CQAs along with process safety implications. Failures linked to fixed carbon specifications carried the highest risk scores for product quality and process efficiency impacts. As increased throughput can be inversely related to meeting product quality specifications; achieving throughput and other material-based CQAs simultaneously will likely require system optimization or prioritization based on system economics. Ultimately, this work successfully demonstrates FMEA as a risk analysis approach for other bioenergy process systems.

MEDICAL DATA EXTRACTION

The proposed system is a user-friendly and efficient Data Extraction software for Medical Documents and Images, integrating advanced technologies such as PHP, Android, database, ML-Kit, retrofit, recycler views. The application facilitates efficient user registration, user login, insurance claims, and management of claimed users. The system aims to enhance the accessibility and accuracy of medical data extraction, thereby optimizing insurance procedures for agents, Also This paper outlines the technical architecture, functionalities, and implementation details of the application, highlighting its potential to revolutionize insurance operations within the healthcare industry. Keywords: - Machine Learning Kit(ML-Kit)

Current Trends in Research on the Role of University Libraries in Students’ Academic Success: a Review

The student’s academic success is considered as a measure of institution’s performance and their departments, therefore also of the libraries. The purpose of this paper is to review the literature to determine the impact of university libraries on student’s academic success. A four-step literature review methodology was performed. The search was made in Web of Science Core Collection and Google Academic databases. The most used elements to measure this success were: grade point average, semester grades and retention. The most used library services were: borrowing, information literacy, library visit, computer login and library website login. The literature analyzed in this research shows that university libraries have a clear positive impact on the student’s academic success. Also, they have a role in maintaining academic integrity and promote nine Sustainable Development Goals. Libraries embrace all aspects of sustainability. However, the focus is on environmental aspects. The importance given to this subject increases starting from 2017. Another aspect that should be mentioned is that there are no articles that came from the Central and Eastern Europe region.

Underpass Water Logging System Using IOT and ML

In recent years, the surge in population growth has exacerbated numerous challenges for both humans and the environment, including issues like inadequate infrastructure and frequent occurrences of waterlogging in underpasses. During heavy rainfall, underpasses often become inundated, posing significant risks to passing vehicles and exacerbating traffic congestion. This is primarily due to ineffective drainage systems and flawed designs for diverting rainwater away from the underpasses. To address these pressing issues, this paper proposes an IoT-based solution aimed at monitoring and managing heavy traffic within underpasses. The primary objective is to establish a comprehensive framework capable of continuously monitoring the water levels within underpasses. By leveraging IoT technology, this framework can activate warning signs and implement traffic control measures in real-time to mitigate the risks posed by waterlogging and alleviate traffic congestion. This innovative approach not only offers a proactive solution to the immediate challenges faced by underpasses but also lays the foundation for a more sustainable and efficient urban infrastructure management system. Through the integration of IoT sensors and intelligent control mechanisms, this framework promises to enhance the resilience of underpasses to adverse weather conditions while optimizing traffic flow, ultimately contributing to safer and more livable urban environments . Keywords — Internet of Things, sensors, Wi-Fi module, LCD, cloud architecture.

TEXT TO IMAGE GENERATOR USING AI

This web application is proposed to generate images that is given in a prompt. This can generate imaginary pictures. For the conversion, we need DALL E & Open Ai. It will be fun creating the artistic, realistic images from the prompt. This is a Web Application project developed using A& OpenAI, we used Natural language description prompt for our project. It creates images through prompts. This will be advantage for executing different ideas, thoughts into textual presentation. DALLE can be used for advertising, printing, selling etc. Using this web application project, we can enhance our imaginative ideas into a realistic one. It is a friendly web where we won't face any issues in pictures. And We can't find this imaginary picture generator in any search engine. This Android application project will give you a picture with whatever size you want. And it won't reduce the quality of a picture. The quality size of a picture will be 256 × 256, 512 × 512 and 1024 × 1024.We can choose a quality size based on our network quality. Before using this web application, we have to make sure the network facilities. Keywords – Web application, user login, user registration, prompt field, search field, download, image assessment.

Web Development for Bank Statement Analysis: Technologies, Challenges, and Innovations

The purpose of this project is to fulfill the requirements of a bachelor’s degree in Cloud Technology and Information Security. The project aims to design and develop a Bank Statement Analysis system that provides a more secure approach to managing bank customers' statements, strengthening the relationship between banks and their customers by offering solutions with multi-level security to improve customer satisfaction. Methodology The project utilizes a range of technologies, including: · Python Flask Framework · Computer Vision (Google's Tesseract OCR) · PDF to Image Python package (pdf2image) · Machine Learning (Scikit-Learn) to predict transaction categories · Azure SQL Database to store transaction data and user login details · Power-BI for data visualization · Docker and AWS EC2 for service hosting. Findings The Bank Statement Analysis system developed in this project offers a secure and efficient solution for managing bank customers' statements. The use of multi-level security measures, such as Azure SQL Database and Power-BI visualizations, enhances the overall security and user experience, strengthening the relationship between banks and their customers. Conclusion This project demonstrates the successful design and development of a Bank Statement Analysis system that leverages various technologies, including cloud computing, computer vision, and machine learning, to provide a more secure and user-friendly solution for managing bank customers' statements. Keywords Azure SQL Database, Power-BI Tool, AWS EC2 Service

Automotive digital forensics through data and log analysis of vehicle diagnosis Android apps

Modern vehicles including smart cars have been equipped with many electronic devices such as electronic control units (ECUs), on-board diagnostics (OBD) systems, telematics and infotainment systems, gateways, sensors, etc. Because these devices create, transmit, and store a lot of digital data, modern vehicles are becoming key source of digital evidence in vehicular forensics. In addition, some dedicated mobile apps can capture driving and diagnostic data from a vehicle via a Bluetooth-enabled OBD-II scanner. In this paper, we propose a new process for effective automotive forensics. It collects and analyzes three different types of data left on an Android phone which has been connected to the OBD-II port of a vehicle via Bluetooth communication. The three types of data are OBD-II Android apps' data, Bluetooth HCI snoop log, and the main log buffer of the Android logging system. By analyzing them individually and integratedly, we find Bluetooth connection time, vehicle information, MAC address of the OBD-II scanner, vehicle velocity, sharp speeding event, sudden braking event, refueling event, and so on. We also construct a timeline of Bluetooth traffic and driving events through the timeline analysis, which can be used to determine the driver's behaviors in terms of vehicle forensics.

Turf Tracker Application

Abstract: To solve the Sports venues booking problems, Turf Tracker Application is designed, combining the experience of the existing online booking systems, the main methods and the major ideal of building these systems in different cities. The basic functions of the system are designed according to the general requirement analysis, including user's registration and login, online sports venue booking, personal center and database construction. The development of the system used Java programming language, J2EE for backend development, Spring Boot (a Web platform development technology), MySQL database processing technology, ReactJS for frontend and Visual Studio Code. The system has accomplished such functions as online Turf booking, view user bookings, add grounds, add indoor court and view grounds/court. The tests of the system are run in good conditions. The use of the system has made up for the current luck of sports venues management, solved the problems of the online booking of sports venues, improved the efficiency of the venues and meet the needs of efficient use of the venues.