Data Loss Research Articles

Dual-Wide Multi-Band (DWMB) four-port flexible MIMO antenna for on-body multiple wireless applications including high diversity performance.

The single-input-single-output technology experiences loss of data in the communication channel due to the receiving antenna undergoing fading of the signal impinged on it. Today's need is faster data transfer with multiple applications in the single antenna with multiple-identical radiating elements, leading to multiple-input-multiple-outputDWMB (MIMODWMB) technology. The MIMODWMB configuration with multi-band capability is the objective of the proposed work with applications ranging between microwave-millimeterWave bands. The four-port Dual-Wide Multi-Band (DWMB) MIMODWMB antenna radiating electro-magnetic-energy is proposed, which generates measured bandwidths of 7.27GHz-34.32GHz (Band 1) and 46.54GHz-71.52GHz (Band 2) including applications Up-link/Down-link Satellite System, X-Band, Ku-Band, ISM 24.0GHz (24.0GHz-24.25GHz), 24.0GHz UWB Band (21.65GHz-26.65GHz), n258, n257/n261 and n263 V-band. The proposed antenna technology is printed on Rogers's low permittivity substrate with a hexagon patch etched with dual merged-elliptical slot and three identical circular slots to achieve high impedance matching for Band 1. The partial-ground is etched by a rectangular slot for better impedance matching, and two-thin-etched rectangular slits generate 60.0GHz Band 2. The thin substrate, thickness 0.254mm, is utilized for flexible applications without compromising the operation of dual wide bandwidths. The flexible antenna is also subjected to analysis of Specific-Absorption-Rate (SAR) analysis at key frequencies within both the bands and found to be within the standard limit of 1.60W/Kg for 1g of the human tissue model and corresponds to 1.01W/Kg at 10.0GHz, 0.280W/Kg at 15.0GHz, 0.475W/Kg at 26.0GHz, 0.588W/Kg at 28.0GHz & 0.301W/Kg at 60.0GHz. The high diversity performance with Envelope Correlation Coefficient<0.50, Diversity Gain≈10.0dB, Total Active Reflection Coefficent<0dB, Channel Capacity Loss<0.40b/s/Hz and multi-band capability for mobile users make the proposed work suitable for flexible on-body applications in a wireless environment. The proposed work MIMODWMB antenna offers advantages such as reduced size (20mm×24mm: 0.61λ0×0.74λ0 at λ0 = 7.27GHz) and a wide range of impedance bandwidths, which are useful for several applications. Also, due to the flexible nature of the design, they can be used for future on-body wearable applications.

Active Impulsive Noise Control with Missing Input Data Based on FxImdMCC Algorithm

In this study, we address the challenge of noise reduction in environments characterized by impulsive noise and missing input data in active noise control (ANC) systems, where existing algorithms often fail to deliver satisfactory results. Background noise, especially impulsive noise, poses a significant obstacle to signal processing and noise reduction. Furthermore, data loss during transmission or acquisition further complicates the noise reduction task. In this paper, a filtered-x imputation of the missing data maximum correntropy criterion (FxImdMCC) algorithm is proposed based on an imputation model, least mean square, and the maximum correntropy criterion (MCC), which can effectively reduce the impact of outliers and missing input data. The simulation results demonstrate the efficacy of the proposed FxImdMCC algorithm, which significantly outperforms existing algorithms in the context of active impulsive noise control.

Evaluating Uncertainty Quantification in Medical Image Segmentation: A Multi-Dataset, Multi-Algorithm Study

Deep learning is revolutionizing various scientific fields, with medical applications at the forefront. One key focus is automating image segmentation, a process crucial in many clinical services. However, medical images are often ambiguous and challenging even for experts. To address this, reliable models need to quantify their uncertainty, allowing physicians to understand the model’s confidence in its segmentation. This paper explores how the performance and uncertainty of a model are influenced by the number of annotations per input sample. We examine the effects of both single and multiple manual annotations on various deep learning architectures. To tackle this question, we employ three widely recognized deep learning architectures and evaluate them across four publicly available datasets. Furthermore, we explore the effects of dropout rates on Monte Carlo models by examining uncertainty models with dropout rates of 20%, 40%, 60%, and 80%. Subsequently, we evaluate the models using various measurement metrics. The findings reveal that the influence of multiple annotations varies significantly depending on the datasets. Additionally, we observe that the dropout rate has minimal or no impact on the model’s performance unless there is a substantial loss of training data, primarily evident in the 80% dropout rate scenario.

Image Encryption Using High-Speed Scrambling and ‎Modular Arithmetic

With the enormous growth and advancement of the computer industry, it is critically necessary to transmit a large amount of encrypted multimedia data to prevent third parties from attacking one's privacy or misbehaving. This study proposes a new encryption strategy for preserving original images. It is highly effective and has been shown to be resilient against impulsive noise and loss of data. At first, some random data is entered within the perimeter of the image. Then, three rounds of high-speed scrambling and adaptive diffusion for pixels are carried out to mix the contiguous pixels at random and distribute the newly placed data throughout the image. The suggested encryption method can be used to encrypt original images in any form of representation directly. We offer a particular operation to carry out adaptive diffusion for pixel modulo arithmetic. The results of encryption are evaluated using the histogram, which shows a near-zero correlation with a Number of Pixel Change Rate (NPCR) of 099.624, a Unified Average Change Intensity (UACI) of 033.577, and an average entropy of 7.9993. The proposed method can accomplish much better speed and adapt better to impulse noise and loss of data interference than several conventional and cutting-edge encryption methods.

A Comparative Study Enhancing Dynamic Routing in Computer Networks: Strategies for Optimal Performance using EIGRP

Computer networks have been stagnant for a long time since earlier computers could not connect and communicate with each other. Nowadays, computer networks have evolved so much that computers can easily process and share data through a network. But while the systems are now communicating, it doesn’t mean the information is travelling flawlessly and there are no packet loss, meaning the loss of data. This brings us to different ways as how we can send and receive data, Internet protocols have been in place and are getting better with time. This research highlights the nuanced protocols incorporating dynamic routing that are being used like EIGRP referring to Enhanced interior gateway routing protocol and also shows a comparison with other protocols like OSPF standing for Open Shortest Path First, and RIP (Routing Information Protocol), also IGRP (Internal Gateway Routing Protocol). Keywords— Computer networks, Dynamic routing, Packet loss, Data transmission, Internet protocols, Network communication.

A Block-chain Based Mechanism for Securely Storing Data on Cloud and IOT

The burgeoning paradigm of cloud computing has made a number of tools available to researchers. It is a cutting-edge technology that keeps revolutionizing many sectors in every imaginable method. It is a promising technology that gives users and researchers new perspectives, development, and many amazing factors. The integration of cloud and block-chain technology and internet of things (IOT). The benefits both service providers and customers by enabling the use of resources that can be authenticated while protecting anonymity. This joining method is distinct and environmentally. This cooperative method sheds light on the different paths or facets of computers that can solve issues other than security-related ones. The primary problem at the moment is cloud security. For service providers, it's critical to protect client privacy and avoid data loss. Using client data privacy solutions, the current review study focused on block-chain with combination of IOT and data security. In order to strengthen security and expand the power or utility of cloud systems, a comprehensive examination is also carried out. Block-chain technology has the ability to increase speed and anonymity while providing numerous advantages to cloud-based and IOT based applications.

Bayesian modeling for analyzing heterogeneous response in preclinical mouse tumor models.

In anticancer research, tumor growth measured in mouse models is important for assessing treatment efficacy for a treatment to progress to human clinical trials. Statistical analysis of time-to-event tumor volume data is complex because of heterogeneity in response and welfare-related data loss. Traditional statistical methods of testing the mean difference between groups are not robust because they assume common responses across a population. Heterogeneity in response is also seen in the clinic, and consequently, the assessment of the treatment considers the diversity through classification of the individual's response using the RECIST (Response Evaluation Criteria in Solid Tumors). To provide a comparable and translatable assessment of in vivo tumor response, we developed a statistical method called INSPECT (IN vivo reSPonsE Classification of Tumors) for analyzing heterogeneous responses through Bayesian modeling. This method can classify individual tumor behaviors into the categories of nonresponder, modest responder, stable responder, and regressing responder. Using both published and simulated data, we show that INSPECT methodology is more accurate and sensitive than existing methods with respect to balancing false-negative and false-positive rates. A case study demonstrates the value of INSPECT in drug projects for supporting the translation of drug efficacy from the preclinical phase into clinical trials. We also provide a package, "INSPECTumours," that launches a web interface to enable users to conduct the analysis and generate reports.

Enhancing Erasure Resilience in Reed-Solomon Codes: Theory and Applications in Data Storage Systems

Reed-Solomon (RS) codes are widely utilized in data storage and communication systems due to their robust error detection and correction capabilities. However, traditional RS codes encounter challenges in addressing the increasing data corruption rates demanded by modern storage systems. This paper aims to overcome these limitations by modifying and extending the structure of traditional RS codes, particularly enhancing their recovery capabilities in the face of significant data loss. We begin by reviewing the theoretical foundations of RS codes and existing extension methods and discussing emerging technologies integrated into RS codes. We then present detailed methodologies for RS code encoding, erasure recovery mechanisms, soft decision decoding, and interleaving coding techniques, followed by a series of experiments designed to test the proposed methods. The experimental results indicate that soft-decision decoding outperforms traditional hard-decision decoding under high signal-to-noise ratio conditions, albeit with increased computational complexity as the list of candidate codewords grows. Interleaved Reed-Solomon (IRS) coding offers improved performance under low signal-to-noise ratio conditions but may introduce additional system complexity due to the interleaving and deinterleaving processes. We conclude with a summary of the research findings, a discussion of the study’s limitations, and suggestions for future research directions.

Development and Evaluation of a Robust UART Communication System with Enhanced Interference Resistance

Interference is a significant issue in UART communication, often causing data jitter and distortion, particularly in industrial environments. This study addresses these challenges by developing an anti-interference UART system that includes the design and implementation of receiving, processing, and sending modules. The system utilizes techniques such as multi-level sampling, synchronization, and cumulative decision-making to enhance data transmission accuracy and reliability. In embedded systems, electromagnetic interference (EMI) poses a major problem, leading to potential data loss and communication failures. Reliable communication is crucial for maintaining the performance and safety of industrial operations. The anti-interference UART system developed in this study aims to improve operational stability by mitigating the effects of EMI. The system’s performance was validated through extensive simulations, demonstrating its ability to maintain stable and accurate data transmission even under various interference conditions. The results show significant improvements in data integrity and communication reliability. This paper presents a comprehensive approach to designing, implementing, and validating a robust UART system that can effectively operate in challenging environments, ensuring reliable data communication and enhancing overall system performance.

Developed Internet of Vehicles Architecture: Communication, Big Data and Route Determination Perspectives

Internet of vehicles (IoV) has become an important research topic due to its direct effect on intelligent transportation systems (ITS) development. There are many challenges in the IoV environment, such as communication, big data and best route assigning. In this paper, an effective IoV architecture is proposed. This architecture has four main objectives. The first objective is to utilize a powerful communication scheme in which three tiers of coverage tools — Internet, satellite, high-altitude platform (HAP) — are utilized. Therefore, the vehicles maintain a continuous connection to the IoV environment everywhere. The second objective is to apply filtering and prioritization mechanisms to reduce the detrimental effects of IoV big data. The third objective is to assign the best route for a vehicle after determining its real-time priority. The fourth objective is to analyze the IoV data. The proposed architecture performance is measured using a simulation environment that is created by the NS-3 package. The simulation results proved that the proposed IoV architecture has a positive impact on the IoV environment according to the performance metrics: energy, success rate of route assignment, filtering effect, data loss, delay, usage of coverage tools and throughput.

Crack Propagation Tests for Load Sequences Developed Using Different Flight Parameters of a Trainer Aircraft

Abstract Military aircraft are subjected to highly variable and unpredictable loads due to diverse mission profiles, armament configurations, and individual piloting styles. This variability complicates the definition of precise load spectra, particularly in cases where data loss occurs due to Flight Data Recorder (FDR) malfunctions or data mishandling. This paper investigates the use of different flight parameters, such as load factor (nz ), barometric height (Hb ), and horizontal velocity (Vp ), to define load sequences for the PZL-130 “Orlik” TC-II military trainer aircraft. These sequences were then used to evaluate crack propagation using Compact Tension (CT) specimens. The results show that the incorporation of additional flight parameters improves the accuracy of crack propagation predictions when compared to direct strain measurements. This study highlights the potential of using available flight data to develop reliable load spectra for fatigue life estimation in military aircraft, even when direct load measurements are not financially feasible.

Finite-time bounded security control of networked switched stochastic systems

This paper is dedicated to discussing stochastic finite-time bounded control of switched stochastic systems with feedback information sampled by an event-triggered mechanism and transmitted by network. Besides the incomplete information because of sample, data loss and corruption during information transmission caused by hybrid cyber attacks including DoS attacks and false-data injection attacks, are also taken into consideration. Based on the above incomplete information, state feedback controllers are established to fulfil stochastic finite-time boundedness of switched stochastic systems and make it have a certain disturbance attenuation capability, i.e. have a finite-time L 2 -gain, successfully. Sufficient conditions are cast into a convex optimisation problem which can be easily solved by fixing some parameters. Simulation results are employed to verify the validity of results.

Advancing Marine Surveillance: A Hybrid Approach of Physics Infused Neural Network for Enhanced Vessel Tracking Using Automatic Identification System Data

In the domain of maritime surveillance, the continuous tracking and monitoring of vessels are imperative for the early detection of potential threats. The Automatic Identification System (AIS) database, which collects vessel movement data over time, including timestamps and other motion details, plays a crucial role in real-time maritime monitoring. However, it frequently exhibits irregular intervals of data collection and intricate, intersecting trajectories, underscoring the importance of analyzing long-term temporal patterns for effective vessel tracking. While Kalman Filters and other physics-based models have been employed to tackle these issues, their effectiveness is limited by their inability to capture long-term dependence and non-linearity in the historical data. This paper introduces a novel approach that leverages Long Short-Term Memory (LSTM), a type of recurrent neural network, renowned for its proficiency in recognizing patterns over extended periods. Recognizing the strengths and limitations of the LSTM model, we propose a hybrid machine-learning algorithm that integrates LSTM with a physics-based model. This combination harnesses the physical laws governing vessel movements alongside data driven pattern mining, thereby enhancing the predictive accuracy of vessel locations. To assess the performance of standalone and hybrid models, various scenarios with different levels of complexity are generated. Furthermore, to simulate real-world data loss conditions often encountered in maritime tracking, temporal data gaps are randomly introduced into the scenarios. The competing approaches are then evaluated using both with time gap and without time gap conditions. Our results show that, although the LSTM model performs better than the physics-based model, the hybrid model consistently outperforms both standalone models across all scenarios. Furthermore, while data gaps negatively impact the accuracy of all models, the performance reduction is minimal for the physics-infused model. In summary, this study not only demonstrates the potential of combining data-driven and physics-based approaches but also sets a new benchmark for maritime vessel tracking.

Implementing Dynamic Systems Development Method for a Web-Based System to Evaluate Child Health and Growth

The Simpang Gambir UPTD Community Health Center has developed an innovative digital system to monitor the growth of toddlers. Previously, the recording of toddler growth data was done manually, often leading to data loss or damage. This new system is designed to address these issues and provide a more efficient and accurate solution. The system not only facilitates health center staff and posyandu cadres in monitoring toddler development but also assists them in creating digital growth reports. With this system, toddler growth data can be accessed quickly and easily, facilitating decision-making regarding child health management. One of the key features of this system is its ability to track toddler growth based on weight-for-age charts. This feature allows health workers to easily identify toddlers with nutritional problems and promptly provide necessary interventions. Additionally, the system is equipped with a fast data search feature, enabling staff to easily find specific toddler growth data. The development of this system utilizes the Dynamic System Development Method (DSDM), allowing for a structured and efficient development process. With this method, the system can be developed rapidly and in accordance with user needs.

Extracting terrain elevation information in front of the vehicle based on vehicle-mounted LiDAR in dynamic environments

Abstract Point cloud maps constructed using 3D LiDAR, are widely used for robot navigation and localization. Few studies have utilized point cloud maps to extract terrain elevationinformation in front of a vehicle, which can be used as active suspension inputs to reduce vehicle bumps. In addition, the trajectories of dynamic objects in point cloud maps and global navigation satellite system (GNSS) data loss can affect the extraction of elevation information. To solve these problems, this paper proposes a framework for extracting terrain elevation information in front of the vehicle based on vehicle-mounted LiDAR in dynamic environments. The framework consists of two modules: point cloud map construction and vehicle front terrain elevation information extraction. In the point cloud map construction module, a system for simultaneous localization and mapping (SLAM) is proposed, which is capable of building point cloud maps without GNSS. Furthermore, a dynamic descriptor-based dynamic object filtering algorithm is proposed which is applied to SLAM. Therefore, the SLAM system overcomes the influence of dynamic objects on vehicle position and attitude estimation, and there are no trajectories of dynamic objects in the point cloud maps built by the system. In the vehicle front terrain elevation information extraction module, the unscented Kalman filter is utilized to predict the vehicle position at the next moment. Based on the geometric features of the tire-ground contact area, the terrain elevation information of the tire contact area at the predicted position on the point cloud map is extracted. Experiments show that the algorithm in this paper overcomes the effect of dynamic objects and builds a vehicle point cloud map without dynamic objects under GNSS data loss, which improves the accuracy of the extraction of terrain elevation information in front of the vehicle.

Information and Technology Governance Capability Analysis Using COBIT 2019: Case Study of CV Wirelessindo

The implementation of IT supports company sustainability. Large companies use IT as an integral part of their business model. Digital strategies must align with business strategies to achieve sustainability. Wirelessindo, established in 2007, provides IT solutions such as equipment, site surveys, network design and security, and tower installations. Its mission is to increase company productivity to win the competition in the era of Industry 4.0. IT is used to enhance customer service and internal business process efficiency. Initial investigations found issues with IT function mismatches, financial losses due to data loss, errors in IT equipment installation and configuration, and business process incidents caused by emerging risks. Identified root problems include employee knowledge gaps, risk management disparities, errors in defining business needs, and change management gaps. Based on these findings, IT governance evaluation was chosen as the research focus. This study aims to provide an overview of CV Wirelessindo IT capability levels and recommendations for improvements to meet expected levels. COBIT 2019 were used in this study. The General Manager, IT Division Manager, Software Development Team, Network Monitoring Team, Project Manager, and HR Manager were selected as respondents based on the Responsible, Accountable, Checked, Informed chart guidelines. Of the nine selected objectives, BAI08 showed level 0. APO12, BAI02, BAI06, BAI07, and DSS01 showed level 1. APO07, BAI10, and DSS03 showed capability level 2. Therefore, 61 recommendations were provided to raise the IT governance capability level to level 3 for all objectives.

The Feasibility of Using an Intravaginal Intra-Abdominal Pressure Sensor During Running to Evaluate Pelvic Floor Loading and Its Association with Running-Induced Stress Urinary Incontinence: An Observational Cohort Study.

The aim was to investigate the feasibility of using an intravaginal intra-abdominal pressure (IAP) sensor worn by female runners during running to evaluate pelvic floor loading, and the association between IAP and running-induced stress urinary incontinence (RI-SUI). Twenty-eight female runners participated in this cross-sectional study (15 with RI-SUI and 13 continent). Participants completed a 37-min treadmill running protocol instrumented with an intravaginal sensor measuring IAP, and a skin-mounted accelerometer measuring pelvic accelerations. Linear regression analyses evaluated the relationship between IAP and pelvic acceleration. One-way ANOVAs assessed the impact of running speed on IAP. Sensor dislodgement was recorded, and effect sizes (Cohen's d) were calculated for the comparison of IAP and accelerometry outcomes between runners with and without RI-SUI. Intra-abdominal pressure showed high variance and the sensor was expelled in almost 15% of participants. Positive associations were found between pelvic acceleration and both absolute peak and normalized IAP but explained only between 12% and 18.6% of the variance in the models. Pelvic acceleration during running was not associated with cumulative IAP. Peak IAP significantly increased with running speed. Although IAP tended to be higher in runners with RI-SUI than in continent runners (Cohen's d between 0.14 and 0.74), pelvic accelerations tended to be lower (Cohen's d between 0.02 and 0.55). There is high variance in IAP recorded during running using an intravaginal sensor. Faster speeds increase pelvic floor loading. Runners with RI-SUI may experience higher IAP than continent runners, warranting investigation. Recruitment should include a 15% data loss rate owing to sensor expulsion.

Cybercrime and the Necessity of Self-Protection in Fighting Cyber-Attacks

A cyberattack is an attack initiated from a computer against a website, computer system, or individual computer … that compromises the confidentiality, integrity, or availability of the computer or information stored on it. Every man’s activity nowadays is guarded, controlled, and directed majorly by the internet. The internet which seeks to connect individuals and corporate bodies has no doubt been infiltrated by bandwagons of internet fraudsters, cyber attackers, and computer programmers who have fashioned out so many means of attacking internet users like phishing, writing virus codes, installing rootkits, cross-site scripting and a host of others which in turn causes damages by stealing private personal data, attacking companies and their websites, attacking corporate entities, and organizations, etc. There’s equally no doubt to the fact that all these attacks by whatever mode always affect the receiver of the attack ineptly and in so many cases, lead to the loss of some personal data, private documents, and some others in monetary terms/values. It has become so important that individuals and corporate entities adopt various strategies and devise means to protect and guard themselves against cyberattacks. This article seeks to explain the concept of self-protection by identifying some types of cyber threats and explaining various ways by which individuals and bodies can protect themselves on the internet against cyberattacks thereby ensuring cyber security.

Maize Phenotypic Parameters Based on the Constrained Region Point Cloud Phenotyping Algorithm as a Developed Method

As one of the world’s most crucial food crops, maize plays a pivotal role in ensuring food security and driving economic growth. The diversification of maize variety breeding is significantly enhancing the cumulative benefits in these areas. Precise measurement of phenotypic data is pivotal for the selection and breeding of maize varieties in cultivation and production. However, in outdoor environments, conventional phenotyping methods, including point cloud processing techniques based on region growing algorithms and clustering segmentation, encounter significant challenges due to the low density and frequent loss of point cloud data. These issues substantially compromise measurement accuracy and computational efficiency. Consequently, this paper introduces a Constrained Region Point Cloud Phenotyping (CRPCP) algorithm that proficiently detects the phenotypic traits of multiple maize plants in sparse outdoor point cloud data. The CRPCP algorithm consists primarily of three core components: (1) a constrained region growth algorithm for effective segmentation of maize stem point clouds in complex backgrounds; (2) a radial basis interpolation technique to bridge gaps in point cloud data caused by environmental factors; and (3) a multi-level parallel decomposition strategy leveraging scene blocking and plant instances to enable high-throughput real-time computation. The results demonstrate that the CRPCP algorithm achieves a segmentation accuracy of 96.2%. When assessing maize plant height, the algorithm demonstrated a strong correlation with manual measurements, evidenced by a coefficient of determination R2 of 0.9534, a root mean square error (RMSE) of 0.4835 cm, and a mean absolute error (MAE) of 0.383 cm. In evaluating the diameter at breast height (DBH) of the plants, the algorithm yielded an R2 of 0.9407, an RMSE of 0.0368 cm, and an MAE of 0.031 cm. Compared to the PointNet point cloud segmentation method, the CRPCP algorithm reduced segmentation time by more than 44.7%. The CRPCP algorithm proposed in this paper enables efficient segmentation and precise phenotypic measurement of low-density maize multi-plant point cloud data in outdoor environments. This algorithm offers an automated, high-precision, and highly efficient solution for large-scale field phenotypic analysis, with broad applicability in precision breeding, agronomic management, and yield prediction.

Advanced Anomaly Detection in ECG Signals Through Convolutional Autoencoders

This article aims to present a comprehensive study on convolutional autoencoders for advanced anomaly detection in ECG signals. Anomaly detection in complex datasets has become increasingly critical due to the rising need for systems that can effectively identify irregularities that may indicate fraud, system failures, or significant deviations from normal operations. Traditional methods often need help capturing nuanced patterns in high-dimensional data, necessitating more sophisticated approaches. This research uses an autoencoder-based model as a robust solution for anomaly detection, utilizing its capability to learn high-level representations in an unsupervised manner. The proposed model uses a convolutional autoencoder architecture to compress and decompress input data, thus highlighting anomalies through reconstruction errors. We outline detailed experiment strategies, including model training on average data to minimize reconstruction loss, setting an optimal threshold for anomaly sensitivity based on validation loss, and evaluating the model using precision, recall, F1-score, and AUC-ROC metrics. These experiments were conducted using a dataset with labeled normal and abnormal instances, allowing precise tuning and assessment of model performance. The results indicate that the autoencoder discriminates between normal and abnormal data, achieving high precision and recall at 99.22% and 98.98%, respectively. The confusion matrix and loss distribution analysis further validate the model's efficacy, clearly distinguishing between normal and abnormal data loss values concerning the defined threshold. This research shows the autoencoder model demonstrates high accuracy in anomaly detection and offers insights into the types of anomalies it can detect, supporting its application across various domains requiring reliable anomaly identification.