bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of a Wireless Sensor Network for Structural Health Monitoring
Authors: Chamoli Vivek
Published: May 2023

The importance of wireless sensor networks for monitoring structural health is constantly developing in response to the rising need for security and safety in urban areas. As wireless technology has improved, wireless sensor networks have become an integral part of structural monitoring systems. When compared to traditional wired systems, structural health monitoring systems based on wireless sensor networks provide novel technology with tempting advantages, such as reduced installation and maintenance costs. Due to structural health monitoring, wireless sensor networks now have to deal with even more challenging network design problems. This report summarizes the extensive knowledge gained by researchers using wireless sensor networks for structural health monitoring. We look into the design, functioning, communication methods, and well-known operating systems of wireless sensor nodes, as well as the technologies used in both wired and wireless sensor systems. The most up-to-date summaries of academic and commercial wireless platform technologies are then tallied and evaluated in the context of testbed and field deployments for applications related to structural health monitoring. After this, the primary obstacles connected with using wireless sensor networks for structural health monitoring are outlined, and the feasibility of applying wireless technology for such applications is discussed at length. Finally, the issues that still need fixing in wireless sensor networks for structural health monitoring are analyzed.
Evaluative method and Performance Analysis of Water Treatment Plant
Authors: Negi Ankit
Published: May 2023

The effectiveness of water treatment plants (WTPs) and their individual components in achieving safe drinking water standards must be assessed. The study's goal is to assess how successfully the WTP in Nangloi, West Delhi, is doing its job. Some of the physiochemical variables used to measure efficiency are turbidity, total solids, temperature, and total dissolved solids. water loss, pH, dissolved solids, DO, DO, pH, and RC are all parameters that should be monitored. Every hour, six random samples were obtained across the city and examined in compliance with the most up-to-date IS:10500 guidelines for safe drinking water. They were the inlet chamber, the discharge from the four primary clarifier, and the filtration unit. Given that this WTP draws water from a sea water source, it is to be anticipated that all water quality indicators, with the exception of turbidity and microbial levels, are within criteria for the drinking water. The ultimate turbidity of the water is routinely measured to be less than 1NTU, the threshold for unacceptable levels. Any leftover germs are eliminated by the 1.5 ppm of chloride present in the final product. Just 0.36 percent of the water goes down the drain throughout the treatment procedure. The studies show that the treated wastewater is suitable for human intake since it conforms, on average, to the drinking water standards specified by IS:10500. This study will aid in determining the efficiency and location of underperforming units, which will improve water quality at no extra cost..
An Improved Monitoring System via the Integration of Structural Health Monitoring and Earthquake Emergency Alert Technologies
Authors: Nainwa Ankit
Published: May 2023

As a result of the advantages it offers in construction management and maintenance, structural health monitoring for civil structures is becoming more popular in Europe and throughout the world. The main advantages of using such methods include cheaper inspections, more accurate research into the behavior of structures under dynamic loads, better seismic protection, and real-time or near-real-time observation of structural response and evolution of damage, which helps with post-earthquake scenario production and rescue efforts. As a result, Structural Monitoring Systems is a very multidisciplinary field, in which researchers from a wide variety of fields and institutions may work together to improve the effectiveness and reliability of these systems, the benefits of which are almost self-evident. Nowadays, developments in wireless sensor networks, techniques for determining modal properties, and damage detection systems are among the most urgent issues in the sector. Although modal identification and damage detection algorithms have improved, sensor technology has advanced at a faster rate.
Information System for Watershed Management and Long-Term Sustainability
Authors: Negi Ankit
Published: May 2023

In recent years, the many economic, social, and environmental advantages of land-based resources have become more critical. Hence, sustainable source of energy production systems need effective and efficient implementation of these resources. Many believe that watershed management, which is essentially the application of land resource management systems, is the best strategy for protecting and sustaining the planet's precious natural resources and raising the standard of life for people everywhere. Management and conservation of renewable and nonrenewable natural resources in upland regions is commonly regarded to best be achieved via integrated watershed management with involvement of all necessary key players.
The Use of Microbial Technology to Soil Stabilization
Authors: Bahuguna Anoop
Published: May 2023

Microbially exacerbate existing precipitation (MICP) is an emerging technique with great potential for enhancing soil quality. The study's overarching objective is to quantify MICP's contribution to black cotton soil's improved shear strength and reduced hydraulic conductivity. The findings of a laboratory study on the influence of microorganisms on the engineering properties of soil treated with MICP. The bacteria utilized in this study was called Bacillus megaterium. A mold was made with the same interior size as the vortex shear instrument, and soil samples were then tested using this setup. The presence of microbes in black cotton soil greatly improves its shear strength and decreases its hydraulic conductivity, as shown by vane shear and penetration tests.
Exploring the Potential of Wearable Electronics for Healthcare Monitoring and Diagnosis
Authors: Kandpal Jyoti
Published: May 2023

Chronic diseases kill many Humans in all over the world. Monitor risk factors including physical exercise to manage these illnesses. Wearables like Fitbit can track and give health data to help users make decisions. Most wearables marketing targets the young, active, and most populous racial groups. Wearable electronics can revolutionize healthcare by continuously monitoring health factors. Sensor technology, data processing, and communication protocols have made wearable gadgets useful for healthcare monitoring and diagnosis. This article discusses sensors, data processing, and communication protocols used in wearable electronics to revolutionize healthcare monitoring and diagnosis. A side-by-side table compares each method's pros and cons. The topic covers wearable electronics processing for healthcare monitoring and diagnosis. A block architecture and graphic explain healthcare monitoring and diagnosis using wearable electronics. Wearable electronics adoption is often hampered by concerns regarding data privacy and security, data reliability, and healthcare system compatibility. Wearable electronics are revolutionizing medicine in numerous ways, from monitoring chronic illnesses to giving emergency treatment. Wearable tech could develop into artificial intelligence, machine learning, augmented reality, virtual reality, cutting-edge sensors, telemedicine, 5G networks, nanotechnology, and blockchain. Finally, wearable electronics research could improve patient outcomes and quality of life, transforming healthcare.
Design and Implementation of Deep Learning Model for Atrial Fibrillation Classification using ECG Signals
Authors: Sahoo Ashok Kumar
Published: May 2023

Electrocardiograms (ECGs), which are an essential diagnostic tool, are required to be performed in the normal course of clinical practise in order to evaluate cardiac arrhythmias. Convolutional neural network framework is suggested for use in this method, which makes use of deep learning to carry out automatic ECG arrhythmia diagnosis by classifying patient ECGs into the proper cardiac states. The prior training for this network was done using a standard signal data set. The primary objective of this approach is to provide a basic, reliable, and easily implemented deep learning algorithm for the categorization of the two separate cardiac category scenarios that have been selected. The findings demonstrated that a conventional back propagation neural network used in cascade with transferred deep learning classification was able to accomplish exceptionally high levels of performance. The primary objective of this research is to develop an efficient classification system that can forecast the severity of a patient's sleep apnea, as well as to improve classification accuracy and reduce the number of incorrect classifications.
Wavelet Transform Domain for Deep Image Compression Using High Frequency Sub-Band Prediction
Authors: Garg Umang
Published: May 2023

This procedure demonstrates a Two Dimensional Discrete Cosine Transform (2D-DCT) system with quantization and zigzag layout. The JPEG picture compression approach uses this process as the foundation and main channel. Utilizing the 2D- DCT property, the calculation divides the overall process into compression and decompression. Even though the data in the images remained the same, the pixel was reduced predicated on the quantization and zigzag process. Division operation is used throughout the quantization process. As a result, this procedure minimizes data loss during decompression. The theoretical research offered in this study offers some fresh perspectives on how local variation behaves when JPEG compression is applied. Additionally, it might be utilised in a few aspects of image processing and analysis, including picture enhancement, image quality evaluation, and image filtering. MATLAB is used for this project, and improved vector quantization is used. Using the compression ratio, MSE, and PSNR, we can produce compressed pictures.
Application of SVM Algorithm for Fetal ECG Extraction from a Single Maternal Abdominal Record
Authors: Sivaraman Himani
Published: May 2023

The electrical activity of the foetal cardiac muscles is known as foetal ECG (FECG), and it can provide crucial details on the health of the fetus's heart. A pregnant woman's belly can be used to non-invasively capture this signal during pregnancy. However, since additional sources of noise, including the maternal ECG generally overpower the FECG recording, it would be ineffective. However, a clean FECG may be retrieved from the abdominal recording if it is correctly processed, and FECG can be used to evaluate the functioning of the foetal heart. In order to extract foetal electrocardiogram (ECG) from a single abdomen record, a unique two-tier approach is presented in this work. The abdominal signal is processed through a smoothing filter in the first layer of the proposed approach in order to determine the maternal ECG's estimated value. Findings on synthetic and actual abdominal ECG data demonstrate that the intended technique can extract foetal ECG with signal quality equivalent or superior to that retrieved by multichannel based mechanisms. The anticipated maternal ECG is then nonlinearly matched with the abdominal signal through polynomial networks.
An Analytical Review on Plant Leaf Disease Classification in Agriculture Area
Authors: Gupta Richa
Published: May 2023

In this procedure, we suggest that plant disease detection systems use imaging technology to automatically recognise the signs of a plant's ailments on its leaves and stem, assisting in the cultivation of healthy plants in a farm. These devices keep an eye on the plant's leaves and stem, and any alteration seen in its distinguishing characteristics will be automatically detected and also reported to the user. The disease detection techniques that are currently used in plants are evaluated in this work. Plant disease research focuses on a specific plant's visually discernible patterns. In order to successfully cultivate crops, it is determined that identifying plants, leaves, and stems as well as determining the presence of pests, illnesses, or their proportion are all extremely important. The method that many farmers use to find and identify plant diseases is simple observation with the unaided eye. On vast farms, it is less practical and necessitates constant observation. Additionally, non-native illnesses are unknown to the farmers. The primary goals of this research are to pinpoint the area that is afflicted, identify the disease that is present, and enhance classification performance by increasing segmentation accuracy.
An Efficient Segmentation and Classification of MRI Brain Images to Identify the Damage Area
Authors: Panwar Avnish
Published: May 2023

Both computational intelligence and pattern recognition include brain damage segmentation and classification as essential components. In this procedure, an effective algorithm was used to segment the damaged area and extract the data from the images using characteristics like GLCM. Based fuzzy C-means clustering technique (M-FCM) is suggested for clustering during the segmentation process. In order to categorise the severity of the brain input, a procedure to identify brain lesions is used in the medical area, along with a way to classify its characteristics using KNN. The major goals of this method are to locate the malignant area on an MRI of the brain and to categorise the severity of that brain in order to increase process efficiency.
Pavement Crack Detection Algorithm Based on Densely Connected and Deeply Supervised Network
Authors: Aeri Manisha
Published: May 2023

Road cracks may form as a result of climatic changes and low-quality building materials, which is important for maintenance as well as the fact that continual exposure would seriously harm the environment. The automated identification and classification of fractures in the surface of a road's paving, without the need for manually labelled samples. This idea seeks to reduce human subjectivity that results from conventional visual surveys. The fracture identification process is initially carried out using learning from example photos extracted from the dataset. Non-overlapping picture blocks are categorised by the algorithm as either having crack pixels or not. The road photos can be used to find fractures in the pavement. With the aid of feature extraction from the photos, a supervised model is created for the detection of fractures in road photographs. The features were extracted, normalised, and then classified as either crack or non-crack based on the feature values produced. The picture is classified as cracked or not cracked based on the characteristics that were retrieved. During the testing phase, the pictures' characteristics were retrieved and image fractures were located. By comparing the retrieved feature values with the suggested threshold, the fractures were then further divided into several categories. Finally, the process' performance is assessed.
A Cloud-based Decision Support System for Crop Management: Techniques and Applications
Authors: Sharma Abhishek
Published: May 2023

The purpose of this study is to provide an introduction to cloud-based decision support systems for crop management. In recent years, cloud computing, the Internet of Things (IoT), and machine learning have seen significant advancements, which has contributed to the rise in popularity of these kinds of systems. These technologies make it possible for farmers to gather, process, and analyse data in real time, which provides them with invaluable insights about the growth, yield, and overall health of their crops. This, in turn, assists farmers in making decisions that are better informed on the timing of when to sow, irrigate, and fertilise their crops, as well as when to harvest their produce. In this work, we draw on current research conducted in both the academic and industrial spheres to describe the many methods and uses of cloud-based decision support systems for crop management. In conclusion, we will discuss the many advantages and disadvantages of these systems, as well as the possible directions that future study may go.
Cloud Computing-based Disaster Recovery
Authors: Kumar Indrajeet
Published: May 2023

Cloud-based disaster recovery (DR) solutions help organisations recover from natural disasters and cyberattacks quickly and affordably. Cloud-based disaster recovery solutions improve recovery times, capital costs, scalability, and security. Yet, data security and DR strategy are still barriers to adoption. Only two issues. Besides these challenges, cloud-based catastrophe recovery looks promising. Cloud-based disaster recovery solutions will continue to grow as cloud technology improves. Hybrid cloud disaster recovery solutions, which integrate public and private cloud environments, will continue to grow in popularity, as will DRaaS, a cost-effective and flexible disaster recovery solution. As cloud-based disaster recovery solutions grow more prevalent, organisations must understand their disaster recovery needs, carefully analyse their cloud service providers' security and compliance procedures, and regularly test and update their disaster recovery plans and solutions. If organisations follow these measures, they may maximise the benefits of cloud-based disaster recovery solutions to safeguard their critical data and apps and ensure business continuity in a disaster.
Quantitative Evaluation of Sentiment Analysis on E-Commerce Data
Authors: Verma Aditya
Published: May 2023

With the emergence of technology and Big Data in the modern world, it is important to govern such vast amounts of data. Amazon and other e-commerce websites play a vital role in the delivery of goods services to the user. Nonetheless, such services are frequently accompanied by consumer reviews and ratings of the things they sell. Such reviews and ratings provided in the form of textual feedback serve to improve service delivery and product quality in the event that a consumer is dissatisfied. Thus, the process of evaluations and ratings is regarded as a significant component of the customer satisfaction process. Thus, it is vital to analyse them so that useful insights can be gleaned from them and the correct judgements can be made regarding the enhancement of the product. With emphasis on this concept, the authors of the proposed paper intend to develop a model capable of performing sentiment analysis on customer-provided product reviews and analysing products with positive and negative comments. On the basis of these reviews, three machine learning algorithms, primarily Decision Trees, XGBoost, and AdaBoost, are utilised to undertake sentiment analysis on products. It was noticed that XGBoost produced a greater detection accuracy of 84.45%.
Analysis of Fraud Detection on Credit Cards using Data Mining Techniques
Authors: Kumain Kiran
Published: May 2023

Credit cards are widely used and accepted in the financial sector all over the world. The latest trend is to use electronic payments and to go cashless. Unfortunately, these credit card-based online transactions and cashless payments invite online fraudsters, who then attack all forms of online payment, including shopping sites and banking services. According to polls, approximately 4 billion people are presently affected by credit card fraud detection, and by 2025, that figure is projected to increase to 8 billion. Concern for its detection has increased as a result of this worrying pace. Both research scholars and industry experts have contributed their effort in this area for this goal. When considering the credit card detection method, its detection largely becomes a difficult problem. Due mostly to its unstable nature and dependence on customer behaviour, and secondarily because the dataset is readily available and easily accessible. This causes the dataset to become imbalanced, which makes it harder for a researcher to find instances of credit card fraud. Implementations of data mining algorithms are suitable for overcoming such difficulties. As a result, applying the proposed thesis necessitates using the random forest, decision trees, logistic regression, and Naive Bayes. The paper also proposes the use of a stacking algorithm, which integrates the basic theories of decision trees, logistic regression, and random forests, in addition to datamining techniques. According to experimental study of the aforementioned classifiers, the stacking algorithm produced an optimum model with exact precisions and generated the greatest accuracy of 97.78%.
A Deep Learning-based Approach for Stock Price Prediction
Authors: Chaudhary Preeti
Published: May 2023

Predicting stock prices accurately is difficult owing to the numerous elements that might influence stock prices, such as economic indicators, news events, and market patterns. Conventional approaches to stock price prediction frequently depend on statistical models that may be incapable of capturing the complicated interactions between these variables. Deep learning approaches like Long Short-Term Memory (LSTM) networks have showed promise in understanding these complicated linkages and effectively forecasting stock values in recent years. We conduct a literature review on deep learning-based techniques for stock price prediction in this study. We give an overview of deep learning techniques and how they may be used to forecast stock prices. We investigate publically available datasets for training and evaluating deep learning models for stock price prediction. We also show a case study in which we anticipate the stock values of many businesses listed on the National Stock Exchange of India using an LSTM-based model. Our findings show that the LSTM-based model outperforms traditional approaches to stock price prediction, showing that deep learning techniques have the potential to be a valuable tool for investors looking to make educated stock investing decisions.
A Transfer Learning-based Approach for Multimodal Emotion Recognition
Authors: Jain Ayushi
Published: May 2023

The topic of multimodal emotion recognition is one that is expanding at a rapid rate. The goal of this field is to identify and comprehend human emotions through the use of many modalities, such as speech, facial expressions, and physiological data. Transfer learning strategies have been found to be successful in overcoming the issues of processing and integrating material from a variety of modalities, as demonstrated by the findings of a number of studies. For testing multimodal emotion detection models, it is helpful to make use of publicly accessible datasets like IEMOCAP, EmoReact, and AffectNet. They provide useful resources. Data variability, data quality, modality integration, limited labelled data, privacy and ethical issues, and interpretability are only few of the hurdles that must be overcome in order to construct accurate and effective models. In order to address these challenges, a multidisciplinary approach must be taken, and research must continue to be conducted in this area. The goal of this research is to develop more robust and accurate models for multimodal emotion recognition that can be applied across a variety of contexts and populations.
Scalability Challenges and Opportunities in Blockchain-based Systems: A Systematic Review
Authors: Pandey Chetan
Published: May 2023

The blockchain technology is quickly becoming the most disruptive technology of recent times. This technology has generated a lot of buzz and optimism, and it has gained a lot of attention from both the public and private sectors. Information can be transferred using blockchain technology in a manner that is confidential, safe, reliable, and open to scrutiny. This analysis focuses on the ways in which blockchain technology can be used to solve problems related to scalability and give solutions in the context of the healthcare industry. In light of this, the  proposed solutions can be categorized into two primary categories: storage optimization and blockchain redesign. However, there are still several restrictions, such as the block size, the enormous volume of data and transactions, the number of nodes, and the obstacles posed by the protocol. It has seen widespread application in decentralized crypto currencies like Bitcoin and Ethereum, for example. An example of a public blockchain application that was successful, Bitcoin, was the impetus for a significant increase in research and development into blockchain technology. Nevertheless, scalability continues to be a significant obstacle. According to the facts that we've compiled, the primary causes of scalability issues appear to be bottlenecks in the transaction throughput, the network latency, and the consensus methods. On the other side, the potential for scalability includes the application of techniques like as sharding, hybrid systems, and off-chain computations. We also examine the consequences of these findings as well as prospective paths for future study to help solve the scalability problems that blockchain-based systems provide.
Performance Analysis of Routing Protocols in Mobile Ad Hoc Networks (MANETs)
Authors: Manwal Manika
Published: May 2023

Mobile devices are able to build their own networks, which are referred to as mobile ad hoc networks. These networks do not require any kind of permanent wiring or other infrastructure (MANETs). The success of MANETs and their adaptability to a variety of tasks is, to a considerable extent, based on the routing protocols that they use. Using metrics such as routing overhead, end-to-end delay, and packet delivery ratio, we compare and contrast the various routing protocols that can be employed in MANETs, including proactive, reactive, and hybrid protocols. This comparison and contrast is carried out in this study. In addition to this, we discuss the simulation tools that are utilized to evaluate the efficacy of MANET routing algorithms and define the several performance indicators that may be utilizedin the course of this endeavor. This paper makes a contribution to an ongoing body of research into the analysis of routing protocols in MANETs by offering guidance to network engineers and researchers on how to select the most appropriate routing protocol and simulation tool for a particular MANET scenario. In doing so, the paper maximizes the potential for effective and efficient communication between mobile nodes, which is one of the main goals of the research.
A Secure and Robust Cluster-Based Authentication Protocol for VANETs
Authors: Yadav Rishika
Published: May 2023

An emerging subcategory of ad hoc networks known as "vehicular ad hoc networks" (or "VANETs"), this type of network allows cars to communicate and share data with one another in a manner that is decentralized. The vulnerability of the VANET stems from the fact that communication between vehicles takes place in an open environment. Authentication techniques are an essential component of VANETs since they enable secure communication between vehicles. The implementation of cluster-based authentication protocols is a typical strategy that is utilized for the protection of VANETs. As part of our research, we deliver a trustworthy authentication system that is built on clusters and is intended for usage in VANETs. Our strategy makes use of public-key cryptography to protect against forgeries. This is accomplished by placing the onus of verifying the authenticity of cars on the cluster head. We show that our protocol is resistant to a wide range of attacks and that it can permit authentication in VANETs that is both quick and reliable.
A Machine Learning-based Approach for Network Traffic Analysis and Management
Authors: Chauhan Rahul
Published: May 2023

For a network to function properly and remain secure, network traffic management and analysis are essential. In this field, machine learning-based techniques have demonstrated considerable potential by offering precise and effective network traffic analysis and anomaly detection. In this research, we offer a machine learning-based methodology for network traffic monitoring and management. This method analyses network data and identifies network anomalies using a variety of machine learning methods. Using the NSL-KDD dataset and other machine learning methods, such as decision trees, SVM, neural networks, and random forests, we assess the effectiveness of our strategy. The outcomes of our tests show how successful our suggested strategy is, with high accuracy rates and low false positive rates. In numerous network management and security applications, our suggested approach beats cutting-edge machine learning-based algorithms for network traffic analysis and management. The suggested strategy offers a positive perspective for improving network administration and security through machine learning.
Secure Data Sharing in Cloud Computing Systems: Techniques and Applications
Authors: Aeri Manisha
Published: May 2023

Secure data sharing in cloud computing systems is critical to protecting data from unauthorized access or manipulation. Conventional data security approaches are insufficient to assure total data protection in the cloud, and new strategies are necessary. Access control, data encryption, tokenization, digital signatures, data masking, data backup and recovery, virtual private networks, firewalls, intrusion detection systems, and cloud access security brokers are among the techniques investigated in this paper for secure data sharing in cloud computing systems. Furthermore, the study investigates safe data sharing applications in a variety of industries, including healthcare, banking, education, government, retail, manufacturing, legal, and non-profit. These examples show how secure data sharing can improve cooperation, efficiency, and trust in a variety of industries. Secure data sharing in cloud computing platforms is critical, and firms must employ adequate security measures to protect their data. Organizations may ensure that their data is secure, available only to authorized individuals, and used to promote cooperation, efficiency, and trust by implementing the methodologies and applications discussed in this paper.
Blockchain-based IoT Systems: Techniques, Applications, and Challenges
Authors: Chamoli Sushant
Published: May 2023

The potential for blockchain-based IoT systems to enable secure and effective data sharing in a variety of industries has attracted a lot of attention in recent years. The fusion of blockchain technology and IoT devices can open up new avenues for growth and innovation, but it also comes with a number of serious drawbacks, including issues with scalability, security, interoperability, data privacy, cost, energy use, and governance. This study offers a thorough analysis of the various methods for putting into practise Blockchain-based IoT systems, along with their benefits and drawbacks. In order to address the issues with Blockchain-based IoT systems, we also suggest a hybrid technique that combines the benefits of several techniques. We also cover the important issues that need to be solved as well as the potential uses of Blockchain-based IoT devices. In order to help researchers and practitioners better grasp the current state of the art and the possibilities for further study and development, this paper seeks to provide a thorough review of Blockchain-based IoT systems.
A Privacy-Preserving Medical Data Sharing Framework: Techniques, Applications, and Challenges
Authors: Manchanda Mahesh
Published: May 2023

To improve patient outcomes and advance medical research, it is crucial that healthcare data are shared securely and effectively. It is difficult to communicate data while retaining confidentiality, though, due to the delicate nature of healthcare data and worries about patient privacy. Sensitive patient data must be safeguarded while facilitating secure data sharing, which calls for a framework that protects privacy. For implementing such a system, various methods like differential privacy, secure multi-party computation, and homomorphic encryption have been suggested. It is necessary to handle issues including interoperability problems, legal and ethical dilemmas, and technical difficulties. In order to create a framework for sharing medical data while protecting privacy, this study suggests a method that combines homomorphic encryption and blockchain technology. The suggested method offers a safe and effective means of exchanging and storing encrypted healthcare data while preserving data integrity and privacy. A multidisciplinary strategy combining cooperation between healthcare providers, data scientists, privacy experts, and regulatory agencies is necessary for the development and implementation of a medical data sharing framework that protects patient privacy. A privacy-preserving framework for medical data sharing can promote effective and secure data exchange for better healthcare outcomes by addressing the issues and utilizing the tools at hand.
An Approach to Diagnosis of Thyroid using Data Mining Techniques
Authors: Chaudhary Preeti
Published: May 2023

The occurrence of thyroid as disease tends to affect the physical health of people across the world. The disease is expected to be present in the endocrine glands of a human body and appears to be in the anterior position with respect to the neck. An imbalanced production of hormones from such glands results into a dysfunction of thyroid glands due to insufficient generation of such hormones. Hence, the glands begin to swell and might lead to malignant tumours. One of the available treatments to for such a disease is being done using sodium levothyroxine; commonly referred to as LT4. It is known to be hormone for treating thyroid and its respective disorders. In addition to the treatments being made; it is necessary to predict and identify the occurrence of the disease in a human body so that it can be cured at the right time in the early stages. For this to occur; endocrinologists must detect the imbalance being created with respect to the generation of thyroid hormones. Hence, the proposed research work presents the detection and identification of the same by utilizing the concepts of data mining techniques. A database consisting of 857 patients is acquired from a repository and pre-processing stages are applied to it to refine and filter the collected data. Four data mining algorithms are further used to evaluate the accuracy thus produced and the algorithm with highest generating accuracy is thus declared as the optimised model. For the implementation of the proposed research paper; the execution of random forests generated highest accuracy of 81.26 percent.
Results of a pilot study of presenteeism using the Stanford Presenteeism Scale among the working population in the Russian Federation
Authors: Antsiferova Aleksandra A. , Kontsevaya Anna V. , Drapkina Oxana M.
Published: May 2023

BACKGROUND: The health status of the population and the economic development of the country are interrelated. The economic costs of compensatory payments associated with an increase in the incidence of diseases among company employees are constantly growing. The indirect costs of employers related to the health of employees are made up of absenteeism and presenteeism of employees. Determining the prevalence of frequency and level of presenteeism requires a special approach.
AIM: To assess the level of presenteeism among employees in the Russian Federation using the Stanford Presenteeism Scale.
MATERIALS AND METHODS: In 2020, experts from the National Medical Research Center for Therapy and Preventive Medicine of the Ministry of Health of Russian Federation developed a set of tools (a questionnaire for employers and a questionnaire for employees). The 5-block employee questionnaire includes a translated and adapted version of the Stanford Presenteeism Scale. This unit assesses the degree of decline in productivity due to health conditions and, accordingly, when planning health promotion programs, calculate the opportunities to reduce presenteeism and increase productivity.
RESULTS: Pilot testing of the developed package of tools took place in the spring of 2020 in 28 regions of Russia. The study included workers from the Yamalo-Nenets Autonomous Okrug (44.9%, n=248), the Republic of Karelia (18.8%, n=104), the Udmurt Republic (17.5%, n=97), the Sakhalin Region (13.7%, n=76) and Altai Territory (5.1%, n=28). As a result, 553 completed questionnaires were returned. Among the interviewed employees, 33.5% were men (n=187) with the mean age of 39.810.7 years. Women (n=366, 66.5%) had the average age of 42.611.1 years. Most employees had an average level of presenteeism (n=271, 57.3%), while low and high levels were observed in 41.2% (n=196) and 1.5% (n=6), respectively. The average level of presenteeism was observed in 96 men (59.9%) and 175 women (55.9%). Presenteeism was the most prevalent among employees aged 40 to 49 (33.2%, n=157), and the least prevalent among employees aged 60 years and older (5.7%, n=27). The lowest level of presenteeism was observed among technical specialists (6.1%, n=29), as well as among managers (10.1%, n=48).
CONCLUSION: The results of this pilot study revealed high prevalence of presenteeism among workers warranting further research in other regions. Studies aimed at reducing the level of presenteeism are also needed.
Numerical Modeling of Hydrodynamic Performance on Porous Slope Type Floating Breakwater
Authors:   Institut Teknologi Sepuluh Nopember (ITS), Department of Ocean Engineering Surabaya, Indonesia, Sujantoko Sujantoko , Djatmiko Eko Budi , Wardhana Wisnu , Gemilang Firmasyah Is Naldi
Published: May 2023

A breakwater is a coastal building that aims to break up or withstand wave energy that enters the beach so that the characteristics of the incoming waves are by calculations and can reduce abrasion on the shoreline. Designing a floating breakwater is very complicated because it depends on many aspects. These fundamental aspects depend on each other, so if one of these aspects changes, the integrity of the floating breakwater structure will also change. One of these aspects is the magnitude of the transmission and reflection coefficients generated by the floating breakwater. This research will study the hydrodynamic performance of floating breakwater due to variations in slope and porosity in reducing and reflecting waves with computational fluid dynamics (CFD). The slope-porous floating breakwater dimension is based on previous experimental data, including a constant water depth of 0.75 m, a wave height of 0.05 - 0.125 m, and a wave period of 1.1 - 2 sec on regular waves. The results of the numerical model validation and experiments on all variations of the floating breakwater model are quite good, which is less than 10% for both wave transmission and reflection. Analysis of the influence of changes in the mooring line angle, the simulation is carried out at an angle of 30 deg to 90 deg and produces an average transmission coefficient of 0.79 and a reflection of 0.21. While the effect of changes in water level elevation (0.85 m, 0.75 m, and 0.65 m) gives a reasonably significant average transmission coefficient of 0.85 and a reflection of 0.13. The mooring line angle will be gentler at high tide, and the transmission and reflection coefficients will be higher. However, the mooring line will loosen at low tide, causing the structure to move more freely and eliminating the function of the floating breakwater itself so that the tidal phenomenon becomes a challenge for coastal experts in designing structures to produce effective and efficient hydrodynamic performance.
Evaluation of serum adipocytokine and interleukin-18 levels in patients with epilepsy
Authors: DÜNDAR Ahmet , KILINÇ Derya , YILMAZ Ahmet , JAFAR Vugar , AYAN Orhan , ÇEVİK Mehmet Uğur , ASLANHAN Hamza
Published: May 2023

Objective: Epilepsy is a neurological disease characterized by recurrent seizures. The underlying pathophysiological mechanisms in epilepsy are not fully known. Our aim is to investigate the relationship between serum adipocytokine and interleukin (IL)-18 levels in epilepsy patients receiving and not receiving antiepileptic therapy.

Method: Our study was established as three groups. I: Epilepsy patients receiving antiepileptic therapy (n=30), II: Newly diagnosed epilepsy patients (n=30) and III: Control group (n=30). Serum adipocytokine and IL-18 levels were measured by enzyme-linked immunoassorbent assay method.

Results: It was determined that serum adipocytokine and IL-18 levels were increased in epilepsy patients who received topiramate treatment and did not receive antiepileptic therapy compared to the control group. Serum glucose, total protein, cholesterol and albumin concentrations of patients who received antiepileptic treatment were decreased compared to the control group (p0.05). It was found that the body mass index (BMI) ratio of epilepsy patients who received antiepileptic treatment decreased and was significant compared to the control group and the group that did not receive treatment (p
Prognosis and risk factors of chronicity in childhood idiopathic thrombocytopenic purpura: a single-center experience
Authors: ÇAKMAK Hatice Mine , KOCABAY Kenan
Published: May 2023

Objective: In previous studies, chronicity risk factors for idiopathic thrombocytopenic purpura (ITP) are unclear. This study aimed to evaluate the outcome of children with ITP and determine the chronicity risk factors.

Methods: This study retrospectively examined the demographics, laboratories, outcome, and chronicity risk factors among sixty children with ITP and obtained the data from the computer system. We analyzed demographics, treatment, and laboratory risk factors for chronic ITP by IBM SPSS and used binary logistic regression analysis.

Results: Of 60 children with ITP, 32 (53.3%) had acute, 25 (41.7%) had chronic, and 3 (5%) had persistent ITP. Demographics, laboratories (age
High Performance of Human Resource Practice Towards Work Engagement: A Practice at a Government Public Office
Authors: Prasetyo Agung , Palupi Majang
Published: May 2023

This study aims to examine and analyze the effect of high-performance human resource practice on work engagement in the Secretary Office of the Regional House of the Representatives City of Dumai by using indicators of high-performance human resource practices, which consist of extensive training, employment security, participation and communication, selection and recruitment, and reward and compensation as independent variables. This quantitative research had a total sampling technique of 100 respondents. Data was collected by distributing questionnaires which were then processed using the IBM SPSS 26 application. The results indicated that the variables of extensive training, selection and recruitment, and reward and compensation had a positive and significant impact on work engagement. At the same time, the variable employment security had a positive but insignificant impact on work engagement. The variable of participation and communication had a negative but insignificant impact on work engagement.
Pengaruh Motivasi Terhadap Kinerja Petugas Penanganan Prasarana dan Sarana Umum di Kelurahan Lebak Bulus
Authors: Valentina Adinda , Rahman Abdul , Mawar Mawar , Amalia Tasya
Published: May 2023

Tujuan penelitian ini adalah untuk mengetahui dan menganalisis bagaimana pengaruh motivasi terhadap kinerja pegawai Penanganan Sarana dan Prasarana Umum (PPSU) di Kelurahan Lebak Bulus Jakarta Selatan. Metode penelitian yang digunakan adalah metode penelitian asosiatif dengan pendekatan kuantitatif. Teknik pengambilan sampel yang digunakan adalah non-probability sampling, dengan teknik sampling jenuh, menggunakan seluruh populasi sebagai sampel, karena populasinya relatif kecil yaitu berjumlah 89 responden. Metode analisis data yang digunakan adalah uji validitas, uji reliabilitas, uji normalitas, analisis regresi linier sederhana, analisis koefisien determinasi, dan uji hipotesis (uji-t). Berdasarkan hasil analisis bahwa terdapat pengaruh yang positif dan signifikan antara motivasi terhadap kinerja dengan nilai R square sebesar 0,559 atau 55,9% kontribusi motivasi terhadap kinerja berada pada kategori kuat, sedangkan 44,1% dipengaruhi oleh faktor lain tidak. diamati dalam penelitian ini. Hasil persamaan regresi linier sederhana adalah Y = 15,357 + 0,759X, artinya peningkatan motivasi sebesar 1% akan meningkatkan kinerja sebesar 0,759. Kemudian hasil uji hipotesis nilai signifikansi 0,00 0,05 disimpulkan bahwa terdapat pengaruh positif dan signifikan pengaruh motivasi terhadap kinerja pegawai PPSU di Kelurahan Lebak Bulus.
The Effectiveness of Social Services in Handling Beggars as Social Welfare Service Needers in South Tangerang City
Authors: Rahman Abdul , Lubis Hanifa Arwanti , Utari Syifa Astasia
Published: May 2023

This study aims to analyze the effectiveness of Social Services in handling beggars as Social Welfare Service Needers (PPKS) in South Tangerang City since, until now, they still encounter many obstacles. First, handling PPKS has not become one of the government’s main agendas. Second, the problems are the rampant existence of beggars who disturb public order and cause public unrest. The third, based on a study by the Integrated Service Center Team for the Empowerment of Women and Children (P2TP2A) of South Tangerang City, is not optimal for handling beggars/PPKS in South Tangerang City. This study used a descriptive method with a qualitative approach. Meanwhile, the organizational effectiveness theory of Steers et al., which consists of 4 indicators of organizational effectiveness, such as organizational characteristics, environmental characteristics, worker characteristics, and management practices, became the main analytical knife of the study. This study concluded that the Social Services have not effectively handled beggars as Social Welfare Service Needers in South Tangerang City. Several aspects need to be improved, such as more intense training for employees of the South Tangerang City Social Service, additional human resources who have a background in social science studies, providing public complaint channels, conducting strict action against beggars who have been caught twice, gaining effectiveness counseling to rehabilitated beggars, developing social institutions, and formulating regional regulations governing sanctions for beggars.
Outcomes of patients treated with minimally invasive plate osteosynthesis in the management of distal tibial fractures
Authors: Randhawa Prateek Singh , Joshi Gururaj , Mankar Pratik Ajay
Published: May 2023

Background: Distal tibial fractures are a common injury, often caused by high-energy trauma, and can result in significant morbidity. Traditional surgical management with open reduction internal fixation (ORIF) is associated with complications including infection and soft tissue injury. Minimally invasive plate osteosynthesis (MIPO) has emerged as a potential alternative, with advantages including less soft tissue injury and a faster return to function.  The study aimed to evaluate the outcomes of MIPO for distal tibial fractures.
Method: Sample size of 38 patients between 18-80 years of age with distal tibial fractures treated with MIPO was included. Patient demographics, fracture characteristics, surgical details and postop outcomes collected.
Results: A total of 38 patients with distal tibia fractures with a mean age of 44.36 years were included in the study. The mean duration of hospital stay was 12.71 days, and the time required for a union was 18.61 weeks. The AOFAS score improved significantly from 3 to 6 months after surgery. However, 15 patients (34.88%) reported complications, with deep infection and skin necrosis being the most common. Out of 43 patients, 11 underwent culture and sensitivity testing, with no growth in 5 cases, and the remaining cases showed various bacterial infections. Seven patients underwent implant removal due to complications.
Conclusions: The study provides valuable information about the use of MIPO in treating distal tibia fractures, as well as the risk of complications associated with this approach.